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3,214 Works

bit-by-bit

Michael Schiltz
Bit-by-bit: Search Strategies, Resource Organization, Management & Sustainability; The Creation of Knowledge in the Digital Era

bit-by-bit

Michael Schiltz
Bit-by-bit: Search Strategies, Resource Organization, Management & Sustainability; The Creation of Knowledge in the Digital Era

mstack_0.1.tar.gz

Peter Metz
In order to refine stacking disorder models in real and reciprocal space, MSTACK has been written to extend two established profile generators: DIFFaX, a reciprocal space intensity distribution calculator built on a stochastic stacking disorder model description; and DiffPy-CMI, a suite of tools including pair distribution function calculators. MSTACK includes tools to expand the stochastic stacking model parameters typical of DIFFaX into supercell models suitable for calculation of stacking disordered pair distribution function data, and...

E-Z Drift Diffusion Model script

Jessica Aylward & Oliver Robinson
This is the E-Z DDM script for 'Back-translating a rodent measure of negative bias into humans: the impact of induced anxiety and unmedicated mood and anxiety disorders'

Cluster Computing Joruanl: "FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method"

Mohammad Shojafar, Saeed Javanmardi, Saeid Abolfazli & Nicola Cordeschi
Job scheduling is one of the most important research problems in distributed systems, particularly cloud environments/computing. The dynamic and heterogeneous nature of resources in such distributed systems makes optimum job scheduling a non-trivial task. Maximal resource utilization in cloud computing demands/necessitates an algorithm that allocates resources to jobs with optimal execution time and cost. The critical issue for job scheduling is assigning jobs to the most suitable resources, considering user preferences and requirements. In this...

Cluster Computing Joruanl: "FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method"

Mohammad Shojafar, Saeed Javanmardi, Saeid Abolfazli & Nicola Cordeschi
Job scheduling is one of the most important research problems in distributed systems, particularly cloud environments/computing. The dynamic and heterogeneous nature of resources in such distributed systems makes optimum job scheduling a non-trivial task. Maximal resource utilization in cloud computing demands/necessitates an algorithm that allocates resources to jobs with optimal execution time and cost. The critical issue for job scheduling is assigning jobs to the most suitable resources, considering user preferences and requirements. In this...

IEEE Transaction on Cloud Computing (TCC)-"Adaptive Computing-plus-Communication Optimization Framework for Multimedia Processing in Cloud Systems"

Mohammad Shojafar, Claudia Canali, Riccardo Lancellotti & Jemal Abawajy
A clear trend in the evolution of network-based services is the ever-increasing amount of multimedia data involved. This trend towards big-data multimedia processing finds its natural placement together with the adoption of the cloud computing paradigm, that seems the best solution to cope with the demands of a highly fluctuating workload that characterizes this type of services. However, as cloud data centers become more and more powerful, energy consumption becomes a major challenge both for...

IEEE Transaction on Cloud Computing (TCC)-"Adaptive Computing-plus-Communication Optimization Framework for Multimedia Processing in Cloud Systems"

Mohammad Shojafar, Claudia Canali, Riccardo Lancellotti & Jemal Abawajy
A clear trend in the evolution of network-based services is the ever-increasing amount of multimedia data involved. This trend towards big-data multimedia processing finds its natural placement together with the adoption of the cloud computing paradigm, that seems the best solution to cope with the demands of a highly fluctuating workload that characterizes this type of services. However, as cloud data centers become more and more powerful, energy consumption becomes a major challenge both for...

IEEE Transactions on Cloud Computing (TCC)(Energy-efficient Adaptive Resource Management for Real-time Vehicular Cloud Services)

Mohammad Shojafar, Nicola Cordeschi & Enzo Baccarelli
Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of...

IEEE Transactions on Cloud Computing (TCC)(Energy-efficient Adaptive Resource Management for Real-time Vehicular Cloud Services)

Mohammad Shojafar, Nicola Cordeschi & Enzo Baccarelli
Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of...

SUPE2016_P-SEP_Sourcecode.zip

Mohammad.Shojafar@Uniroma1.It, Paola.Vinueza@Uniroma1.It, Enzo.Baccarelli@Uniroma1.It, Zahra.Pooranian@Uniroma1.It & Habib.Mostafaei@Uniroma3.It
Energy efficiency is one of the main issues that will drive the design of fog-supported wireless sensor networks (WSNs). Indeed, the behavior of such networks becomes very unstable in node’s heterogeneity and/or node’s failure. In WSNs, clusters are dynamically built up by neighbor nodes, to save energy and prolong the network lifetime. One of the nodes plays the role of Cluster Head (CH) that is responsible for transferring data among the neighboring sensors. Due to...

SUPE2016_P-SEP_Sourcecode.zip

Mohammad.Shojafar@Uniroma1.It, Paola.Vinueza@Uniroma1.It, Enzo.Baccarelli@Uniroma1.It, Zahra.Pooranian@Uniroma1.It & Habib.Mostafaei@Uniroma3.It
Energy efficiency is one of the main issues that will drive the design of fog-supported wireless sensor networks (WSNs). Indeed, the behavior of such networks becomes very unstable in node’s heterogeneity and/or node’s failure. In WSNs, clusters are dynamically built up by neighbor nodes, to save energy and prolong the network lifetime. One of the nodes plays the role of Cluster Head (CH) that is responsible for transferring data among the neighboring sensors. Due to...

Data and Programs for Ma and Stern (2016) Resource and Energy Economics

David Stern & Chunbo.Ma@Uwa.Edu.Au
Excel data files and RATS programs for main estimates reported in Ma and Stern (2016). Spreadsheets show how data was processed into the form used in the RATS programs. In each case the worksheet "RATS" has the processed data in the form used.

Data and Programs for Ma and Stern (2016) Resource and Energy Economics

David Stern & Chunbo.Ma@Uwa.Edu.Au
Excel data files and RATS programs for main estimates reported in Ma and Stern (2016). Spreadsheets show how data was processed into the form used in the RATS programs. In each case the worksheet "RATS" has the processed data in the form used.

mstack_0.1.tar.gz

Peter Metz
In order to refine stacking disorder models in real and reciprocal space, MSTACK has been written to extend two established profile generators: DIFFaX, a reciprocal space intensity distribution calculator built on a stochastic stacking disorder model description; and DiffPy-CMI, a suite of tools including pair distribution function calculators. MSTACK includes tools to expand the stochastic stacking model parameters typical of DIFFaX into supercell models suitable for calculation of stacking disordered pair distribution function data, and...

A critical reexamination of doing arithmetic nonconsciously: Supplementary Materials

Pieter Moors & Guido Hesselmann
This html file contains the code used and supplementary analyses reported in:
Moors, P., Hesselmann, G. (2017). A critical reexamination of doing arithmetic nonconsciously. Psychonomic Bulletin & Review.

Trebuchet Optimization Code.zip

Jared Park
We performed an optimization on the design of a counterweight trebuchet with a fixed “footprint,” or projected area. For this project, we used research performed by others, re-deriving the equations of motion ourselves, to find the best design for throwing a set projectile the greatest distance. Design variables were short arm length, sling length, weight rope length, swing clearance, starting angle and release angle. The constraints were the footprint, the height of the pivot, and...

Trebuchet Optimization Code.zip

Jared Park
We performed an optimization on the design of a counterweight trebuchet with a fixed “footprint,” or projected area. For this project, we used research performed by others, re-deriving the equations of motion ourselves, to find the best design for throwing a set projectile the greatest distance. Design variables were short arm length, sling length, weight rope length, swing clearance, starting angle and release angle. The constraints were the footprint, the height of the pivot, and...

OPTIMIZATION OF DESTINATIONS AND PATHS FOR QUADCOPTER DELIVERY OF AMAZON PARCELS_Code.zip

Humberto Detrinidad
See details in publication: OPTIMIZATION OF DESTINATIONS AND PATHS FOR QUADCOPTER DELIVERY OF AMAZON PARCELS

Final path plotting algorithm

Jenna Newcomb
Final algorithm that returns the flight path of UAV. Used in final optimization to find bank angles.

Final path plotting algorithm

Jenna Newcomb
Final algorithm that returns the flight path of UAV. Used in final optimization to find bank angles.

Optimization Code

Allison Lee
This code requires Matlab Global Optimization Toolbox as it is uses Matlab's Genetic Algorithm function. It was designed for Matlab 2015b.

Optimization Code

Allison Lee
This code requires Matlab Global Optimization Toolbox as it is uses Matlab's Genetic Algorithm function. It was designed for Matlab 2015b.

skedm

Nick Cortale
Scikit Empirical Dynamic Modeling (skedm) can be used as a way to forecast time series, spatio-temporal 2D or 3D arrays, and even discrete spatial arrangements. More importantly, skedm can provide insight into the underlying dynamics of a system, specifically whether a system is nonlinear and deterministic or whether it is dominated by noise.

skedm

Nick Cortale
Scikit Empirical Dynamic Modeling (skedm) can be used as a way to forecast time series, spatio-temporal 2D or 3D arrays, and even discrete spatial arrangements. More importantly, skedm can provide insight into the underlying dynamics of a system, specifically whether a system is nonlinear and deterministic or whether it is dominated by noise.

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    29
  • 2012
    1

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