Monotonic Variations of Consumer Surplus and Comparative Performance Results

We allow for three traffic scenarios: the tradable credits scheme, congestion pricing, and no traffic measure. The utility functions of different modes (car, bus, and bicycle) are developed by considering the income’s impact on travelers’ behaviors. Their purpose is to analyze the demand distribution of different modes. A social optimization model is built aiming at maximizing the social welfare. The optimal tradable credits scheme (distribution of credits, credits charging, and the credit price), congestion pricing fees, bus frequency, and bus fare are obtained by solving the model. Mode choice behavior under the tradable credits scheme is also studied. Numerical examples are presented to demonstrate the model’s availability and explore the effects of the three schemes on traffic system’s performance. Results show congestion pricing would earn more social welfare than the other traffic measures. However, tradable credits scheme will give travelers more consumer surplus than congestion pricing. Travelers’ consumer surplus with congestion pricing is the minimum, which injures the travelers’ benefits. Tradable credits scheme is considered the best scenario by comparing the three scenarios’ efficiency.

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A Chaotic-Based Encryption/Decryption Framework for Secure Multimedia Communications

Entropy ◽

10.3390/e22111253 ◽

2020 ◽

Vol 22 (11) ◽

pp. 1253

Author(s):

Ibrahim Yasser ◽

Mohamed A. Mohamed ◽

Ahmed S. Samra ◽

Fahmi Khalifa

Keyword(s):

Dominant Role ◽

Data Encryption ◽

Comparative Performance ◽

Secure Data Transmission ◽

Encryption Schemes ◽

Media Types ◽

Encryption Decryption ◽

Residual Intelligibility ◽

Performance Results ◽

And Diffusion

Chaos-based encryption has shown an increasingly important and dominant role in modern multimedia cryptography compared with traditional algorithms. This work proposes novel chaotic-based multimedia encryption schemes utilizing 2D alteration models for high secure data transmission. A novel perturbation-based data encryption for both confusion and diffusion rounds is proposed. Our chaotification structure is hybrid, in which multiple maps are combined combines for media encryption. Blended chaotic maps are used to generate the control parameters for the permutation (shuffling) and diffusion (substitution) structures. The proposed schemes not only maintain great encryption quality reproduced by chaotic, but also possess other advantages, including key sensitivity and low residual clarity. Extensive security and differential analyses documented that the proposed schemes are efficient for secure multimedia transmission as well as the encrypted media possesses resistance to attacks. Additionally, statistical evaluations using well-known metrics for specific media types, show that proposed encryption schemes can acquire low residual intelligibility with excessive nice recovered statistics. Finally, the advantages of the proposed schemes have been highlighted by comparing it against different state-of-the-art algorithms from literature. The comparative performance results documented that our schemes are extra efficacious than their data-specific counterpart methods.

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Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment

Sensors ◽

10.3390/s21082582 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2582

Author(s):

Seedahmed S. Mahmoud ◽

Akshay Kumar ◽

Youcun Li ◽

Yiting Tang ◽

Qiang Fang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Healthy Individuals ◽

Comparative Performance ◽

Time Frequency ◽

Assessment Tasks ◽

Decision Logic ◽

Performance Results ◽

Learning Frameworks ◽

Speech Assessment

Speech assessment is an essential part of the rehabilitation procedure for patients with aphasia (PWA). It is a comprehensive and time-consuming process that aims to discriminate between healthy individuals and aphasic patients, determine the type of aphasia syndrome, and determine the patients’ impairment severity levels (these are referred to here as aphasia assessment tasks). Hence, the automation of aphasia assessment tasks is essential. In this study, the performance of three automatic speech assessment models based on the speech dataset-type was investigated. Three types of datasets were used: healthy subjects’ dataset, aphasic patients’ dataset, and a combination of healthy and aphasic datasets. Two machine learning (ML)-based frameworks, classical machine learning (CML) and deep neural network (DNN), were considered in the design of the proposed speech assessment models. In this paper, the DNN-based framework was based on a convolutional neural network (CNN). Direct or indirect transformation of these models to achieve the aphasia assessment tasks was investigated. Comparative performance results for each of the speech assessment models showed that quadrature-based high-resolution time-frequency images with a CNN framework outperformed all the CML frameworks over the three dataset-types. The CNN-based framework reported an accuracy of 99.23 ± 0.003% with the healthy individuals’ dataset and 67.78 ± 0.047% with the aphasic patients’ dataset. Moreover, direct or transformed relationships between the proposed speech assessment models and the aphasia assessment tasks are attainable, given a suitable dataset-type, a reasonably sized dataset, and appropriate decision logic in the ML framework.

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Review for "Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results"

10.1002/eng2.12295/v1/review3 ◽

2020 ◽

Author(s):

A Ashraf A.M. Khalaf

Keyword(s):

Antenna Array ◽

Smart Antenna ◽

Adaptive Beamforming ◽

Performance Study ◽

Comparative Performance ◽

Array Design ◽

Smart Antenna Array

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The Relation Between Financial Incentives, Motivation, and Performance

Journal of Personnel Psychology ◽

10.1027/1866-5888/a000182 ◽

2017 ◽

Vol 16 (2) ◽

pp. 61-76 ◽

Cited By ~ 12

Author(s):

Anaïs Thibault Landry ◽

Marylène Gagné ◽

Jacques Forest ◽

Sylvie Guerrero ◽

Michel Séguin ◽

...

Keyword(s):

Financial Incentives ◽

Work Performance ◽

Autonomous Motivation ◽

Compensation Plans ◽

Work Settings ◽

Path Analyses ◽

Three Samples ◽

And Performance ◽

Performance Results

Abstract. To this day, researchers are debating the adequacy of using financial incentives to bolster performance in work settings. Our goal was to contribute to current understanding by considering the moderating role of distributive justice in the relation between financial incentives, motivation, and performance. Based on self-determination theory, we hypothesized that when bonuses are fairly distributed, using financial incentives makes employees feel more competent and autonomous, which in turn fosters greater autonomous motivation and lower controlled motivation, and better work performance. Results from path analyses in three samples supported our hypotheses, suggesting that the effect of financial incentives is contextual, and that compensation plans using financial incentives and bonuses can be effective when properly managed.

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