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Showing 2 results for Gholami
Dr Saiedeh Gholami, Mr. Mahdi Jalalian, Dr Reza Ramezanian,
Volume 7, Issue 1 (4-2016)
Abstract
In the past decade, fuel consumption and CO2 emission have increased in the airline industry. Large CO2 footprint has a damaging effect on the environment. Global concerns over this issue has made the airline industry to be greener. Most efforts of the green airline industry are improving the fuel consumption to reduce the CO2 emission and its environmental damage. Here, we use cruise speed control to control the fuel consumption and CO2 emission. Each aircraft has a different speed level needing a different fuel consumption. Service quality is studied besides the energy consumption. We investigate two objectives including total energy consumption (TEC) and passenger service level (PSL). TEC and PSL are conflicting in nature. We develop a mixed-integer nonlinear programming model to integrate schedule design, aircraft assignment and maintenance routing problems. We make use of the augmented ε-constraint method to solve the problem. To evaluate the model, a real data based on the Emirates airline flights is used. The results are compared using four different scenarios
Saba Gholami, Sara Motamed, Elham Askari,
Volume 17, Issue 1 (5-2026)
Abstract
Autism spectrum disorder (ASD) is consistently associated with abnormal functional connectivity; resting-state fMRI data were obtained from the ABIDE dataset. Dynamic functional connectivity (DFC) was obtained in an autism-specific subnetwork consisting of 17 regions identified from previous static connectivity analyses. Time-varying connectivity matrices were estimated using a sliding window approach, and recurrent connectivity states were identified using a hidden Markov model. Dynamic measures included state occupancy rate, mean dwell time, and edge-level connectivity variability. Compared with controls, individuals with ASD showed a significant decrease in the occupancy of highly integrated connectivity states (ASD: 28.6 ± 7.4% vs. control: 36.9 ± 8.1%, p < 0.001) and longer dwell times in poorly integrated connectivity states (ASD: 42.3 ± 10.2 vs. control: 31.7 ± 9.5 s, p = 0.002). In contrast, edge-level connectivity variability was significantly increased in ASD, particularly in default mode-limbic connections. Importantly, increased connectivity variability in the default mode network significantly predicted ADOS total scores (β = 0.41), (p = 0.001). These findings suggest a dissociation between reduced network state flexibility and increased moment-to-moment connectivity variability in autism spectrum disorder (ASD).
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