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Providing a structured approach for complex product development (PD) is a challenging issue that necessitates utilizing sophisticated modeling tools and analysis techniques. One effective approach for optimizing the complex engineering system development is the cross application of modularization and sequencing analyses on the design structure matrix (DSM), which leads to a cost-effective and time-efficient PD process. In this regard, the current study presents an application-specific method for the simultaneous implementation of modularization and sequencing analyses on DSMs to find the most independent and excluded modules with the minimum feedback loops among them. To this end, we first formulate the total feedback length among the modules by introducing two new modules’ feedback criteria. Second, we propose a novel objective function combining these feedback criteria with the modularity index metric to simultaneously fulfill the sequencing and modularization goals. This objective function makes the proposed method application specific in the sense that the expert is able to adjust the importance weight of each criterion based on the project's characteristics. Third, an efficient metaheuristic algorithm named discrete particle swarm optimization simulated annealing (DPSOSA) is developed to find the optimal solution by minimizing the introduced objective function. Eventually, to evaluate the DPSOSA performance in different applications, we conduct comprehensive experiments on a total of 21 well-known real-world DSMs. The obtained results demonstrate the superiority of the proposed method over previous approaches proving its effectiveness in each of modularization, sequencing, and modularization-sequencing experiments.

Community detection in multiview networks has drawn an increasing amount of attention in recent years. Many approaches have been developed from different perspectives. Despite the success, the problem of community detection in adversarial multiview networks remains largely unsolved. An adversarial multiview network is a multiview network that suffers an adversarial attack on community detection in which the attackers may deliberately remove some critical edges so as to hide the underlying community structure, leading to the performance degeneration of the existing approaches. To address this problem, we propose a novel approach, called higher order connection enhanced multiview modularity (HCEMM). The main idea lies in enhancing the intracommunity connection of each view by means of utilizing the higher order connection structure. The first step is to discover the view-specific higher order Microcommunities (VHM-communities) from the higher order connection structure. Then, for each view of the original multiview network, additional edges are added to make the nodes in each of its VHM-communities fully connected like a clique, by which the intracommunity connection of the multiview network can be enhanced. Therefore, the proposed approach is able to discover the underlying community structure in a multiview network while recovering the missing edges. Extensive experiments conducted on 16 real-world datasets confirm the effectiveness of the proposed approach.

This paper introduces a new modular cascaded multilevel inverter (MLI) which is composed of a cascaded connection of submodules (SMs). The combination of basic cells (BCs) with polarity changer H-Bridge produces the SMs, which is the basis of the introduced inverter. Owing to its high modularity, the proposed inverter can be extended to reach higher voltage levels. Hence, two extensions are suggested: 1) Extension based on cascading more BCs within each SM, and 2) Extension based on cascading more SMs. Using a set of algorithms, the magnitude of DC voltage sources is determined to synthesize the proper AC side voltage waveform. Furthermore, the proposed topology is optimized in different ways to achieve the desired purposes, such as maximizing the number of voltage levels, minimizing the number of switches and DC sources, and providing the least total standing voltage on the switches. Therefore, in each of the mentioned optimization goals, the optimal structure is obtained. A prototype of the presented MLI is defined and its performance is verified through several simulation studies in MATLAB/Simulink and experimental tests. Besides, a comprehensive comparison is made with the best performance of the existing alternatives to demonstrate the merits and superiorities of the proposed MLI.

This paper proposed a modular three-phase multilevel inverter configuration using level doubling network (LDN). This configuration doubles voltage levels in each phase, preserving modularity and without increasing the device count and ratings. Preserved modularity and switching redundancies of the configuration support inverter fault-tolerant ability. A simple control algorithm imposing a popular level-shifted carrier arrangement is used to implement the proposed configuration. Performance of the proposed configuration and PWM is analyzed and validated for multiple case studies in the Simulink environment using MATLAB.

The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.

The advanced modular powertrains are envisioned as primary part of future hybrid fuel cell vehicles (FCVs). The existing papers in the literature solely cope with the hardware side of modularity, while the software side is also vital to capitalize on the total capacity of these powertrains. Driven by this motivation, this article puts forward a comparative study of two novel decentralized convex optimization frameworks based on alternating direction method of multipliers (ADMM) to solve a multi-objective power allocation strategy (PAS) problem in a modular FCV (MFCV). The MFCV in this article is composed of two fuel cell (FC) stacks and a battery pack. Despite the existing centralized strategies for such a modular system, this manuscript proposes two decentralized PASs (Dec-PASs) based on Consensus ADMM (C-ADMM) and Proximal Jacobian ADMM (PJ-ADMM) to bridge the gap regarding the appreciation of modularity in software terms. Herein, after formulating the central PAS optimization problem, the principle of utilizing such decentralized algorithms is presented in detail. Subsequently, the performance of the proposed Dec-PASs is examined through several numerical simulations as well as experiments on a developed small-scale test bench. The obtained results illustrate that decomposition into decentralized forms enables solving the complex PAS optimization problem faster and provides modularity and flexibility. Furthermore, the proposed Dec-PASs can cope with fault and malfunction and thus augment the durability and robustness of modular powertrain systems.

Community detection is an important area of research in social media mining. Numerous algorithms have been developed to detect disjoint, overlapping, and dynamic communities in a network. However, most of the existing algorithms do not consider the influence of each node in a group, which may be different and can affect the result of community detection. In this paper, we have proposed a novel Louvain-based algorithm named “NI-Louvain,” which considers the influence of each node in a group that not only detects community but also most influential nodes in the community. The proposed algorithm works in three steps. First, the input graph is processed to reduce its density. This is done by calculating cliques from the graph. In second step, Louvain's multilevel algorithm is applied to this clique graph. The resultant community obtained has a better modularity value than the communities obtained from existing algorithms like edge betweenness, label propagation, leading eigen, Louvain, fast greedy, walktrap, and infomap. In the third step, the nodes of resultant communities are transformed back to the nodes of the original graph. This step is beneficial in detecting overlapping communities, fuzzy membership of each node, most influential node in each community formed, and outlier nodes.

Graph theory allows us to gain better insight concerning changes in brain functional architecture associated with cognitive impairments in the early stages of multiple sclerosis (MS). In the present study, we employed a machine-learning system based on graph measures from functional networks constructed by cognitive task-related functional magnetic resonance imaging (fMRI) data. We used a predefined atlas to define the brain regions and Pearson’s correlation to describe the connectivity strength between the regions. Then, several graph metrics were extracted for each subject. After that, the most efficient subsets of features were selected through the Wilcoxon rank-sum test, and the linear support vector machine (SVM) classifier was employed to distinguish between MS and healthy subjects. The node degree, subgraph centrality, K-coreness, and PageRank centralities measured in the left fusiform, hippocampus, and parahippocampal gyri regions demonstrated an accuracy of 85% through the combination of all local measures. Two optimal global measures, modularity and small-worldness index, and individual betweenness centrality feature improved the identification of MS patients with a sensitivity of 81.25%. Our results indicated the potential of the proposed system to identify cognitive changes in early MS for diagnostic purposes.

In the last decade, fuzzy community detection has received increasing attention, since it} can not only uncover community structure of a network, but also reflect the membership degrees of each node to multiple communities. Although some pioneers proposed a few algorithms for finding fuzzy communities, there is still room for further improvement in the quality of detected fuzzy communities. In this study, a metaheuristic-based modularity optimization algorithm, named Symbiotic Organisms Search Fuzzy Community Detection (SOSFCD) is proposed. On the one hand, an improved bio-inspired metaheuristic algorithm, Elite Symbiotic Organisms Search (Elite-SOS), is designed as optimization strategy to improve the global convergence of fuzzy modularity optimization. On the other hand, a Neighbor-based Membership Modification (NMM) operation is proposed to intensify exploitation ability and speed up convergence, by efficiently utilizing local information (i.e., node neighborhood) of network topology. Experimental results on both of synthetic and real-life networks with different scales and characteristics show that SOSFCD can find max-modularity fuzzy partitions and coverings, which outperforms many state-of-the-art algorithms in terms of accuracy and stability.

The timely and accurate measurement of nitrogen oxide (NOx) emissions is important for efficient pollution controlling of municipal solid waste incineratio plants. To design an efficient and effective prediction model for NOx concentrations, a brain-inspired modular neural network (BIMNN) is developed in this study. First, a biologically-inspired modularization technique is proposed, in which the topological modularity gives rise to functional modularity. Consequently, different modules correspond to different tasks, improving the network efficiency by performing task decomposition. Subsequently, an adaptive task-oriented radial basis function (ATO-RBF) neural network is applied to construct each module. The ATO-RBF neural network is comprised of a structure self-organizing mechanism and an adaptive second-order learning algorithm, providing basis for generalization ability of BIMNN. Finally, during the testing or application stages, a competitive strategy is utilized to integrate the modules. The proposed prediction methodology is verified using industrial data, and the experimental results demonstrate its outperformance.

Signed networks depict the individual cooperative or hostile relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or communities. In this article, first of all, an improved modularity function for signed networks is proposed on the basis of the existing modularity function. Then, a new community detection algorithm for signed networks has also been devised, and time complexity analysis shows that the time required for the algorithm has a linear relationship with the number of nodes in the sparse networks. Meanwhile, the affinity index that can be used to convert directed signed networks into the corresponding undirected signed networks is come up with. Finally, the current algorithm has been applied into several illustrative and realistic networks. The experimental results indicate that the number of communities given by the proposed algorithm is consistent with that of actual communities, and thus, it can be further conducive to identifying the community structure hidden within the real-world systems.

Epistasis between single nucleotide polymorphisms (SNPs) plays an important role in elucidating the missing heritability of complex diseases. Diverse approaches have been invented for detecting SNP interactions, but they canonically neglect the important and useful connections between SNPs and other bio-molecules (i.e., miRNAs and lncRNAs). To comprehensively model these disease related molecules, a heterogeneous bio-molecular network based solution EpiHNet is introduced for high-order SNP interactions detection. EpiHNet firstly uses case/control data to construct an SNP statistical network, and meta-path based similarity on the heterogeneous network composed with SNPs, genes, lncRNAs, miRNAs and diseases to define another SNP relational network. The SNP relational network can explore and exploit different associations between molecules and diseases to complement the SNP statistical network and search the significantly associated SNPs. Next, EpiHNet integrates these two networks into a composite network, applies the modularity based clustering with fast search strategy to divide SNP nodes into different clusters. After that, it detects SNP interactions based on SNP combinations derived from each cluster. Synthetic experiments on diverse two-locus and three-locus disease models manifest that EpiHNet outperforms competitive baselines, even without the heterogeneous network. For real WTCCC breast cancer data, EpiHNet also demonstrates expressive results on detecting high-order SNP interactions.

Sustainable solutions for hydrogen production, such as dye-sensitized photoelectrochemical cells (DS-PEC), rely on the fundamental properties of its components whose modularity allows for their separate investigation. In this work, we design and execute a high-throughput scheme to tune the ground state oxidation potential (GSOP) of perylene-type dyes by functionalizing them with different ligands. This allows us to identify promising candidates which can then be used to improve the cell's efficiency. First, we investigate the accuracy of different theoretical approaches by benchmarking them against experimentally determined GSOPs. We test different methods to calculate the vertical oxidation potential, including GW with different levels of self-consistency, Kohn–Sham (KS) orbital energies and total energy differences. We find that there is little difference in the performance of these methods. However, we show that it is crucial to take into account solvent effects as well as the structural relaxation of the dye after oxidation. Other thermodynamic contributions are negligible. Based on this benchmark, we decide on an optimal strategy, balancing computational cost and accuracy, to screen more than 1000 dyes and identify promising candidates which could be used to construct more robust DS-PECs., The alignment of the GSOP, calculated with the adiabatic approach for large number of dyes, with the limitations of a hypothetical system – the CB edge of TiO2 semiconductor and highest oxidation potential of Ru-based WOC.

An industrial process usually has multiple operating conditions or periods due to various factors such as the fluctuation of raw material quality, differences of worker levels, and changes of product specifications. Industrial data from different operating conditions or periods have different statistical characteristics, namely multimode behavior. In fact, multimode recognition of industrial processes is the first problem to develop effective intelligent manufacturing strategies for engineers. The clustering-based methods can help them characterize multimode behavior in historical databases and group them together with little detailed knowledge of the process. However, these methods are facing the challenges of massive high-dimensional industrial big data and unknown mode number. This study proposes a construction method of complex network to describe the potential relationship in industrial big data. The topological structure of complex network can be acquired by taking the correlation matrix of multi-variables as nodes and the similarity between correlation matrices as edges. On this basis, community detection technology is employed to reveal the aggregation behavior of nodes in network. It takes the maximum modularity as the optimization objective, and partitions each node into one community through greedy search algorithm, where a community denotes a mode of industrial process. Finally, case studies have demonstrated the proposed method has good performance and application prospects.

Node-link visualizations are a familiar and powerful tool for displaying the relationships in a network. The readability of these visualizations highly depends on the spatial layout used for the nodes. In this paper, we focus on computing layered layouts, in which nodes are aligned on a set of parallel axes to better expose hierarchical or sequential relationships. Heuristic-based layouts are widely used as they scale well to larger networks and usually create readable, albeit sub-optimal, visualizations. We instead use a layout optimization model that prioritizes optimality— as compared to scalability— because an optimal solution not only represents the best attainable result, but can also serve as a baseline to evaluate the effectiveness of layout heuristics. We take an important step towards powerful and flexible network visualization by proposing STRATISFIMAL LAYOUT, a modular integer-linear-programming formulation that can consider several important readability criteria simultaneously— crossing reduction, edge bendiness, and nested and multi-layer groups. The layout can be adapted to diverse use cases through its modularity. Individual features can be enabled and customized depending on the application. We provide open-source and documented implementations of the layout, both for web-based and desktop visualizations. As a proof-of-concept, we apply it to the problem of visualizing complicated SQL queries, which have features that we believe cannot be addressed by existing layout optimization models. We also include a benchmark network generator and the results of an empirical evaluation to assess the performance trade-offs of our design choices. A full version of this paper with all appendices, data, and source code is available at osf.io/3vqms with live examples at https://visdunneright.github.io/stratisfimal/.

Massive data is generated due to the intensive use of social networks by learners. Thus, various applications in different domains such as education are conducted to understand the relationships among actors. Generally, learners create learning communities according to their levels, interests, and skills. A large number of approaches have been proposed to detect these communities in social networks. In this study, we propose a new approach to detect and evaluate learning communities in order to help teachers and administrators understand learners’ needs and improve the educational process. This approach is an improved version of our previous model. Hence, the main objective of this article is to lower the execution time of our previous approach and to improve the evaluation process. Our approach mainly consists of two phases. The first phase is developed to discover the learning community using the maximal clique concept, while the second one is devoted to learning community evaluation based on the interactions among learners and their socio-economic characteristics. The performance of our model is tested on four real-world networks: Seventh graders, UC Irvine, UK Faculty, and Forum discussion using the modularity and the silhouette measures. Two types of learning communities have been identified: safe communities and at-risk communities. The experimental results showed that our approach is highly reliable and efficient for discovering and evaluating learning communities in social networks compared to other approaches, as well as it has a low temporal complexity.

Background From the perspective of information processing, an integrated understanding of the structural and functional connectomes in depression patients is important, a multimodal meta-analysis is required to detect the robust alterations in graph metrics across studies. Methods Following a systematic search, 952 depression patients and 1447 controls in nine diffusion magnetic resonance imaging (dMRI) and twelve rest state functional MRI (rs-fMRI) studies with high methodological quality met the inclusion criteria and were included in the meta-analysis. Results Regarding the dMRI results, no significant differences of meta-analytic metrics were found; regarding the rs-fMRI results, the modularity and local efficiency were found to be significantly lower in the depression group than in the controls (Hedge's g = -0.330 and -0.349, respectively). Conclusion Our findings suggested a lower modularity and network efficiency in the rs-fMRI network in depression patients, indicating that the pathological imbalances in brain connectomes needs further exploration. Limitations: Included number of trials was low and heterogeneity should be noted.

Recommender system (RS) suggests relevant objects to generate personalized service and minimize information overload issue. User-based collaborative filtering (UBCF) plays a dominant role in practical RSs. However, traditional UBCF suffers from a recommendation overfitting problem, i.e., recommendations generated by UBCF usually concentrate on popular items, resulting in lower diversity. In addition, UBCF cannot maintain a reasonable tradeoff between the accuracy and diversity of recommendations because raising the diversity is often accompanied by a decrease in accuracy. In this article, we propose a novel approach, namely link-based collaborative filtering, to enhance the recommendation accuracy and diversity simultaneously without employing additional complex information. First, a user-item bipartite network is constructed based on the user-item rating matrix of RSs. Then, a global-local weighted bipartite modularity is presented to conduct link partition so that links with the same community can not only be relatively denser but also own the same characteristic. Furthermore, redundant links are removed from each community by utilizing a link reduction algorithm so that neighborhood of a target user can be selected according to the more efficient nonredundant links. Finally, rating prediction is executed based on the rating information of neighborhood. Also, items owning the highest predicted rating scores will be recommended to the target user. Experimental results from three real datasets of RSs suggest that, without taking advantage of special additional data, our proposed approach outperforms the state-of-the-art studies and is able to generate personalized recommendations with satisfying accuracy and diversity simultaneously.

Background and objective Based on magnetic resonance imaging (MRI), macroscopic structural and functional connectivity of human brain has been widely explored in the last decade. However, little work has been done on effective connectivity between individual brain parcels. In this preliminary study, we aim to investigate whole-brain effective connectivity networks from resting-state functional MRI (rs-fMRI) images. Material and methods After the functional connectivity networks of 26 healthy subjects (aged from 25 to 35 years old) from Human Connectome Project database were derived from rs-fMRI images with dynamic time warping, proportional thresholding (PT) was performed on the functional connectivity matrices by retaining the PT% strongest functional connections. PT% ranges from 40% to 10% in steps of 5%. Then, effective connections corresponding to the PT% strongest functional connections, both bi-directional and unidirectional, were estimated with Renyi's 2-order transfer entropy (TE) method. Topological metrics of the built functional and effective connectivity networks were further characterized, including clustering coefficient, transitivity, and modularity. Results It is found that the effective connectivity networks exhibit small world attributes, and that the networks contain a subset of highly interactive regions, including right frontal pole (in-degree 6), left middle frontal gyrus (in-degree 8, out-degree 1), right precentral gyrus (out-degree 9), left precentral gyrus (out-degree 7), right posterior division of supramarginal gyrus (in-degree 2, out-degree 3), left angular gyrus (out-degree 6), left inferior division of lateral occipital cortex (out-degree 6), right occipital pole (in-degree 5), right cerebellum 7b parcel (in-degree 15), and right cerebellum 8 parcel (in-degree 7, out-degree 1). Conclusions The observations in this study provide information about the casual interactions among brain parcels in resting state, helping reveal how different subregions of large-scale distributed neural networks are coupled together in performing cognitive functions.

Droop control has been well adopted for multi-source multi-load DC microgrids (MGs) due to its inherent modularity and reliability by only using local measurements, especially with the most commonly-used voltage-current droop (V-I) mode. While another implementation named current-voltage droop (I-V) mode also can be adopted. With the same value of droop gain in these two modes, a DC MG will have the same steady-state feature, but its dynamic performance of DC voltage control may be different. Our primary motivation is to investigate this phenomenon from the perspective of equivalent RLC circuits. This paper proposes a generic reduced-order modeling method suitable for exploring the dynamic stability of DC voltage control with these two modes. By ignoring fast inner current control dynamic, each droop based DC voltage control unit can be modeled as a RLC parallel circuit in these two modes, which is convenient for modular modeling and extension. In addition, if the inner current control with slow dynamic cannot be ignored in some specific scenarios, a modified RLC model can be still obtained to analyze its influence. Tthe effectiveness of the proposed reduced-order modeling method has been verified by detailed simulation and experiment results.

Label propagation-based methods are the most popular methods for community detection, which have a linear time complexity, thanks to the use of local features for updating node labels. However, they suffer from major concerns including instability, low accuracy, and discovering monster communities. To solve these problems, this paper proposes a novel distance based peripheral nodes label propagation algorithm for fast community detection, called DPNLP. First, core nodes are detected and their labels are distributed to the neighbors to form the initial communities. Then, labels of the peripheral nodes are identified using combinations of local features. Finally, the structures of communities are extracted after assigning a label to the nodes with degree one and two at the last stage of the method. The proposed method achieves significant speed up because of optimizing the number of required updates. In addition, DPNLP is remarkably stable and it does not have monster-community problem. According to the conducted evaluations over artificial and real-world networks, the proposed methods achieve improved results in terms of NMI, F-measure, modularity, and runtime metrics. Experiments have also been performed to confirm the stability of the algorithm and the lack of monster community’s formation.

Purpose The study aims to analyze the fretting phenomenon, manifested at the taper junctions of modular total hip prostheses (THP). Modularity of prostheses implies the micro-movement occurrence. Fractures can arise as a result of the fretting cracking of the prostheses components, affecting durability of modular THPs. Fretting corrosion is associated with the decrease in the clinical acceptance of hip modular implants. Design/methodology/approach Starting from the fretting phenomenon influence on modularity, monoblock THPs and prostheses with modular femoral head recovered from some review surgeries were investigated. Modular prostheses have a taper junction femoral head – femoral stem neck. Investigation consisted in the analysis of fretting wear and fretting corrosion, of the femoral heads’ taper and of the femoral stems’ trunnions. Findings The main result was that the micro-movement that provokes the fretting of the femoral head-femoral stem taper junction analyzed does not have the same direction. It is manifesting in the direction of the axis of the femoral head taper, around this axis or as a composed movement. The authors suspect that this is due to the different design of the taper. In this way, the inclination of the stem’s trunnion into the head hole has a different angular misalignment and may cause greater damages of the taper. Originality/value This result can be a starting point from the improvement of the future taper junctions design that will improve the quality, durability and modularity of THPs.

Aiming at the problem of the lack of user social attribute characteristics in the process of dividing overlapping communities in multilayer social networks, in this paper, we propose a multilayer social network overlapping community detection algorithm based on trust relationship. By combining structural trust and social attribute trust, we transform a complex multilayer social network into a single-layer trust network. We obtain the community structure according to the community discovery algorithm based on trust value and merge communities with higher overlap. The experimental comparison and analysis are carried out on the synthetic network and the real network, respectively. The experimental results show that the proposed algorithm has higher harmonic mean and modularity than other algorithms of the same type.

We investigate the economic and environmental implications of jointly implementing leasing and modularity, two prominent circular economy strategies, and their interaction. We develop a durable goods model to obtain insights for firms considering the use of these strategies. We first analyze the effect of adopting leasing on a firm’s product architecture choice. We find that a modular product architecture is more attractive under leasing only if off-lease products depreciate to a greater extent. We next analyze the effect of adopting a modular product architecture on the firm’s business model choice. We find that adopting a modular architecture can cause a leasing firm to switch to selling but will not lead a selling firm to switch to leasing. Building on these results, we show that leasing a modular product is preferred by the firm only under limited conditions (i.e., when production costs are low, off-lease products are in relatively better condition, and when there is a large difference between depreciation levels of different modules). Finally, we show that jointly implementing leasing and modularity can also lead to higher environmental impact than only adopting leasing or modularity in isolation. This paper was accepted by Vishal Gaur, operations management.

The 22q11 deletion syndrome is a genetic disorder associated with a high risk of developing psychosis, and is therefore considered a neurodevelopmental model for studying the pathogenesis of schizophrenia. Studies have shown that localized abnormal functional brain connectivity is present in 22q11 deletion syndrome like in schizophrenia. However, it is less clear whether these abnormal cortical interactions lead to global or regional network disorganization as seen in schizophrenia. We analyzed from a graph-theory perspective fMRI data from 40 22q11 deletion syndrome patients and 67 healthy controls, and reconstructed functional networks from 105 brain regions. Between-group differences were examined by evaluating edge-wise strength and graph theoretical metrics of local (weighted degree, nodal efficiency, nodal local efficiency) and global topological properties (modularity, local and global efficiency). Connectivity strength was globally reduced in patients, driven by a large network comprising 147 reduced connections. The 22q11 deletion syndrome network presented with abnormal local topological properties, with decreased local efficiency and reductions in weighted degree particularly in hub nodes. We found evidence for abnormal integration but intact segregation of the 22q11 deletion syndrome network. Results suggest that 22q11 deletion syndrome patients present with similar aberrant local network organization as seen in schizophrenia, and this network configuration might represent a vulnerability factor to psychosis.

Let F be a totally real number field which is unramified at 3 , 5 , and 7. We give explicit conditions on a finite place v 0 of F such that any elliptic curve over F with potential good reduction at v 0 is modular. The main point is to prove that there exist no elliptic curves with reducible mod 5, and 7 representations (or mod 3, 5, and 7 representations) under our assumptions.

This article presents a redesign methodology based on modularity to minimize resource consumption and reuse components, avoiding the need to replace a whole product with another with higher functional performance. The method employs two decision algorithms to modularize product families that offer the same functionality in different levels (i.e., scalable functions) based on design parameters such as geometry, size, and functional relationships among components. The proposed approach's benefits are demonstrated through a case study of a family of upper limb prostheses. Significant improvements in the manufacturing stage, such as raw material and energy consumption, manufacturing cost, and complexity, were obtained from implementing the method. Other benefits in the use stage were also obtained from modularization, increasing the product family's reuse of components.

Interdependencies in social groups of animals are a combination of multiple pairwise interactions. Heterospecific groups are often characterized by important species that contribute more to group initiation, maintenance or function than other species. However, in large heterospecific groups, many pairwise interactions are not realised, while others may not be biologically significant, confounding inferences about species importance. Hence, in this study, we examine context dependent changes in species importance and assortment in mixed-species bird flocks from a tropical field site in Southern India using social network analysis. Specifically, we ask how the structural importance of a species and the clustering patterns of species relationships depends on species richness in mixed-species flocks. We constructed both raw and filtered networks; while our results are largely correlated, we believe that filtered networks can provide insights into community-level importance of species in mixed-flocks while raw networks depict flock-level patterns. We find significant differences in flocks of different richness in that different species emerge as structurally important across flocks of varying richness. We also find that assortment is higher in two-species flocks and decreases with an increase in the number of species in the flock (‘flock richness’ hereafter). We argue that the link between structural importance of species in mixed-species flock networks and their functional significance in the community critically depends on the social context: namely, the species richness of the mixed-species flock. We propose that examining species structural importance at different flock-richness values provides insights into biologically meaningful functional roles of species. More generally, we suggest that it is important to consider context when interpreting species centrality and importance in network structure.

Introduced species can alter the dynamics and structure of a native community. Network analysis provides a tool to study host–parasite interactions that can help to predict the possible impact of biological invasions or other disturbances. In this study, we used weighted bipartite networks to assess differences in the interaction patterns between hosts and helminth parasites of native (Sea of Japan) and invasive (Black Sea and Sea of Azov) populations of Planiliza haematocheilus (Teleostei: Mugilidae). We employed three quantitative network descriptors, connectance, weighted nestedness and modularity, to gain insight into the structure of the host–parasite networks in the native and invaded areas. The role of parasite species in the networks was assessed using the betweenness centrality index. We analyzed networks encompassing the whole helminth community and subsets of species classified by their transmission strategy. The analyses were downscaled to host individual-level to consider intraspecific variation in parasite communities. We found significant differences between networks in the native and invaded areas. The latter presented a higher value of nestedness, which may indicate a co-occurrence between parasite species with many connections in the network and species with fewer interactions within the same individual-host. In addition, modularity was higher in the native area’s networks than those of the invaded area, with subgroups of host individuals that interact more frequently with certain parasite species than with others. Only the networks composed of actively transmitted parasites and ectoparasites did not show significant differences in modularity between the Sea of Azov and the Sea of Japan, which could be due to the introduction of a part of the native community into the invaded environment, with a lower diversity and abundance of species. We show that network analysis provides a valuable tool to illuminate the changes that occur in host–parasite interactions when an invasive species and its parasite community are introduced into a new area.

Given a target protein structure, the prime objective of protein design is to find amino acid sequences that will fold/acquire to the given three-dimensional structure. The protein design problem belongs to the non-deterministic polynomial-time-hard class as sequence search space increases exponentially with protein length. To ensure better search space exploration and faster convergence, we propose a protein modularity-based parallel protein design algorithm. The modular architecture of the protein structure is exploited by considering an intermediate structural organization between secondary structure and domain defined as protein unit (PU). Here, we have incorporated a divide-and-conquer approach where a protein is split into PUs and each PU region is explored in a parallel fashion. It has been further analyzed that our shared memory implementation of modularity-based parallel sequence search leads to better search space exploration compared to the case of traditional full protein design. Sequence-based analysis on design sequences depicts an average of 39.7% sequence similarity on the benchmark data set. Structure-based comparison of the modeled structures of the design protein with the target structure exhibited an average root-mean-square deviation of 1.17 Å and an average template modeling score of 0.89. The selected modeled structures of the design protein sequences are validated using 100 ns molecular dynamics simulations where 80% of the proteins have shown better or similar stability to the respective target proteins. Our study informs that our modularity-based protein design algorithm can be extended to protein interaction design as well.

In recent years, many attributednetwork have emerged, such as Facebook networks in social networks, protein networks and academic citation networks. In order to find communities where the nodes are tightly connected and have attributes similar to each other by unsupervised learning and improve the accuracy of community detection to make better analysis of the attributed networks, we propose a two-stage attributed network community detection combined with network embedding and parameter-free clustering. In the first stage, we build an attributed network embedding framework that integrates common neighbor information and node attributes. We define node similarity in terms of local link information, jointly model it with attribute proximity, and then adopt the distributed algorithm to obtain the embedding vector of each node. In the second stage, the number of communities can be decided automatically based on curvature and modularity, and the community detection results can be obtained by clustering the embeddings. The performance experiments of our method compared with some representative approaches are tested on real network datasets. The experimental results validate the effectiveness and superiority of our approach.

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this paper, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. Firstly, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications and the modular topology, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the NoC design generated by the proposed method presents significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs., Comment: 19 pages, 15 figures, 8 tables, accepted by ACM TODAES

Research on learning, the structure of attained knowledge, and the use of this competence in performance has repeatedly returned to longstanding proposals about how to better understand proficient use of knowledge and how humans acquire it. The following article takes up an exchange between Chiappe & Gardner (2011) and Barrett & Kurzban (2012) on the concept of modularity, one of these proposals. Despite the disagreements expressed, a careful reading of the contributions shows that they also left us with lines of discussion that will eventually sort out the relevant hypotheses and integrate findings for future research. These lines of work will contribute to a clearer understanding of an updated version of the modularity hypothesis that is also compatible with evolutionary science perspectives on learning. How might the categories of domain-specific and domain-general correspond to the distinction between competence and performance and to that of narrow faculty and broad faculty?

Using artificial intelligence (AI) components to solve real-world heterogeneous tass is more than simple plug and play type integration and often necessitates tight coupling of components. With current state of the art, such integration consumes large engineering effort due to problems like low expressivity, modularity, reusability, and abstraction. To alleviate these problems, a dataflow graph-based functional programming model is proposed to integrate AI components as microservices. Along with loose coupling, the integration platform also allows tight coupling of components to solve complex heterogeneous tasks without compromising modularity. A graphical method of construction of integration programs relieves a developer from learning programming syntax. The usability of the proposed method has been demonstrated by solving a heterogeneous maze problem where an agent needs to solve a reinforcement learning environment, a machine vision task, and integrate them. Solving a speech recognition problem demonstrates modularity and functional abstraction of the method. Qualitative comparison with current state-of-the-art methods based on these use cases justifies the claims.

Detecting communities or the modular structure of real-life networks (e.g. a social network or a product purchase network) is an important task because the way a network functions is often determined by its communities. Traditional approaches to community detection involve modularity-based algorithms, which generally speaking, construct partitions based on heuristics that seek to maximize the ratio of the edges within the partitions to those between them. On the other hand, node embedding approaches represent each node in a graph as a real-valued vector and is thereby able to transform the problem of community detection in a graph to that of clustering a set of vectors. Existing node embedding approaches are primarily based on, first, initiating random walks from each node to construct a context of a node, and then make the vector representation of a node close to its context. However, standard node embedding approaches do not directly take into account the community structure of a network while constructing the context around each node. To alleviate this, we propose a community structure aware node embedding approach, where we incorporate an initial combinatorial approach-based partition information into the objective function of node embedding. We demonstrate that our proposed combination of the combinatorial and the embedding approaches for community detection outperforms a number of combinatorial-based baselines on a wide range of real-life and synthetic networks of different sizes and densities.

It is crucial to generate random graphs with specific structural properties from real graphs, which could anonymize graphs or generate targeted graph data sets. The state-of-the-art method called spectral graph forge (SGF) was proposed at INFOCOM 2018. This method uses a low-rank approximation of the matrix by throwing away some spectrums, which provides privacy protection after distributing graphs while ensuring data availability to a certain extent. As shown in SGF, it needs to discard at least 20% spectrum to defend against deanonymous attacks. However, the data availability will be significantly decreased after more spectrum discarding. Thus, is there a way to generate a graph that guarantees maximum spectrum and anonymity at the same time? To solve this problem, this paper proposes graph nonlinear scaling (GNS). We firmly prove that GNS can preserve all eigenvectors meanwhile providing high anonymity for the forged graph. Precisely, the GNS scales the eigenvalues of the original spectrum and constructs the forged graph with scaled eigenvalues and original eigenvectors. This approach maximizes the preservation of spectrum information to guarantee data availability. Meanwhile, it provides high robustness towards deanonymous attacks. The experimental results show that when SGF discards only 10% of the spectrum, the forged graph has high data availability. At this time, if the distance vector deanonymity algorithm is used to attack the forged graph, almost 100% of the nodes can be identified, while when achieving the same availability, only about 20% of the nodes in the forged graph obtained from GNS can be identified. Moreover, our method is better than SGF in capturing the real graph’s structure in terms of modularity, the number of partitions, and average clustering.

Interactions among living beings are the structuring basis of ecosystems, and studies of networks allow us to identify the patterns and consistency of such interactions. Antagonistic networks reflect the energy flow of communities, and identifying network structure and the biological aspects that influence its stability is crucial to understanding ecosystem functioning. We used antagonistic anuran interactions-predator-prey and host-parasite-to assess structural patterns and to identify the key anuran species structuring these networks. We tested whether anuran body-size and life-habit are related to their roles in these networks. We collected individuals of 9 species of anurans from an area of the Atlantic Forest in Brazil and identified their prey and helminth parasites. We used network (modularity, specialization, and nestedness) and centrality metrics (degree, closeness, and betweenness) to identify the role of anuran species in both networks. We then evaluated whether anuran body-size or life-habit were related to anuran centrality using generalized linear mixed models. The networks formed specialized interactions in compartments composed by key species from different habits. In our networks, anurans with rheophilic and cryptozoic habit are central in predator-prey networks, and those with larger body size and arboreal and cryptozoic habit in the host-parasite network. This study represents a step towards a better understanding of the influential factors that affect the structure of anuran antagonist networks, as well as to recognize the functioning roles of anuran species.

Accumulative structure or cluster-like shape is one of the important features of social networks. These structures and clusters are communities in a complex network and are fully detectable. Common group behaviors of different communities can be categorized using community detection methods. Categorize behavior allows the study of each part of the network to be done centrally. This paper uses trust-based centrality to detect the communities that make up the network. Centrality determines the relative importance of a node in the graph of social networks. Redefining the trust-based centrality makes it possible to change the position in the analysis of centrality and separates the local central nodes and global central nodes. Then, a trust-based algorithm is proposed to express the strength of trust penetration conceptually between nodes to extract communities in networks. This method has led to the achievement of a flexible and effective community detection method. The proposed algorithm is applied to four benchmark networks. The experiments consist of two independent parts. The first part is to use the proposed algorithm to detect clusters and communities. After that, the algorithm is compared with a Girvan–Newman inspired method. The second part is the implementation of the proposed algorithm with a large number of iterations with the aim of modularity maximization and comparing it with other community detection algorithms. Although, the modularity criterion has been used to validate and compare the solution quality in both independent parts of the experiments. The results show about 1.4–5.2% improvement in community detection.

In order to identify the basic structural properties of a network such as connectedness, centrality, modularity, accessibility, clustering, vulnerability, and robustness, we need distance-based parameters. A number of tools like these help computer and chemical scientists to resolve the issues of informational and chemical structures. In this way, the related branches of aforementioned sciences are also benefited with these tools as well. In this paper, we are going to study a symmetric class of networks called convex polytopes for the upper and lower bounds of fractional metric dimension (FMD), where FMD is a latest developed mathematical technique depending on the graph-theoretic parameter of distance. Apart from that, we also have improved the lower bound of FMD from unity for all the arbitrary connected networks in its general form.

The success of deep neural networks suggests that cognition may emerge from indecipherable patterns of distributed neural activity. Yet these networks are pattern-matching black boxes that cannot simulate higher cognitive functions and lack numerous neurobiological features. Accordingly, they are currently insufficient computational models for understanding neural information processing. Here, we show how neural circuits can directly encode cognitive processes via simple neurobiological principles. To illustrate, we implemented this model in a non-gradient-based machine learning algorithm to train deep neural networks called essence neural networks (ENNs). Neural information processing in ENNs is intrinsically explainable, even on benchmark computer vision tasks. ENNs can also simulate higher cognitive functions such as deliberation, symbolic reasoning and out-of-distribution generalization. ENNs display network properties associated with the brain, such as modularity, distributed and localist firing, and adversarial robustness. ENNs establish a broad computational framework to decipher the neural basis of cognition and pursue artificial general intelligence. The authors demonstrate how neural systems can encode cognitive functions, and use the proposed model to train robust, scalable deep neural networks that are explainable and capable of symbolic reasoning and domain generalization.

Brain disease diagnosis based on brain network classification has become a hot topic. Recently, classification methods based on convolutional neural networks (CNNs) have attracted much attention due to their ability to capture the basic topological structure of the brain network. However, they ignore abnormal structures within modules caused by brain disease, which limits the diagnostic accuracy. In this paper, we propose a novel brain network classification framework based on a CNN model capable of extracting modular features from brain networks at the node and whole-network levels. More specifically, we first develop a novel algorithm to obtain the modular structure of each node, which is then fed into a CNN model to extract the node-level modular features. Second, we minimize the harmonic modularity of the extracted node-level features to reveal the modular structure at the whole-brain network level. Finally, we employ a deep neural network to further extract high-level features for the classification of brain disease. The experimental results on a real-world autism spectrum disorder dataset show that our proposed method achieves the best accuracy of 68.55% and outperforms other common methods and demonstrates the discriminant power of the modular features at multiple levels. In addition, feature analysis based on the trained framework reveals the associations between modular structures and brain disease, which provides new insights into the pathological mechanism from the perspective of modular structures.

The customer demands of various products bring a challenge for manufacturers. They have to design customized products while maintaining economies of scale and low costs. In this paper, to address this challenge, four approaches are argued to help companies find out the optimal solutions of products’ performance and the maximum profit: (i) only platform modularity without component sharing (ii) only component sharing without platform modularity, (iii) using both platform modularity and component sharing to develop products, or iv) the products are developed individually from a given unshared components set. A theoretical model is proposed and the most profitable approach is found to develop a whole new product family when uncertainty exists in the customer demand and economies of scale with pre-defined parameters. We find that, when consumers’ valuation is considered, the manufacturer may prefer to adopt platform or component sharing individually rather than combining them because the performance of high-end products using platform and component sharing strategies is worse than that using two strategies separately. If platform and component sharing are adopted, the high-end product is under designed, but the manufacturer can benefit from economies of scale. When economies of scale of the platform are greater than or equal to that of component sharing, the optimal performance level of low-end products under platform strategy is lower than that under component sharing strategy. Finally, the detailed numerical analysis provides support for the feasibility and effectiveness of the model.

Decomposition is a dominant design strategy because it enables complex problems to be broken up into loosely-coupled modules that are easier to manage and can be designed in parallel. However, contrary to widely held expectations, we show that complexity can increase substantially when natural system modules are fully decoupled from one another to support parallel design. Drawing on detailed empirical evidence from a NASA space robotics field experiment we explain how new information is introduced into the design space through three complexity addition mechanisms of the decomposition process: interface creation, functional allocation, and second order effects. These findings have important implications for how modules are selected early in the design process and how future decomposition approaches should be developed. Although it is well known that complex systems are rarely fully decomposable and that the decoupling process necessitates additional design work, the literature is predominantly focused on reordering, clustering, and/or grouping based approaches to define module boundaries within a fixed system representation. Consequently, these approaches are unable to account for the (often significant) new information that is added to the design space through the decomposition process. We contend that the observed mechanisms of complexity growth need to be better accounted for during the module selection process in order to avoid unexpected downstream costs. With this work we lay a foundation for valuing these complexity-induced impacts to performance, schedule and cost, earlier in the decomposition process.

Many reforms in past decades have come and gone without having the desired impact on teaching practices. Two shared ideals that reforms have had are offering challenging content by using whole task...

Complex networked systems study has encountered a surge in the past years. The problem of characterizing and detecting community structure in networks has given a copious amount of interest. Community detection helps in analyzing and visualizing the overall network’s structure. We propose a new approach Modified Fast Louvain Method (MFLM) to deal with the issue of community detection which can efficiently reveal communities in network. The proposed MFLM approach is a greedy agglomerative technique to uncover the underneath structures of complex networks. A new similarity measure has also been introduced, i.e., Modified Jaccard Similarity Measure (MJSM) to calculate the similarity of the nodes. We demonstrate that our proposed method is an efficient method compared to existing methods. The proposed method unveils better community structures for real world networks. We have used modularity as an objective function to assess the quality of communities identified in the network. A comparative study of MFLM has been performed with respect to an existing PyLouvain and Louvain method. The performance of the proposed MFLM is evaluated in terms of modularity score, number of communities and accuracy.

Proteins evolve through the modular rearrangement of elements known as domains. Extant, multidomain proteins are hypothesized to be the result of domain accretion, but there has been limited experimental validation of this idea. Here, we introduce a technique for genetic minimization by iterative size-exclusion and recombination (MISER) for comprehensively making all possible deletions of a protein. Using MISER, we generate a deletion landscape for the CRISPR protein Cas9. We find that the catalytically-dead Streptococcus pyogenes Cas9 can tolerate large single deletions in the REC2, REC3, HNH, and RuvC domains, while still functioning in vitro and in vivo, and that these deletions can be stacked together to engineer minimal, DNA-binding effector proteins. In total, our results demonstrate that extant proteins retain significant modularity from the accretion process and, as genetic size is a major limitation for viral delivery systems, establish a general technique to improve genome editing and gene therapy-based therapeutics., Proteins evolve through the modular rearrangement of domains. Here the authors introduce MISER, a minimization by iterative size-exclusion and recombination method to make all possible deletions of a protein, uncovering functions for Cas9 domains involved in DNA binding.

International audience; Causal broadcast is a communication abstraction built on top of point-to-point send/receive networks, which ensures that any two messages whose broadcasts are causally related (as captured by Lamport's "happened before" relation) are delivered in their sending order. Several causal broadcast algorithms have been designed for failure-free and crash-prone asynchronous message-passing systems. This article first gives a formal definition of a causal broadcast abstraction in the presence of Byzantine processes, in the form of two equivalent characterizations, and then presents a simple causal broadcast algorithm that implements it. The main difficulty in the design and the proof of this algorithm comes from the very nature of Byzantine faults: Byzantine processes may have arbitrary behavior, and the algorithm must ensure that correct processes (i) maintain a coherent view of causality and (ii) are never prevented from communicating between themselves. To this end, the algorithm is built modularly, namely it works on top of any Byzantine-tolerant reliable broadcast algorithm. Due to this modularity, the proposed algorithm is easy to understand and inherits the computability assumptions (notably the maximal number of processes that may be Byzantine) and the message/time complexities of the underlying reliable broadcast on top of which it is built.

This paper reviews new technologies that make it possible to implement such modern principles of development and creation of applied linear electron accelerators as modularity, miniaturization, and cost reduction. The development of accelerators in this direction became possible due to the emergence of technologies such as compact sources of radio-frequency (RF) power supply and efficient approaches to the fabrication of accelerating structures, as well as an increase in accelerating gradients and a decrease in the power of RF losses in structures. The review is based on the author’s experience in developing compact accelerators.

There are three types of social network users: ordinary users, opinion leaders, and structural hole (SH) spanners. Opinion leaders and SH spanners are key circulators of information. SH sp...