Fuzzy Geometric Programming in Multivariate Stratified Sample Surveys in Presence of Non-Response with Quadratic Cost Function

In this paper, the problem of non-response with significant travel costs in multivariate stratified sample surveys has been formulated of as a Multi-Objective Geometric Programming Problem (MOGPP). The fuzzy programming approach has been described for solving the formulated MOGPP. The formulated MOGPP has been solved with the help of LINGO Software and the dual solution is obtained. The optimum allocations of sample sizes of respondents and non respondents are obtained with the help of dual solutions and primal-dual relationship theorem. A numerical example is given to illustrate the procedure.

Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface(IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage.However,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead.Besides the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert communications.On this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)cooperatively.Thanks to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to others.Numeral results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

Structure of the 40S ribosomal subunit of Plasmodium falciparum by homology and de novo modeling

Generation of three dimensional structures of macromolecules using in silico structural modeling technologies such as homology and de novo modeling has improved dramatically and increased the speed by which tertiary structures of organisms can be generated. This is especially the case if a homologous crystal structure is already available. High-resolution structures can be rapidly created using only their sequence information as input, a process that has the potential to increase the speed of scientific discovery. In this study, homology modeling and structure prediction tools such as RNA123 and SWISS–MODEL were used to generate the 40 S ribosomal subunit from Plasmodium falciparum. This structure was modeled using the published crystal structure from Tetrahymena thermophila, a homologous eukaryote. In the absence of the Plasmodium falciparum 40 S ribosomal crystal structure, the model accurately depicts a global topology, secondary and tertiary connections, and gives an overall root mean square deviation(RMSD) value of 3.9 ? relative to the template's crystal structure. Deviations are somewhat larger in areas with no homology between the templates. These results demonstrate that this approach has the power to identify motifs of interest in RNA and identify potential drug targets for macromolecules whose crystal structures are unknown. The results also show the utility of RNA homology modeling software for structure determination and lay the groundwork for applying thisapproach to larger and more complex eukaryotic ribosomes and other RNA-protein complexes. Structures generated from this study can be used in in silico screening experiments and lead to the determination of structures for targets/hit complexes.

Intelligent geospatial maritime risk analytics using the Discrete Global Grid System

Each year,accidents involving ships result in significant loss of life,environmental pollution and economic losses.The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence.Yet,such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures.This paper proposes the use of the Discrete Global Grid System(DGGS)as an efficient and advantageous structure to integrate vessel traffic,metocean,bathymetric,infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings.Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach.A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002.The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures,targeted to regions with the highest risk.Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.

Global Reference Grids for Big Earth Data

The emerging field of Discrete Global Grid Systems(DGGS)provides a way to organise,store and analyse spatio-temporal data at multiple resolutions and scales(from near global scales down to microns).DGGS partition the entire planet into a discrete hierarchy of global tessellations of progressively finer resolution zones(or cells).Data integration,decomposition and aggregation are optimised by assigning a unique spatio-temporal identifier to each zone.These identifiers are encodings of both the zone’s location and its resolution.As a result,complex multi-dimensional,multi-resolution spatio-temporal operations are simplified into sets of 1D array and filter operations.