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Correction: Bottom-Up Engineering of Biological Systems through Standard Bricks: A Modularity Study on Basic Parts and Devices
There is an error in Text S1.
Download corrected item. https://doi.org/10.1371/annotation/91e7d3a1-2f50-4f84-8b12-2c21f88438c3.s001.cn Citation: Pasotti L, Politi N, Zucca S, Cusella De Angelis MG, Magni P (2012) Correction: Bottom-Up Engineering of Biological Systems through Standard Bricks: A Modularity Study on Basic Parts and Devices.
Journal Article
Modularization strategy: analysis of published articles on production and operations management (1999 to 2013)
The objective of this study was to analyze the main topics studied on the subject of modularization in 81 academic articles published between 1999 and 2013 in international and Brazilian journals that include research related to production and operations management. This analysis proposes a general framework of studies that serves as support for future research on modularization strategy. To fulfill the proposed objective, a systematic literature review was conducted using 10 international academic journals and two Brazilian academic journals that publish studies on the study topic. The main results include identifying the lack of studies on the background that leads to modularization, the lack of empirical and quantitative studies on its effects, and the use of modularization in the organizational, service, and environmental contexts. The present study also organizes modularization studies in the proposed conceptual structure and classifies the articles analyzed into a specific modularization taxonomy and in terms of the study’s objective.
Journal Article
CONVEXIFIED MODULARITY MAXIMIZATION FOR DEGREE-CORRECTED STOCHASTIC BLOCK MODELS
The stochastic block model (SBM), a popular framework for studying community detection in networks, is limited by the assumption that all nodes in the same community are statistically equivalent and have equal expected degrees. The degree-corrected stochastic block model (DCSBM) is a natural extension of SBM that allows for degree heterogeneity within communities. To find the communities under DCSBM, this paper proposes a convexified modularity maximization approach, which is based on a convex programming relaxation of the classical (generalized) modularity maximization formulation, followed by a novel doubly-weighted ℓ₁-norm k-medoids procedure. We establish nonasymptotic theoretical guarantees for approximate and perfect clustering, both of which build on a new degree-corrected density gap condition. Our approximate clustering results are insensitive to the minimum degree, and hold even in sparse regime with bounded average degrees. In the special case of SBM, our theoretical guarantees match the best-known results of computationally feasible algorithms. Numerically, we provide an efficient implementation of our algorithm, which is applied to both synthetic and realworld networks. Experiment results show that our method enjoys competitive performance compared to the state of the art in the literature.
Journal Article
Exploring digital servitization trajectories within product–service–software space
PurposeThis study shows various pathways manufacturers can take when embarking on digital servitization (DS) journeys. It builds on the DS and modularity literature to map the strategic trajectories of product–service–software (PSSw) configurations.Design/methodology/approachThe study is exploratory and based on the inductive theory building method. The empirical data were gathered through a workshop with focus groups of 15 servitization manufacturers (with 22 respondents), an on-site workshop (in-depth case study), semi-structured interviews, observations and document study of archival data.FindingsThe DS trajectories are idiosyncratic and dependent on design architectures of PSSw modules, balancing choices between standardization and innovation. The adoption of software systems depends on the maturity of the industry-specific digital ecosystem. Decomposition and integration of PSSw modules facilitate DS transition through business model modularity. Seven testable propositions are presented.Research limitations/implicationsWith the small sample size from different industries and one in-depth case study, generalizing the findings was not possible.Practical implicationsThe mapping exercise is powerful when top management from different functional departments can participate together to share their expertise and achieve consensus. It logs the “states” that the manufacturer undergoes over time.Originality/valueThe Digital Servitization Cube serves as a conceptual framework for manufacturers to systematically map and categorize their current and future PSSw strategies. It bridges the cross-disciplinary theoretical discussion in DS.
Journal Article
Modularity and evolution of flower shape
• Flowers have been hypothesized to contain either modules of attraction and reproduction, functional modules (pollination-effecting parts) or developmental modules (organ-specific). Do pollination specialization and syndromes influence floral modularity?
• In order to test these hypotheses and answer this question, we focused on the genus Erica: we gathered 3D data from flowers of 19 species with diverse syndromes via computed tomography, and for the first time tested the above-mentioned hypotheses via 3D geometric morphometrics. To provide an evolutionary framework for our results, we tested the evolutionary mode of floral shape, size and integration under the syndromes regime, and – for the first time – reconstructed the high-dimensional floral shape of their most recent common ancestor.
• We demonstrate that the modularity of the 3D shape of generalist flowers depends on development and that of specialists is linked to function: modules of pollen deposition and receipt in bird syndrome, and access-restriction to the floral reward in long-proboscid fly syndrome. Only size and shape principal component 1 showed multiple-optima selection, suggesting that they were co-opted during evolution to adapt flowers to novel pollinators. Whole floral shape followed an Ornstein–Uhlenbeck (selection-driven) evolutionary model, and differentiated relatively late.
• Flower shape modularity thus crucially depends on pollinator specialization and syndrome.
Journal Article
Definitions, methods, and applications in interpretable machine learning
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.
Journal Article
Community detection with Greedy Modularity disassembly strategy
Community detection recognizes groups of densely connected nodes across networks, one of the fundamental procedures in network analysis. This research boosts the standard but locally optimized Greedy Modularity algorithm for community detection. We introduce innovative exploration techniques that include a variety of node and community disassembly strategies. These strategies include methods like non-triad creating, feeble, random as well as inadequate embeddedness for nodes, as well as low internal edge density, low triad participation ratio, weak, low conductance as well as random tactics for communities. We present a methodology that showcases the improvement in modularity across the wide variety of real-world and synthetic networks over the standard approaches. A detailed comparison against other well-known community detection algorithms further illustrates the better performance of our improved method. This study not only optimizes the process of community detection but also broadens the scope for a more nuanced and effective network analysis that may pave the way for more insights as to the dynamism and structures of its functioning by effectively addressing and overcoming the limitations that are inherently attached with the existing community detection algorithms.
Journal Article
Towards a theory of ecosystems
Research summary: The recent surge of interest in \"ecosystems\" in strategy research and practice has mainly focused on what ecosystems are and how they operate. We complement this literature by considering when and why ecosystems emerge, and what makes them distinct from other governance forms. We argue that modularity enables ecosystem emergence as it allows a set of distinct yet interdependent organizations to coordinate without full hierarchical fiat. We show how ecosystems address multilateral dependences based on various types of complementarities—supermodular or unique, unidirectional or bidirectional—which determine the ecosystem's value-add. We argue that at the core of ecosystems lie nongeneric complementarities, and the creation of sets of roles that face similar rules. We conclude with implications for mainstream strategy and suggestions for future research. Managerial summary: We consider what makes ecosystems different from other business constellations, including markets, alliances, or hierarchically managed supply chains. Ecosystems, we posit, are interacting organizations, enabled by modularity, not hierarchically managed, bound together by the nonredeployability of their collective investment elsewhere. Ecosystems add value as they allow managers to coordinate their multilateral dependence through sets of roles that face similar rules, thus obviating the need to enter into customized contractual agreements with each partner. We explain how different types of complementarities (unique or supermodular, generic or specific, uni- or bi-directional) shape ecosystems and offer a \"theory of ecosystems\" that can explain what they are, when they emerge, and why alignment occurs. Finally, we outline the critical factors affecting ecosystem emergence, evolution, and success—or failure.
Journal Article