scispace - formally typeset
Search or ask a question
Institution

Aalto University

EducationEspoo, Finland
About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Carbon nanotube. The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.


Papers
More filters
Posted Content
TL;DR: A design science research process (DSRP) model that would meet three objectives: it would be consistent with prior literature, provide a nominal process model for doing DS research, and provide a mental model for presenting and appreciating DS research in IS is designed and demonstrated.
Abstract: The authors design and demonstrate a process for carrying out design science (DS) research in information systems and demonstrate use of the process to conduct research in two case studies. Several IS researchers have pioneered the acceptance of DS research in IS, but in the last 15 years little DS research has been done within the discipline. The lack of a generally accepted process for DS research in IS may have contributed to this problem. We sought to design a design science research process (DSRP) model that would meet three objectives: it would be consistent with prior literature, it would provide a nominal process model for doing DS research, and it would provide a mental model for presenting and appreciating DS research in IS. The process includes six steps: problem identification and motivation, objectives for a solution, design and development, evaluation, and communication. We demonstrated the process by using it in this study and by presenting two case studies, one in IS planning to develop application ideas for mobile financial services and another in requirements engineering to specify feature requirements for a self service advertising design and sales system intended for wide audience end users. The process effectively satisfies the three objectives and has the potential to help aid the acceptance of DS research in the IS discipline.

422 citations

Journal ArticleDOI
TL;DR: In this paper, the authors propose a value-system continuum that forms the basis for classifying different types of strategic business nets and discuss the core managerial questions and capabilities required in management in strategic nets.

421 citations

Journal ArticleDOI
TL;DR: In this paper, the optical properties of transition metal dichalcogenide (TMD) bilayer heterostructures consisting of MoS${}_{2}$ layers sandwiched with WS${}{2}$, MoSe${}µ, MoTe${} µ, BN, or graphene sheets were investigated.
Abstract: We calculate from first principles the electronic structure and optical properties of a number of transition metal dichalcogenide (TMD) bilayer heterostructures consisting of MoS${}_{2}$ layers sandwiched with WS${}_{2}$, MoSe${}_{2}$, MoTe${}_{2}$, BN, or graphene sheets. Contrary to previous works, the systems are constructed in such a way that the unstrained lattice constants of the constituent incommensurate monolayers are retained. We find strong interaction between the $\ensuremath{\Gamma}$-point states in all TMD/TMD heterostructures, which can lead to an indirect gap. On the other hand, states near the $K$ point remain as in the monolayers. When TMDs are paired with BN or graphene layers, the interaction around the $\ensuremath{\Gamma}$-point is negligible, and the electronic structure resembles that of two independent monolayers. Calculations of optical properties of the MoS${}_{2}$/WS${}_{2}$ system show that, even when the valence- and conduction-band edges are located in different layers, the mixing of optical transitions is minimal, and the optical characteristics of the monolayers are largely retained in these heterostructures. The intensity of interlayer transitions is found to be negligibly small, a discouraging result for engineering the optical gap of TMDs by heterostructuring.

417 citations

Book ChapterDOI
17 Oct 2014
TL;DR: In this paper, a new bio-inspired algorithm, chicken swarm optimization (CSO), is proposed for optimization applications, which mimics the hierarchal order in the chicken swarm and the behaviors of the chicken swarms, including roosters, hens and chicks.
Abstract: A new bio-inspired algorithm, Chicken Swarm Optimization (CSO), is proposed for optimization applications. Mimicking the hierarchal order in the chicken swarm and the behaviors of the chicken swarm, including roosters, hens and chicks, CSO can efficiently extract the chickens’ swarm intelligence to optimize problems. Experiments on twelve benchmark problems and a speed reducer design were conducted to compare the performance of CSO with that of other algorithms. The results show that CSO can achieve good optimization results in terms of both optimization accuracy and robustness. Future researches about CSO are finally suggested.

417 citations

Journal ArticleDOI
TL;DR: This paper proposes a common evaluation framework for automatic stroke lesion segmentation from MRIP, describes the publicly available datasets, and presents the results of the two sub‐challenges: Sub‐Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES).

417 citations


Authors

Showing all 10135 results

NameH-indexPapersCitations
John B. Goodenough1511064113741
Ashok Kumar1515654164086
Anne Lähteenmäki11648581977
Kalyanmoy Deb112713122802
Riitta Hari11149143873
Robin I. M. Dunbar11158647498
Andreas Richter11076948262
Mika Sillanpää96101944260
Muhammad Farooq92134137533
Ivo Babuška9037641465
Merja Penttilä8730322351
Andries Meijerink8742629335
T. Poutanen8612033158
Sajal K. Das85112429785
Kalle Lyytinen8442627708
Network Information
Related Institutions (5)
Georgia Institute of Technology
119K papers, 4.6M citations

95% related

Delft University of Technology
94.4K papers, 2.7M citations

94% related

École Polytechnique Fédérale de Lausanne
98.2K papers, 4.3M citations

94% related

Nanyang Technological University
112.8K papers, 3.2M citations

94% related

ETH Zurich
122.4K papers, 5.1M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023101
2022342
20212,842
20203,030
20192,749
20182,719