Supermatrix Research Articles

Multi-scale hybrid reinforced super duplex stainless steel matrix composites with high strength and ductility via laser powder bed fusion and an in-situ synthesis strategy

The emergence of nanoparticle-reinforced metal matrix composites represents a promising method for efficiently enhancing material strength without significant compromise in ductility. Nevertheless, the homogeneous distribution of nanoparticles in matrix remains a technical challenge. To address this issue, this study designed an advanced method to create a multi-scale hybrid to reinforce super duplex stainless steels (SDSSs) by coupling laser powder bed fusion (LPBF) technique with an in-situ synthesis strategy and optimizing post heat treatment. For as-built composites, both TiCxNy and M23C6 nanoparticles were generated in-situ by the introduction of micron-sized TiC particles into SDSSs, and their microstructure was mainly composed of fine ferrite grains. The post quenching treatment enabled heat-treated composites to possess micro-duplex matrix grains with a multi-scale hybrid, including micron-sized TiC, in-situ sub-micron M23C6 particles, and in-situ TiCxNy nanoparticles. Particularly, the obvious agglomeration phenomenon of in-situ TiCxNy nanoparticles was not observed. In comparison with heat-treated SDSSs (ultimate tensile strength (UTS): ∼839 MPa; uniform elongation (UE): ∼20.6 %), heat-treated composites processed by 1090 °C obtained a higher UTS (∼992 MPa) with only 0.8 % reduction in UE. The great ductility of heat-treated composites was primarily attributed to their relatively high strain hardening rate, facilitated by grain refinement and the formed multi-scale hybrid. The successful creation of a multi-scale hybrid sets the stage for producing high-performance metallic components via LPBF and in-situ strategy.

Surface hardening of Ti-Al-V superalloy spinal implant by using the boronization method.

We compared the raw Ti-Al-V super alloy transpedicular implant screws with boronized and surface-hardened transpedicular implant screws. To improve patients' postoperative prognosis with the production of harder and less fragile screws. Surface hardening was achieved by applying green-body encapsulation of the specimen with elemental boron paste which is sintered at elevated temperatures to ensure the boron-metal diffusion. Boron transported into the Ti-Al-V super alloy matrix gradually while suppressing aluminum and a homogeneously boronized surface with a thickness of ∼15 microns was obtained. The uniform external shell was enriched with TiB2, which is one of the hardest ceramics. The Ti-Al-V core material, where boron penetration diminishes, shows cohesive transition and ensures intact core-surface structure. Scanning electron microscope images confirmed a complete homogeneous, uniform and non-laminating surface formation. Energy-dispersive X-ray monitored the elemental structural mapping and proved the replacement of the aluminum sites on the surface with boron ending up the TiB2. The procedure was 8.6 fold improved the hardness and the mechanical resistance of the tools. Surface-hardened, boronized pedicular screws can positively affect the prognosis. In vivo studies are needed to prove the safety of use.

Design and fabrication of a super strong aluminum matrix composite with continuous titanium-based reinforcements

Super strong lightweight aluminum matrix composites (AMCs) were fabricated using selected laser melting (SLM) and micro-casting technology. The present AMCs show very high strength while maintaining a low density (e.g. σy = 1147 MPa, ρ = 3.7 g/cm3). This break the trade-off dilemma of strength and density in traditional AMCs. The nano/micro/macro-scale hybrid reinforcement involved in the present AMCs is proved an effective strategy for enhancing strength of AMCs. An FEM model was built to analyze the mechanical behavior of AMCs during compression. The simulation results are essentially consistent with the experimental results. Computation-aided design shows promise in reducing the cost and duration of continuous aluminum matrix composites development.

Ranking Competencies of Managers with the Combined Method of Dimtel and Network Analysis (Case study: Public Hospitals of Yazd Province)

Background: The success of any organization depends on the expertise and competence of human resources, especially its managers. Competence is defined as the knowledge and skills necessary to perform a job. The current study aimed to rank the competencies of managers based on the competence model of Dolewis and Higgs (2005), which was carried out using multi-criteria decision-making techniques (combined method of Dimtel and network analysis) in public hospitals of Yazd province.
 Methods: This inductive approach research was carried out quantitatively during 2021 to 2022 in public hospitals of Yazd province. In this study, multi-criteria decision-making techniques were used including the combined method of Dimtel and network analysis. The statistical population was a group of human resources experts and senior managers of public hospitals in Yazd province. Sampling was done first by judgment and then by snowball method and through the introduction of experts and based on the possibility of access to them, and 15 experts were selected as a sample. After extracting the data from the experts' questionnaires, Dimtel method was used to evaluate the relationships between the indicators.
 Results: In the current study, according to the experts’ opinions, the competence model of Doleviz and Higgs (2005) was considered, which included three managerial, personal, and intellectual dimensions. The dimension of managerial competence included components of success, empowerment, development, engaging and effective communication, resource management. The dimension of intellectual competence included components of judgment and critical analysis, perspective and foresight of strategic views. The dimension of personal competence included components of self-awareness and emotional resilience, intuitive understanding, interpersonal sensitivity, influence, motivation, and conscience. Based on the results of the super matrix of network analysis, the weights of competence in order of priority included managerial, intellectual, and personal competence. In managerial competence, the component of resource management, in personal competence, the influence component, and in intellectual competence, the components of foresight and perspective were more important.
 Conclusion: In order to be more successful in achieving future goals and perspective, hospital managers should strive to strengthen their competencies, since improving the competence of managers helps them in effective use of resources, better response to clients, and achieving goals, in accordance with the accreditation standards of public hospitals in Yazd province

EasyCGTree: a pipeline for prokaryotic phylogenomic analysis based on core gene sets

BackgroundGenome-scale phylogenetic analysis based on core gene sets is routinely used in microbiological research. However, the techniques are still not approachable for individuals with little bioinformatics experience. Here, we present EasyCGTree, a user-friendly and cross-platform pipeline to reconstruct genome-scale maximum-likehood (ML) phylogenetic tree using supermatrix (SM) and supertree (ST) approaches.ResultsEasyCGTree was implemented in Perl programming languages and was built using a collection of published reputable programs. All the programs were precompiled as standalone executable files and contained in the EasyCGTree package. It can run after installing Perl language environment. Several profile hidden Markov models (HMMs) of core gene sets were prepared in advance to construct a profile HMM database (PHD) that was enclosed in the package and available for homolog searching. Customized gene sets can also be used to build profile HMM and added to the PHD via EasyCGTree. Taking 43 genomes of the genus Paracoccus as the testing data set, consensus (a variant of the typical SM), SM, and ST trees were inferred via EasyCGTree successfully, and the SM trees were compared with those inferred via the pipelines UBCG and bcgTree, using the metrics of cophenetic correlation coefficients (CCC) and Robinson–Foulds distance (topological distance). The results suggested that EasyCGTree can infer SM trees with nearly identical topology (distance < 0.1) and accuracy (CCC > 0.99) to those of trees inferred with the two pipelines.ConclusionsEasyCGTree is an all-in-one automatic pipeline from input data to phylogenomic tree with guaranteed accuracy, and is much easier to install and use than the reference pipelines. In addition, ST is implemented in EasyCGTree conveniently and can be used to explore prokaryotic evolutionary signals from a different perspective. The EasyCGTree version 4 is freely available for Linux and Windows users at Github (https://github.com/zdf1987/EasyCGTree4).

Study of Intuitionistic Fuzzy Super Matrices and Its Application in Decision Making

Study of Intuitionistic Fuzzy Super Matrices and Its Application in Decision Making

Chromatin alternates between A and B compartments at kilobase scale for subgenic organization

Nuclear compartments are prominent features of 3D chromatin organization, but sequencing depth limitations have impeded investigation at ultra fine-scale. CTCF loops are generally studied at a finer scale, but the impact of looping on proximal interactions remains enigmatic. Here, we critically examine nuclear compartments and CTCF loop-proximal interactions using a combination of in situ Hi-C at unparalleled depth, algorithm development, and biophysical modeling. Producing a large Hi-C map with 33 billion contacts in conjunction with an algorithm for performing principal component analysis on sparse, super massive matrices (POSSUMM), we resolve compartments to 500 bp. Our results demonstrate that essentially all active promoters and distal enhancers localize in the A compartment, even when flanking sequences do not. Furthermore, we find that the TSS and TTS of paused genes are often segregated into separate compartments. We then identify diffuse interactions that radiate from CTCF loop anchors, which correlate with strong enhancer-promoter interactions and proximal transcription. We also find that these diffuse interactions depend on CTCF’s RNA binding domains. In this work, we demonstrate features of fine-scale chromatin organization consistent with a revised model in which compartments are more precise than commonly thought while CTCF loops are more protracted.

Identify Important Cities in the Belt and Road Comprehensive Traffic Network

The Belt and Road has developed rapidly in recent years. Constructing a comprehensive traffic network is conducive to promoting the development of the the Belt and Road. To optimize the layout of the Belt and Road comprehensive traffic network, this paper identifies important cities. First, a weighted super adjacency matrix is defined, which includes sea, air, railway transportation and trans-shipment transportation between these transportation modes. With this matrix, the Belt and Road comprehensive traffic network (B&amp;RCTN) is constructed. To identify important node cities, this paper proposes a method to calculate multi-layer centrality which considers inter-layer relationships. With the results of the above four centrality indexes, the Entropy Weight TOPSIS is used to synthesize the evaluation of the four indexes. Finally, the multi-layer comprehensive centrality rank of node cities is obtained. Result shows that there are 72 important cities in B&amp;RCTN. These important cities are mainly distributed in the east and west of Eurasia. Eastern cities are located in East Asia and Southeast Asia, including 36 cities such as Singapore, Shanghai, Guangzhou, Shenzhen and Hong Kong. Western cities are concentrated in West Asia, Western Europe and North Africa along the Mediterranean coast, including 31 cities such as Istanbul, Dubai, Vienna, Trieste and Koper. There are few important cities in central Eurasia, except Almaty in Central Asia and Colombo in South Asia. In addition, important cities also include Moscow in Eastern Europe, Lagos and Lome in West Africa. Finally, based on the distribution of important cities, this paper puts forward some suggestions on the development of the Belt and Road comprehensive transportation.

Node Importance Identification for Temporal Networks Based on Optimized Supra-Adjacency Matrix.

The research on node importance identification for temporal networks has attracted much attention. In this work, combined with the multi-layer coupled network analysis method, an optimized supra-adjacency matrix (OSAM) modeling method was proposed. In the process of constructing an optimized super adjacency matrix, the intra-layer relationship matrixes were improved by introducing the edge weight. The inter-layer relationship matrixes were formed by improved similarly and the inter-layer relationship is directional by using the characteristics of directed graphs. The model established by the OSAM method accurately expresses the structure of the temporal network and considers the influence of intra- and inter-layer relationships on the importance of nodes. In addition, an index was calculated by the average of the sum of the eigenvector centrality indices for a node in each layer and the node importance sorted list was obtained from this index to express the global importance of nodes in temporal networks. The experimental results on three real temporal network datasets Enron, Emaildept3, and Workspace showed that compared with the SAM and the SSAM methods, the OSAM method has a faster message propagation rate and larger message coverage and better SIR and NDCG@10 indicators.

Enhanced by mobility? Effect of users’ mobility on information diffusion in coupled online social networks

Online social networks have gradually become crucial tools for users to exchange information. Two characteristics of online social networks should be underlined: first, individuals can freely leave or stay; second, information diffusion is more common on coupled online social platforms. However, very few research combines the above two aspects when studying the information diffusion in online social networks. Therefore, we establish an in-out-unacquired2-acquired 2-rejected2 (IO-S2P2R2) information diffusion model which investigates the time evolution of three types of users considering users’ mobility in coupled online social networks using mean-field theory. We analyze the theoretical threshold of information diffusion via linear analysis and find that the information diffusion threshold is related to the maximum eigenvalue of the super adjacency matrix. Moreover, by comparing the mean-field method with the Monte Carlo method, the consistency of the simulation results is obtained, which verifies the scientificity of our model. The results demonstrate the consistency of linear analysis and simulation experiments. Besides, based on the existing data sets, information diffusion rates and users’ mobility rates are positively correlated with the range of information diffusion, while information decline rates are negatively correlated with the range of information diffusion through the sensitivity analysis of parameters.

Research on the Multilayer Network of Relations of Western Agricultural Trade along the Belt and Road

This paper defines the weighted super adjacency matrix based on the existing super adjacency matrix. This paper, for the first time, combines the trade network, competitive network, and complementary network to construct the trade multilayer network, and innovatively defines the interlayer connections. Based on this, we build trade multilayer networks for three major agricultural products in the western region along the “Belt and Road”. The paper then proposes analytical methods, including a classification algorithm for local network relations and a comparative analysis of trade development priorities based on the local network relations. The former is used to identify and categorize key trading countries and potential trading countries for western agricultural products along the “Belt and Road”. The latter is used to examine the western regions’ order of priority in developing trade relations with the classified countries. According to the findings, category I agricultural trade has 37 key trading countries among the “Belt and Road” countries. Their local network relationships are classified into six groups, and their trade development priorities are classified into four levels. There are 49 key trading countries in category II agricultural trade. Their local network relations can be classified into three groups, and their trade development priorities can be classified into two levels. There are 62 key trading countries in category IV agricultural trade. Their local network relationships are classified into six groups, and their trade development priorities are classified into four levels. Furthermore, only Chile is a potential trading partner in category I agricultural trade. Finally, this paper offers policy recommendations for the development of agricultural trade along the “Belt and Road” in the western region.

Phylogenomics Resolves the Phylogeny of Theaceae by Using Low-Copy and Multi-Copy Nuclear Gene Makers and Uncovers a Fast Radiation Event Contributing to Tea Plants Diversity

Simple SummaryThe Theaceae includes more than 300 species of great morphological diversity and has immense economic, cultural, and ornamental values. However, the evolutionary history of this family remains elusive. We integrated 91 genomes and transcriptome datasets of Theaceae and successfully resolved the phylogeny of Theaceae including relatives of cultivated tea plants from both extensive low-copy and multi-copy nuclear gene markers. Bayes-based molecular dating revealed that the ancestor of the tea family originated slightly earlier than the K-Pg boundary (Mass extinction events including the extinction of dinosaurs) with early diversification of three tribes associated with the Early Eocene Climatic Optimum. Further speciation analysis suggested a sole significant diversification shift rate in the common ancestor of Camellia associated with the Mid-Miocene Climatic Optimum. Collectively, polyploidy events, and key morphological innovation characters, such as pericarp with seed coat hardening, could possibly contribute to the Theaceae species diversity.Tea is one of the three most popular nonalcoholic beverages globally and has extremely high economic and cultural value. Currently, the classification, taxonomy, and evolutionary history of the tea family are largely elusive, including phylogeny, divergence, speciation, and diversity. For understanding the evolutionary history and dynamics of species diversity in Theaceae, a robust phylogenetic framework based on 1785 low-copy and 79,103 multi-copy nuclear genes from 91 tea plant genomes and transcriptome datasets had been reconstructed. Our results maximumly supported that the tribes Stewartieae and Gordonieae are successive sister groups to the tribe Theeae from both coalescent and super matrix ML tree analyses. Moreover, in the most evolved tribe, Theeae, the monophyletic genera Pyrenaria, Apterosperma, and Polyspora are the successive sister groups of Camellia. We also yield a well-resolved relationship of Camellia, which contains the vast majority of Theaceae species richness. Molecular dating suggests that Theaceae originated in the late L-Cretaceous, with subsequent early radiation under the Early Eocene Climatic Optimal (EECO) for the three tribes. A diversification rate shift was detected in the common ancestors of Camellia with subsequent acceleration in speciation rate under the climate optimum in the early Miocene. These results provide a phylogenetic framework and new insights into factors that likely have contributed to the survival of Theaceae, especially a successful radiation event of genus Camellia members to subtropic/tropic regions. These novel findings will facilitate the efficient conservation and utilization of germplasm resources for breeding cultivated tea and oil-tea. Collectively, these results provide a foundation for further morphological and functional evolutionary analyses across Theaceae.

Concurrent Material Selection of Natural Fibre Filament for Fused Deposition Modeling Using Integration of Analytic Hierarchy Process/Analytic Network Process

The employment of natural fibres in fused deposition modeling has raised much attention from researchers in finding a suitable formulation for the natural fibre composite filaments. Moreover, selection of suitable natural fibres for fused deposition modeling should be performed before the development of the composites. It could not be performed without identifying selection criteria that comprehend both materials and fused deposition modeling process requirements. Therefore, in this study, integration of the Analytic Hierarchy Process (AHP)/Analytic Network Process (ANP) has been introduced in selecting the natural fibres based in different clusters of selection concurrently. The selection process has been performed based on the interdependency among the selection criteria. Pairwise comparison matrices are constructed based on AHP’s hierarchical model and super matrices are constructed based on the ANP’s network model. As a result, flax fibre has ranked at the top of the selection by scored 19.5% from the overall evaluation. Flax fibre has excellent material properties and been found in various natural fibre composite applications. Further investigation is needed to study the compatibility of this fibre to be reinforced with a thermoplastic polymer matrix to develop a resultant natural fibre composite filament for fused deposition modeling.

Identify and prioritize the factors affecting human resource performance management with emphasis on the role of digital city

BACKGROUND AND OBJECTIVES: Technological advances and the expansion of its application in urban communities have led to extensive changes in conceptual dimensions, strategic importance and geographical concentration of urban services. Today, cities are at the highest level of need to use new methods and technologies of service. Utilizing the numerous capabilities of technology in the field of urban management also has tremendous consequences, and its development in the form of intelligent municipal services requires the proper management of human resources. With the advent of the Fourth Revolution and the development of a new paradigm called digital human resource management, various areas of the human resource management process, including human resource performance management, need to be revised and updated based on this approach. Therefore, the purpose of this study is to identify the factors affecting human resource performance management with emphasis on the digital city and the prioritization of factors in the Municipality of Tehran.METHOD: This research is applied in terms of purpose, descriptive-survey in terms of method. In order to extract the research background the library method and for data collection purposes the field method, and questionnaire tools were used. After applying the selection criteria, 10 articles were selected for information extraction. After extracting the initial indicators using Delphi technique, 10 experts were interviewed. In order to analyze the data, confirmatory factor analysis and structural equations using partial least squares method have been used. The perspective of 11 employees of Municipality of Tehran using pairwise comparison questionnaire and their aggregation (with geometric mean) and analytic technique network process were performed and factors were prioritized with Super Decisions software.RESULTS: All items had a t-statistic greater than 1.96; therefore, none of the items were removed from the model and in total, all coefficients were significant at the 95% level. The relative weight of technological factor was 0.537, organizational 0.045, behavioral 0.078 and environmental 0.340 and since IR > 0.1=0.07, then there is consistency in pairwise comparisons. With the formation of a limit super matrix through software, the values of technological factors with 0.133, organizational 0.124, behavioral 0.086 and environmental 0.071, respectively, had the first to fourth priorities for human resource performance management with emphasis on the role of digital city.CONCLUSION: According to the obtained indicators, four factors affecting the management of human resource performance including technological, organizational, behavioral, and environmental factors were obtained. Findings from network analysis among all the factors, technological factor had the most impact and organizational factor had the least impact on human resource performance management with emphasis on the role of the digital city.

MV-LFN: Multi-view based local information fusion network for 3D shape recognition

3D shape recognition is a challenging task due to the difficulty of representing the complex structure of 3D shapes. Recently, the view-based approaches that utilize the multiple views rendered from the shape for visual information extraction and feature aggregation to generate a global shape descriptor, achieved promising performance. However, the view-based approaches commonly ignore the exploration and utilization of local information in the multiple views, which influences the effectiveness of generated features. In this paper, we design a novel Multi-view based Local Information Fusion Network (MV-LFN) for the 3D shape recognition task. The local correlation attention mechanism (LCAM) is introduced to exploit the local correlations in the feature maps for generating a more effective view descriptor. Then, we hierarchically aggregate the multi-view feature maps to generate a shape super matrix (SSM). The local information is effectively extracted and maintained during the multi-view aggregation process, and the discrimination of shape descriptors is significantly improved. We conduct comparative experiments on the ModelNet and ShapeNetCore55 databases. The experimental performances effectively validate the superiority of MV-LFN.

Fine-grained distributed averaging for large-scale radio interferometric measurement sets

The Square Kilometre Array (SKA) would be the world’s largest radio telescope with eventually over a square kilometre of collecting area. However, there are enormous challenges in its data processing. The use of modern distributed computing techniques to solve the problem of massive data processing in the SKA is one of the most important challenges. In this study, basing on the Dask distribution computational framework, and taking the visibility function integral processing as an example, we adopt a multi-level parallelism method to implement distributed averaging over time and channel. Dask Array was used to implement super large matrix or arrays with supported parallelism. To maximize the usage of memory, we further exploit the data parallelism provided by Dask that intelligently distributes the computational load across a network of computer agents and has a built-in fault tolerance mechanism. The validity of the proposed pattern was also verified by using the Common Astronomy Software Application (CASA), wherein we analyze the smearing effects on images reconstructed from different resolution visibilities.

Detection of subgenome bias using an anchored syntenic approach in Eleusine coracana (finger millet)

BackgroundFinger millet (Eleusine coracana 2n = 4x = 36) is a hardy, nutraceutical, climate change tolerant, orphan crop that is consumed throughout eastern Africa and India. Its genome has been sequenced multiple times, but A and B subgenomes could not be separated because no published genome for E. indica existed. The classification of A and B subgenomes is important for understanding the evolution of this crop and provide a means to improve current and future breeding programs.ResultsWe produced subgenome calls for 704 syntenic blocks and inferred A or B subgenomic identity for 59,377 genes 81% of the annotated genes. Phylogenetic analysis of a super matrix containing 455 genes shows high support for A and B divergence within the Eleusine genus. Synonymous substitution rates between A and B genes support A and B calls. The repetitive content on highly supported B contigs is higher than that on similar A contigs. Analysis of syntenic singletons showed evidence of biased fractionation showed a pattern of A genome dominance, with 61% A, 37% B and 1% unassigned, and was further supported by the pattern of loss observed among cyto-nuclear interacting genes.ConclusionThe evidence of individual gene calls within each syntenic block, provides a powerful tool for inference for subgenome classification. Our results show the utility of a draft genome in resolving A and B subgenomes calls, primarily it allows for the proper polarization of A and B syntenic blocks. There have been multiple calls for the use of phylogenetic inference in subgenome classification, our use of synteny is a practical application in a system that has only one parental genome available.

An Delineate of Super Fuzzy Matrix and their Inverse: A Review

Fuzzy matrix is one the leading and developing concept in day today life. Specially, super fuzzy matrix is essential of every fuzzy concept if need for error free. In this paper an impression anchored in the various types of fuzzy, super fuzzy and inverse of super fuzzy related concept.

Identification of important nodes based on dynamic evolution of inter-layer isomorphism rate in temporal networks

The identification of important nodes can not only improve the research about the structure and function of the network, but also encourage people to widely promote the application fields such as in infectious disease prevention, power grid fault detection, information dissemination control, etc. Currently, numerous conclusions have been proved on the identification of important nodes based on the static-network, which may lead the general property to be weakened as resistivity and conductivity experience the dynamic evolution of the relationship between network nodes with time. Temporal network analysis can more accurately describe the change of interaction order and interaction relationship of network nodes in the process of spatio-temporal evolution, and establish an appropriate temporal network model, as well as provide scientific theoretical support for the identification of important nodes. In this paper, we pay attention to considering the intensity of adjacent and cross-layer coupling, and propose a super-adjacency matrix (ISAM) method based on inter-layer isomorphism rate to represent the temporal networks and measure the importance of nodes. And at the same time, it is given that the temporal network &lt;i&gt;G&lt;/i&gt; has &lt;i&gt;N&lt;/i&gt; nodes and &lt;i&gt;T&lt;/i&gt; time layers, and the ISAM is a super adjacency matrix composed of intra-layer and inter-layer relationships of adjacent and cross-layer networks, and its size is &lt;i&gt;NT&lt;/i&gt; × &lt;i&gt;NT&lt;/i&gt;. We focus on the study of the coupling between adjacent and cross-layer networks. The traditional method (SAM) considers the isomorphism rate of adjacent layers as a constant. In the improved method (SSAM), the connection between layers is described by a neighbor topological overlap coefficient. In this paper, the concept of the compatible similarity between cross-layer networks is given first, and then, by combining the projection value of vectors in &lt;i&gt;n&lt;/i&gt;-dimensional real space and the contribution value of node neighbors, the inter-layer approximation relation coefficient of temporal network is inferred and analyzed. Generally speaking, it ensures the difference in coupling degree among different nodes in the inter-layer relationship. We calculate the importance of nodes based on eigenvector centrality in temporal network, which presents the importance of node &lt;i&gt;i&lt;/i&gt; progressing with time. Simultaneously, the robustness of temporal network is studied by making use of the difference in temporal global efficiency. In the end, the operator of Kendall correlation coefficient is used to evaluate the node ranking effect of different time layers between the eigenvector-based centrality and the difference of temporal global efficiency. According to the experimental results of ISAM, SSAM and SAM on Workspace and Email-eu-core data sets, the average Kendall &lt;i&gt;τ&lt;/i&gt; of both ISAM methods considering adjacent and cross-layer network isomorphism rate can be increased by 8.37% and 2.99% respectively. The conclusions show that the measurement method of temporal network inter-layer isomorphism rate is reliable and effective.

Designing Software Application Decision With Multi Criteria Android-based Analytic Network Process Algorithm

Multi-criteria decision-making is one of the techniques in determining a choice of several alternative options. There are several methods in making decisions, one of which is ANP (Analytic Network Process). ANP is a development of AHP (Analytic Hierarchy Process) method. AHP is a hierarchical method in which any existing criteria will be compared first which will produce an ordinary matrix. While ANP is a network method that compares all things including criteria and alternatives that exist so as to obtain a super matrix is very complex. Currently ANP method is not only calculated but already there is software support one of them is Super Decisions. The software can be used to generate a decision by the ANP method. Every software must have advantages and disadvantages. That's why there is a need for software development that has been there before to appear new software better.