Telling Faults From Cyber-Attacks In A Multi-Modal Logistic System With Complex Network Analysis

We investigate the application of methodologies for the analysis of complex networks to understand the properties of systems of systems in a cybersecurity context. We are interested to resilience and attribution: the first relates to the behavior of the system in case of faults/attacks, namely to its capacity to recover full or partial functionality after a fault/attack; the second corresponds to the capability to tell faults from attacks, namely to trace the cause of an observed malfunction back to its originating cause(s). We present experiments to witness the effectiveness of our methodology considering a discrete event simulation of a multimodal logistic network featuring 40 nodes distributed across Italy and a daily traffic roughly corresponding to the number of containers shipped through in Italian ports yearly, averaged on a daily basis.

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Optimization of tillage and sowing operations using discrete event simulation

Research in Agricultural Engineering ◽

10.17221/49/2017-rae ◽

2018 ◽

Vol 64 (No. 4) ◽

pp. 187-194 ◽

Cited By ~ 2

Author(s):

Armaghan Kosari Moghaddam ◽

Hassan Sadrnia ◽

Hassan Aghel ◽

Mohammad Bannayan

Keyword(s):

Discrete Event Simulation ◽

Discrete Event ◽

Daily Basis ◽

Simulation Software ◽

Field Size ◽

Daily Work ◽

Event Simulation ◽

Significant Difference ◽

Average Work ◽

Work Method

A simulation model was developed for secondary tillage and sowing operations in autumn, using discrete event simulation technique in Arena<sup>®</sup> simulation software (Version 14). Eight machinery sets were evaluated on a 50-hectare farm. Total costs including fixed-costs, variable costs and timeliness costs were calculated for each machinery set. Timeliness costs were estimated for 21-years period on daily basis (Daily Work method) and compared with another method (Average Work method) based on the equation proposed by ASAE Standards, EP 496.3FEB2006. The Inputs of the model were machinery sets, field size, machines performances and daily soil workability state. The optimization criteria were the lowest costs and lowest standard deviation in daily work method plus the lowest costs based on average work method. The validity of the model was evaluated by comparing the output of the model with field observed data collected from various farms. Results revealed that there was no significant difference (P > 0.01) between the observed and predicted finish day.

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Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

PLoS ONE ◽

10.1371/journal.pone.0253869 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253869

Author(s):

Michael Saidani ◽

Harrison Kim ◽

Jinju Kim

Keyword(s):

Simulation Model ◽

Discrete Event Simulation ◽

Discrete Event ◽

Daily Basis ◽

Health Crisis ◽

Event Simulation ◽

Discrete Event Simulation Model ◽

Rapid Result ◽

The Usa ◽

The Impact

Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.

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