Module Partition Research Articles

Module partition for mechanical CAD assembly model based on multi-source correlation information and community detection

Module partition for mechanical CAD assembly model based on multi-source correlation information and community detection

Modular design method of EOD robot based on genetic algorithm

With the rapid development of product design, the modular design method shows unique advantages in the design of the product. Modular design has become a hot spot in the manufacturing and design industry. The aim of the modular design method is to make up the products which include many types and specifications with fewer types and quantities of modules. In this paper, EOD robot design is the research object according to the module division principle, from the point of view of the EOD robot products is divided into several basic units by clustering algorithm, genetic algorithm, qualitative and quantitative module clustering and the application of genetic algorithm to solve the problem of modular design. By analysing the design example of modular EOD robot, the feasibility and effectiveness of the module partition method are verified.

Influence of Si on sigma phase precipitation and pitting corrosion in superaustenitic stainless steel weld metal

In this study, we investigated the effect of Si on the precipitation of the sigma phase in super austenitic stainless steel welds. The Scheil module solidification, partition coefficient and activity were calculated using the thermo-calc software to study the thermodynamics of the precipitation behavior of the sigma phase during solidification of the weld metal. The fraction and average size of the sigma phases significantly increased with increasing Si content in the welds, which consisted of γ-dendrite and inter-dendritic sigma phases. Scheil solidification showed that the formation temperature range of the sigma phase increased with increasing Si content. Also, partition coefficient of Cr and especially Mo and Si substantially decreased under the L-γ solidification with addition of Si. And it resulted in increase of Cr, Mo and Si contents in the sigma phases. The activity of Mo rose sharply with increasing Si, which caused an increase in precipitation due to the fundamental elements of the sigma phase. Corrosion test results showed that pitting was sensitive to an increase in the Si assisted sigma phase precipitation.

Evaluation Approach to Rank Module Partition Schemes of Complex Products Based on Hybrid Fuzzy Multi-Attribute Decision Making

Evaluation Approach to Rank Module Partition Schemes of Complex Products Based on Hybrid Fuzzy Multi-Attribute Decision Making

Module partition of complex mechanical products based on weighted complex networks

It is tough to build an effective mathematical model to describe the complicated relationships in complex mechanical products, which leads to the module partition of complex mechanical products and the guarantee of accurate results become more difficult. In addition, the module partition method cannot bring about a satisfactory module partition result if the scale of the products is huge and complicated. In this case, complex network theory is used to solve these problems in this paper. Firstly, a weighted complex network is established to systematically express the structure of complex mechanical products. In particular, customer demands are taken into account for module partition by introducing customer involvement. Secondly, the interval-valued intuitionistic fuzzy sets are used to calculate the relationships between parts for reducing the subjectivity of the calculation process. Afterwards, a modified GN algorithm (community detection algorithm) is proposed to achieve the module partition of complex mechanical products. Finally, the module partition of a wind turbine is carried out to verify the effectiveness of the proposed method in this paper. The result of the case study shows that the modified GN algorithm achieves better module partition performance than the classical GN algorithm and fuzzy clustering analysis method, which obtains a satisfactory result for applications.

Functional modularity in lake-dwelling characin fishes of Mexico.

Modular evolution promotes evolutionary change, allowing independent variation across morphological units. Recent studies have shown that under contrasting ecological pressures, patterns of modularity could be related to divergent evolution. The main goal of the present study was to evaluate the presence of modular evolution in two sister lacustrine species, Astyanax aeneus and A. caballeroi, which are differentiated by their trophic habits. Two different datasets were analyzed: (1) skull X-rays from 73 specimens (35 A. aeneus and 38 A. caballeroi) to characterize skull variation patterns, considering both species and sex effects. For this dataset, three different modularity hypotheses were tested, previously supported in other lacustrine divergent species; (2) a complete body shape dataset was also tested for four modularity hypotheses, which included a total of 196 individuals (110 Astyanax aeneus and 86 A. caballeroi). Skull shape showed significant differences among species and sex (P < 0.001), where Astyanax caballeroi species showed an upwardly projected mandible and larger preorbital region. For the skull dataset, the modularity hypothesis ranked first included three partitioning modules. While for the complete body dataset the best ranked hypothesis included two modules (head vs the rest of the body), being significant only for A. caballeroi.

BFO-FMD: bacterial foraging optimization for functional module detection in protein–protein interaction networks

Identifying functional modules in PPI networks contributes greatly to the understanding of cellular functions and mechanisms. Recently, the swarm intelligence-based approaches have become effective ways for detecting functional modules in PPI networks. This paper presents a new computational approach based on bacterial foraging optimization for functional module detection in PPI networks (called BFO-FMD). In BFO-FMD, each bacterium represents a candidate module partition encoded as a directed graph, which is first initialized by a random-walk behavior according to the topological and functional information between protein nodes. Then, BFO-FMD utilizes four principal biological mechanisms, chemotaxis, conjugation, reproduction, and elimination and dispersal to search for better protein module partitions. To verify the performance of BFO-FMD, we compared it with several other typical methods on three common yeast datasets. The experimental results demonstrate the excellent performances of BFO-FMD in terms of various evaluation metrics. BFO-FMD achieves outstanding Recall, F-measure, and PPV while performing very well in terms of other metrics. Thus, it can accurately predict protein modules and help biologists to find some novel biological insights.

Structures of partition protein ParA with nonspecific DNA and ParB effector reveal molecular insights into principles governing Walker-box DNA segregation.

Walker-box partition systems are ubiquitous in nature and mediate the segregation of bacterial and archaeal DNA. Well-studied plasmid Walker-box partition modules require ParA, centromere-DNA, and a centromere-binding protein, ParB. In these systems, ParA-ATP binds nucleoid DNA and uses it as a substratum to deliver ParB-attached cargo DNA, and ParB drives ParA dynamics, allowing ParA progression along the nucleoid. How ParA-ATP binds nonspecific DNA and is regulated by ParB is unclear. Also under debate is whether ParA polymerizes on DNA to mediate segregation. Here we describe structures of key ParA segregation complexes. The ParA-β,γ-imidoadenosine 5'-triphosphate (AMPPNP)-DNA structure revealed no polymers. Instead, ParA-AMPPNP dimerization creates a multifaceted DNA-binding surface, allowing it to preferentially bind high-density DNA regions (HDRs). DNA-bound ParA-AMPPNP adopts a dimer conformation distinct from the ATP sandwich dimer, optimized for DNA association. Our ParA-AMPPNP-ParB structure reveals that ParB binds at the ParA dimer interface, stabilizing the ATPase-competent ATP sandwich dimer, ultimately driving ParA DNA dissociation. Thus, the data indicate how harnessing a conformationally adaptive dimer can drive large-scale cargo movement without the requirement for polymers and suggest a segregation mechanism by which ParA-ATP dimers equilibrate to HDRs shown to be localized near cell poles of dividing chromosomes, thus mediating equipartition of attached ParB-DNA substrates.

Role-based approaches for operational tasks modeling and flexible decomposition

In order to realize the fact that operational tasks can be decomposed flexibly with new tasks random adding, role-based approaches for operational tasks model and the flexibility decomposition are proposed. Firstly, aiming at the shortcomings of previous task decomposition methods, operational role concepts are introduced to design a computing framework of task flexibility decomposition. Within the framework, task structure matrixes are constructed for tasks decomposition with the mapping relations of information interaction and action process among tasks. On basis of this, via giving operational decomposition granularity, a flexibility approach of operational task decomposition is put forward with three parts about task decomposition granularity adjustment, tasks module partition with different operational role patterns and task decomposition scheme evaluation. Finally, as an application case shown, it is validated that the proposed approaches for tasks modeling and decomposition are available. By comparing with previous decomposition approaches, the proposed approach, within three types of operational roles, has strong environmental adaptability and scheme diversity, which can restrain calculation complexity increment for realizing task flexible decomposition effectively.

A new module partition method based on the criterion and noise functions of robust design

Against the sweeping trend of mass customization, the importance of product platform design is becoming increasingly recognized by the manufacturers. Module design is the foundation of product platform design, and module partition determines the effectiveness of module design. Traditionally, the vast majority of existing module partition methods ignored the design factor of customer preferences. This study proposes to employ the basic principles of robust design to make the module partition schemes less sensitive to the dynamically changing customer preferences by considering them as a noise factor. A criterion function and a noise function are each established based on the component-component correlation matrix and component-function contribution matrix, respectively. The criterion and noise functions, when combined, lead to a unique multi-objective optimization problem. Furthermore, an improved Pareto archive particle swarm optimization (PAPSO) algorithm is introduced to solve the multi-objective optimization problem in order to prevent the premature selections of non-optimal solutions. A case study is presented to showcase how the proposed new method is followed to conduct the module partition on an electric-traction drum shearer. The improved algorithm demonstrates highly competitive performance in comparison to the existing multi-objective optimization algorithms.

Function Module Partition for Complex Products and Systems Based on Weighted and Directed Complex Networks

Modular design is an effective approach to shorten lead-time and reduce cost for development of complex products and systems (CoPS). Because the physical details of the product are not available at the conceptual design stage, considerations in the downstream product development phases such as manufacturing and assembly cannot be used for partition of modules at the conceptual design stage. Since design solution at the conceptual design stage can be modeled by functions and relationships among these functions such as function flows including information flows, material flows, and energy flows, a novel approach is introduced in this research for function module partition of CoPS through community detection using weighted and directed complex networks (WDCN). First, the function structure is obtained and mapped into a weighted and directed complex network. Based on the similarity between behaviors of communities in WDCN and behaviors of modules in CoPS, a LinkRank-based community detection approach is employed for function module partition through optimization with simulated annealing. The function module partition for the power mechanism in a large tonnage crawler crane is conducted as a case study to demonstrate the effectiveness of the developed approach.

Simulating potential growth in a relay-strip intercropping system: Model description, calibration and testing

Intercropping tends to have a higher productivity than traditional sole crops, mainly due to complementary resource use in time and space among different species. Intercropping may become more important in a world that needs to produce 60–70% more food by 2050 with limited land and other agricultural resources. To assess the role of intercropping in agricultural systems and its contribution to future food security, an intercrop model is needed for growth and yield predictions of intercrops under different growing conditions. Strip intercropping is a prevalent intercropping system, but the existing intercrop models are generally built for full mixtures and are less suitable for strip intercrops. Here we describe a simple intercrop model which is developed based on a sole crop model using the radiation use efficiency (RUE) concept and a strip intercrop light partitioning module. The model allows simulating the growth and yield of each intercropped species in relay-strip intercropping under potential growing conditions (only competition for light; other resources are assumed to be non-limiting), and the intercrop could vary in species combination, planting configuration, sowing densities and sowing dates. The daily inputs of the model are temperature and radiation, and crop-specific parameters are required to simulate crop leaf area index (LAI), biomass and final yield. Data collected during two years (2013 and 2014) field experiments were used to calibrate and test the model. The experiments consisted of two sole crop treatments (sole wheat, SW and sole maize, SM) and three intercrop treatments (replacement intercrop, 6:2WM and add-row intercrops, 8:2WM and 6:3WM). The experiments were conducted in Wageningen, the Netherlands. Data of sole crops (SW and SM) and replacement intercrop (6:2WM) treatment were used to calibrate the model, and data of add-row intercrops (8:2WM and 6:3WM) were used to test the model. Bayesian analysis was applied to calibrate RUE of wheat and maize in sole crops and intercrop. This calibration procedure resulted in posterior distributions of RUE for sole crops and intercrop, on the basis of which distributions of biomass and land equivalent ratio (LER) were simulated. Biomass accumulation and yield of each species were simulated adequately but LAI was slightly overestimated compared to observations. The intercrop model allows simulating the contribution of border row effects to the productivity of intercrops. It combines a simple structure with easy calibration and enables growth and yield simulations for a wide range of relay-strip intercrops. The model thus can be of value in exploratory land use studies to assess the role of intercropping.

Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton

Association network approaches have recently been proposed as a means for exploring the associations between bacterial communities. In the present study, high-throughput sequencing was employed to investigate the seasonal variations in the composition of bacterioplankton communities in six eutrophic urban lakes of Nanjing City, China. Over 150,000 16S rRNA sequences were derived from 52 water samples, and correlation-based network analyses were conducted. Our results demonstrated that the architecture of the co-occurrence networks varied in different seasons. Cyanobacteria played various roles in the ecological networks during different seasons. Co-occurrence patterns revealed that members of Cyanobacteria shared a very similar niche and they had weak positive correlations with other phyla in summer. To explore the effect of environmental factors on species-species co-occurrence networks and to determine the most influential environmental factors, the original positive network was simplified by module partitioning and by calculating module eigengenes. Module eigengene analysis indicated that temperature only affected some Cyanobacteria; the rest were mainly affected by nitrogen associated factors throughout the year. Cyanobacteria were dominant in summer which may result from strong co-occurrence patterns and suitable living conditions. Overall, this study has improved our understanding of the roles of Cyanobacteria and other bacterioplankton in ecological networks.

Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept

Optimum-path forest (OPF) is a graph-based machine learning method that can overcome some limitations of the traditional machine learning algorithms that have been used in intrusion detection systems. This paper presents a novel approach for intrusion detection using a modified OPF (MOPF) algorithm for improving the performance of traditional OPF in terms of detection rate (DR), false alarm rate (FAR), and time of execution. To address the problem of scalability in large datasets and also for achieving high attack recognition rates, the proposed framework employs the k-means clustering algorithm, as a partitioning module, for generating different homogeneous training subsets from original heterogeneous training samples. In the proposed MOPF algorithm, the distance between unlabeled samples and the root (prototype) of every sample in OPF is also considered in classifying unlabeled samples with the aim of improving the accuracy rate of traditional OPF algorithm. Moreover, the centrality and the prestige concepts in the social network analysis are employed in a pruning module for determining the most informative samples in training subsets to speed up the traditional OPF algorithm. The experimental results on NSL-KDD dataset show that the proposed method performs better than traditional OPF in terms of accuracy rate, DR, FAR, and cost per example (CPE) evaluation metrics.

Model–measurement comparison of functional group abundance in &amp;lt;i&amp;gt;α&amp;lt;/i&amp;gt;-pinene and 1,3,5-trimethylbenzene secondary organic aerosol formation

Abstract. Secondary organic aerosol (SOA) formed by α-pinene and 1,3,5-trimethylbenzene photooxidation under different NOx regimes is simulated using the Master Chemical Mechanism v3.2 (MCM) coupled with an absorptive gas–particle partitioning module. Vapor pressures for individual compounds are estimated with the SIMPOL.1 group contribution model for determining apportionment of reaction products to each phase. We apply chemoinformatic tools to harvest functional group (FG) composition from the simulations and estimate their contributions to the overall oxygen to carbon ratio. Furthermore, we compare FG abundances in simulated SOA to measurements of FGs reported in previous chamber studies using Fourier transform infrared spectroscopy. These simulations qualitatively capture the dynamics of FG composition of SOA formed from both α-pinene and 1,3,5-trimethylbenzene in low-NOx conditions, especially in the first hours after start of photooxidation. Higher discrepancies are found after several hours of simulation; the nature of these discrepancies indicates sources of uncertainty or types of reactions in the condensed or gas phase missing from current model implementation. Higher discrepancies are found in the case of α-pinene photooxidation under different NOx concentration regimes, which are reasoned through the domination by a few polyfunctional compounds that disproportionately impact the simulated FG abundance in the aerosol phase. This manuscript illustrates the usefulness of FG analysis to complement existing methods for model–measurement evaluation.

An overview of interval-valued intuitionistic fuzzy information aggregations and applications

Interval-valued intuitionistic fuzzy set, generalized by Atanassov and Gargov, can be used to characterize the uncertain information more sufficiently and accurately when we face the fact that the values of the membership function and the non-membership function in an intuitionistic fuzzy set are difficult to be expressed as exact real numbers in many real-world decision-making problems. In this paper, we provide an overview of interval-valued intuitionistic fuzzy information aggregation techniques, and their applications in various fields such as decision-making, entropy measure, supplier selection and some practical decision-making problems. Meanwhile, we also review some important methods for decision-making with interval-valued intuitionistic fuzzy information, including the QUALIFLEX-based method, the TOPSIS method, the extended VIKOR method, the module partition schemes evaluation (MPSE) approach, the outranking choice method, the inclusion-based LINMAP method and the risk attitudinal ranking method, the evidential reasoning methodology, etc. Finally, we point out some possible directions for future research.

Fast coding unit selection and motion estimation algorithm based on early detection of zero block quantified transform coefficients for high‐efficiency video coding standard

High-efficiency video coding (HEVC) is the newest video coding standard developed by the joint video team, consisting of ITU-T video coding experts group and ISO/IEC Moving Picture Experts Group. The HEVC standard has aggregated an exhaustive algorithm for mode decision based on a recursive quad-tree structured coding tree block. Moreover, several specific features have been incorporated into the motion estimation (ME) process to improve its coding efficiency. However, they resulted in very high computational complexity. To accelerate the encoding process, fast mode decision algorithms for the partitioning module and also for the ME module were proposed in this study. These algorithms are based on early zero block detection technique. To improve the efficiency of these algorithms, an overall algorithm which combines the two techniques has been implemented. The performance of the proposed algorithm was checked through a comparative analysis in terms of encoding time and compression rate. Compared to HEVC test model 10.0, the authors’ proposed algorithms bring a great reduction of the HEVC complexity encoder with a saving time, which can reach 25% in average for different tested videos and a slight coding loss in terms of image quality and compression rate.

A bottom-up search algorithm for size-constrained partitioning of modules to generate configurations in modular robots

A bottom-up search algorithm for size-constrained partitioning of modules to generate configurations in modular robots

Modulation of partition and localization of perfume molecules in sodium dodecyl sulfate micelles.

The influence of perfume molecules on the self-assembly of the anionic surfactant sodium dodecyl sulfate (SDS) and their localization in SDS micelles have been investigated by ζ potential, small angle X-ray scattering (SAXS), one- and two-dimensional NMR and isothermal titration microcalorimetry (ITC). A broad range of perfume molecules varying in octanol/water partition coefficients P are employed. The results indicate that the surface charge, size and aggregation number of the SDS micelles strongly depend on the hydrophobicity/hydrophilicity degree of perfume molecules. Three distinct regions along the log P values are identified. Hydrophilic perfumes (log P < 2.0) partially incorporate into the SDS micelles and do not lead to micelle swelling, whereas hydrophobic perfumes (log P > 3.5) are solubilized close to the end of the hydrophobic chains in the SDS micelles and enlarge the micelles with higher ζ potential and a larger aggregation number. The incorporated fraction and micelle properties show increasing tendency for the perfumes in the intermediate log P region (2.0 < log P < 3.5). Besides, the molecular conformation of perfume molecules also affects these properties. The perfumes with a linear chain structure or an aromatic group can penetrate into the palisade layer and closely pack with the SDS molecules. Furthermore, the thermodynamic parameters obtained from ITC show that the binding of the perfumes in the intermediate log P region is more spontaneous than those in the other two log P regions, and the micellization of SDS with the perfumes is driven by entropy.

Modularizing product extension services: An approach based on modified service blueprint and fuzzy graph

This study aims at modularizing product-extension service (PES). PES is high-value service solution (e.g., repair, maintenance, and energy management) based on product to help manufacturers to achieve sustainable growth and profitability. Modularization can help to realize customization, increase in flexibility, reusability of service modules, and simplification of complex PES systems, etc. However, compared with tangible product, the modularization of product-extension service has rarely been dealt with, despite its potential benefits. More specifically, the question of how to identify service components and partition modules in the practical context is still an unexplored subject. Thus, this paper proposes a PES modularization approach based on modified service blueprint and fuzzy graph. The PES blueprint is firstly used to represent the whole PES scenario and identify all relevant service components. Then, fuzzy graph theory is utilized to module partition based on correlation analysis results of the service components. Finally, an exemplified modular design of compressor rotor service demonstrates the potential of the proposed method.