Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

In this paper,the establishment of efficientWireless Sensor Network(WSN)networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach(SMOWA)algorithm for solving a multi-objective problem.The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficientWireless Sensor Network to minimize energy consumption.After that,the cluster head for each cluster has been selected with the help of the duty cycle.After configuring the WSN networks,the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks.Energy minimization,as well as the amount of day-saving,has been calculated for the differentWSNswhich has been configured through different deployment policies.The major finding of the research paper is to improve the durability of Wireless Sensor Network(i)applying different deployment strategies:(Random,S pattern and nautilus shell pattern),and(ii)using a new Meta-heuristic algorithm(SMOWA Algorithm).In this research,the lifetime of WSN has been increased to a significant level.To choose the best result set from all the obtained results set some constraints such as“equivalent distribution”,“number of repetitions”,“maximum amount energy storage by a node”has been set to an allowable range.

Multi-objective robust secure beamforming for cognitive satellite and UAV networks

A multi-objective optimization based robust beamforming(BF)scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle(UAV)network.Since the satellite network coexists with the UAV network,we first consider both achievable secrecy rate maximization and total transmit power minimization,and formulate a multi-objective optimization problem(MOOP)using the weighted Tchebycheff approach.Then,by supposing that only imperfect channel state information based on the angular information is available,we propose a method combining angular discretization with Taylor approximation to transform the non-convex objective function and constraints to the convex ones.Next,we adopt semi-definite programming together with randomization technology to solve the original MOOP and obtain the BF weight vector.Finally,simulation results illustrate that the Pareto optimal trade-off can be achieved,and the superiority of our proposed scheme is confirmed by comparing with the existing BF schemes.

Deciphering Ascorbic Acid Regulatory Pathways in Ripening Tomato Fruit Using a Weighted Gene Correlation Network Analysis Approach

Genotype is generally determined by the co-expression of diverse genes and multiple regulatory pathways in plants. Gene co-expression analysis combining with physiological trait data provides very important information about the gene function and regulatory mechanism. L-Ascorbic acid （AsA）, which is an essential nutrient component for human health and plant metabolism, plays key roles in diverse biological processes such as cell cycle, cell expansion, stress resistance, hormone synthesis, and signaling. Here, we applied a weighted gene correlation network analysis approach based on gene expression values and AsA content data in ripening tomato （Solanum lycopersicum L.） fruit with different AsA content levels, which leads to identification of AsA relevant modules and vital genes in AsA regulatory pathways. Twenty- four modules were compartmentalized according to gene expression profiling. Among these modules, one negatively related module containing genes involved in redox processes and one positively related module enriched with genes involved in AsA biosynthetic and recycling pathways were further analyzed. The present work herein indicates that redox pathways as well as hormone-signal pathways are closely correlated with AsA accumulation in ripening tomato fruit, and allowed us to prioritize candidate genes for follow-up studies to dissect this interplay at the biochemical and molecular level.

Distributed Filtering Algorithm Based on Tunable Weights Under Untrustworthy Dynamics

Aiming at effective fusion of a system state estimate of sensor network under attack in an untrustworthy environment, distributed filtering algorithm based on tunable weights is proposed. Considering node location and node influence over the network topology, a distributed filtering algorithm is developed to evaluate the certainty degree firstly. Using the weight reallocation approach, the weights of the attacked nodes are assigned to other intact nodes to update the certainty degree, and then the weight composed by the certainty degree is used to optimize the consensus protocol to update the node estimates. The proposed algorithm not only improves accuracy of the distributed filtering,but also enhances consistency of the node estimates. Simulation results demonstrate the effectiveness of the proposed algorithm.

A Goal-Oriented Adaptive Finite Element Method for 3D Resistivity Modeling Using Dual-Error Weighting Approach

A goal-oriented adaptive finite element（FE） method for solving 3D direct current（DC） resistivity modeling problem is presented. The model domain is subdivided into unstructured tetrahedral elements that allow for efficient local mesh refinement and flexible description of complex models. The elements that affect the solution at each receiver location are adaptively refined according to a goal-oriented posteriori error estimator using dual-error weighting approach. The FE method with adapting mesh can easily handle such structures at almost any level of complexity. The method is demonstrated on two synthetic resistivity models with analytical solutions and available results from integral equation method, so the errors can be quantified. The applicability of the numerical method is illustrated on a resistivity model with a topographic ridge. Numerical examples show that this method is flexible and accurate for geometrically complex situations.

Application of an interpretable artificial neural network to predict the interface strength of a near-surface mounted fiber-reinforced polymer to concrete joint

Accurately estimating the interfacial bond capacity of the near-surface mounted(NSM)carbon fiber-reinforced polymer(CFRP)to concrete joint is a fundamental task in the strengthening and retrofit of existing reinforced concrete(RC)structures.The machine learning(ML)approach may provide an alternative to the commonly used semi-empirical or semi-analytical methods.Therefore,in this work we have developed a predictive model based on an artificial neural network(ANN)approach,i.e.using a back propagation neural network(BPNN),to map the complex data pattern obtained from an NSM CFRP to concrete joint.It involves a set of nine material and geometric input parameters and one output value.Moreover,by employing the neural interpretation diagram(NID)technique,the BPNN model becomes interpretable,as the influence of each input variable on the model can be tracked and quantified based on the connection weights of the neural network.An extensive database including 163 pull-out testing samples,collected from the authors’research group and from published results in the literature,is used to train and verify the ANN.Our results show that the prediction given by the BPNN model agrees well with the experimental data and yields a coefficient of determination of 0.957 on the whole database.After removing one non-significant feature,the BPNN becomes even more computationally efficient and accurate.In addition,compared with the existed semi-analytical model,the ANN-based approach demonstrates a more accurate estimation.Therefore,the proposed ML method may be a promising alternative for predicting the bond strength of NSM CFRP to concrete joint for structural engineers.