Search

To enable organizations to achieve carbon neutrality through agile capabilities, this paper establishes an integrative framework of carbon-zero agility consisting of three dimensions: search scope agility, search locus agility, and search pace agility. However, applying common agile methodologies like Scrum, Kanban, and Lean to cultivate these capabilities inevitably introduces feedback ambiguity, which can paralyze decision-making and increase errors due to inherent human cognitive limitations. To address this, tailored carbon-zero feedback algorithms are proposed to complement human judgment in agile workflows. Specifically, prescriptive analytics, federated learning, and probabilistic programming are injected into Scrum, Kanban, and Lean respectively to restore clarity amidst ambiguity. The framework is grounded in cases from the textile industry to demonstrate applicability in practical settings. By targeting the roots of distortions with human-algorithm collaborations, it provides an actionable roadmap to implement carbon-zero agility. • Establishes carbon-zero agility framework: search scope, locus, and pace agility. • Identifies behavioral challenges, and feedback ambiguity in agile for carbon-zero. • Proposes tailored carbon-zero feedback algorithms to enhance agility. • Connects agile processes with broader organizational sustainability capabilities. [ABSTRACT FROM AUTHOR]

The Minimum Vertex Cover P roblem is the optimization problem of finding a vertex cover V c of minimal cardinality in a given graph. It is a classic NP-hard problem, and various algorithms have been suggested for it. In this paper, we start with a basic algorithm for solving the problem. Using a probabilistic idea, we use it to develop an improved algorithm. The algorithm is greedy; at each step it adds to the cover a vertex such that the expected cover size, if we continue randomly after this step, is minimal. We study the new algorithm theoretically and empirically, and run simulations to compare its performance to that of some algorithms of a similar nature. [ABSTRACT FROM AUTHOR]

• A novel algorithm is developed to model the aggregates geometrically. • 3D meso-scale concrete model is developed, considering aggregate parameters. • Inclusion of aggregates in mortar, increases the strength of obtained concrete. • In impact loading, mortar and Interfacial transition zone fail simultaneously. • Extent of damage depends on the loading rate. In this paper, a novel algorithm is developed to generate the geometrical model of coarse aggregate based on its physical properties. The size, elongation index, and flakiness index are considered while developing the algorithm. The developed geometrical model of aggregate is further used for generating the finite element (FE) meso-model of concrete. Three distinct phases are considered in the FE model, i.e., aggregate, mortar, and interfacial transition zone. Further, numerical simulation has been performed under different loading conditions (i.e., quasi-static and high-strain loading rate). Additional considerations are discussed to develop a suitable FE model for a 3D split Hopkinson pressure bar specimen. The finite element (FE) simulation results are compared with available literature to verify the developed meso-model. It is verified that the mortar and ITZ fail during the quasi-static loading, whereas the structural effect (lateral inertia confinement) plays a significant role in the high-strain loading rate. [ABSTRACT FROM AUTHOR]