People
Umer Asgher, MSc., Ph.D.

2025, Neuroscience Bulletin, 41 (1), p. 46-60), ISSN 1995-8218

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.

2024, Neural Network World, 34 (3), p. 135-168), ISSN 1210-0552

This study presents a comprehensive multi-objective transportation model aimed at optimizing complex vehicle routing problems, which are nondeterministic polynomial time NP-hard due to spatial, temporal, and capacity constraints. In this study, the multi-objective transportation model integrates decisionmaker preferences with hybrid optimization techniques, including the approximatecombinatorial method, ant colony optimization and evolutionary algorithms. it seeks to minimize transportation costs, time, and emissions while accounting for real-world constraints such as fleet composition, customer demand, and servicelevel agreements. The techniques like multi-criteria decision-making methods are employed to refine the solution set, balancing objectives like cost, time, environmental impact, and service level. The novel optimization model is applied to a fuel distribution case study involving 18 customers and a heterogeneous fleet, where it optimizes vehicle routes to meet delivery requirements efficiently. The multiobjective transportation framework generates multiple feasible solutions, which are further narrowed down using decision-making frameworks to ensure alignment with organizational goals and decision-maker preferences. The integration of quantitative optimization techniques with qualitative decision-making processes makes this model robust and scalable, offering a practical tool for enhancing operational efficiency in transportation systems. This approach effectively addresses real-world logistics challenges, demonstrating significant improvements in route efficiency, cost savings, and environmental sustainability.

2024, 2024 New Trends in Aviation Development (NTAD), IEEE (Institute of Electrical and Electronics Engineers)), p. 159-164), ISBN 979-8-3315-2774-7, ISSN 2836-2764

Fear of flying, or aviophobia, is a significant issue affecting a substantial portion of the population, with young adults being particularly vulnerable. Despite the high safety record of aviation, this irrational fear can severely impact an individual’s willingness or ability to travel by air, especially in the context of increasing global air traffic. This study investigates the factors contributing to fear of flying among young commuters aged 18-26 using a stated preference survey. The analysis highlights that fear is primarily driven by emotional responses to situational triggers rather than rational assessments of air travel safety. Key predictors of fear include concerns about terrorism, aviation accidents, and adverse weather conditions, with these factors exerting significant emotional influence despite their statistical rarity. The study also reveals a disconnect between objective safety perceptions and subjective fear, indicating that even passengers who acknowledge the general safety of air travel may still experience anxiety triggered by situational factors.

2024, Frontiers in Robotics and AI, 11, ISSN 2296-9144

Colonoscopy is a reliable diagnostic method to detect colorectal polyps early on and prevent colorectal cancer. The current examination techniques face a significant challenge of high missed rates, resulting in numerous undetected polyps and irregularities. Automated and real-time segmentation methods can help endoscopists to segment the shape and location of polyps from colonoscopy images in order to facilitate clinician’s timely diagnosis and interventions. Different parameters like shapes, small sizes of polyps, and their close resemblance to surrounding tissues make this task challenging. Furthermore, high-definition image quality and reliance on the operator make real-time and accurate endoscopic image segmentation more challenging. Deep learning models utilized for segmenting polyps, designed to capture diverse patterns, are becoming progressively complex. This complexity poses challenges for real-time medical operations. In clinical settings, utilizing automated methods requires the development of accurate, lightweight models with minimal latency, ensuring seamless integration with endoscopic hardware devices. To address these challenges, in this study a novel lightweight and more generalized Enhanced Nanonet model, an improved version of Nanonet using NanonetB for real-time and precise colonoscopy image segmentation, is proposed. The proposed model enhances the performance of Nanonet using Nanonet B on the overall prediction scheme by applying data augmentation, Conditional Random Field (CRF), and Test-Time Augmentation (TTA). Six publicly available datasets are utilized to perform thorough evaluations, assess generalizability, and validate the improvements: Kvasir-SEG, Endotect Challenge 2020, Kvasir-instrument, CVC-ClinicDB, CVC-ColonDB, and CVC-300. Through extensive experimentation, using the Kvasir-SEG dataset, our model achieves a mIoU score of 0.8188 and a Dice coefficient of 0.8060 with only 132,049 parameters and employing minimal computational resources. A thorough cross-dataset evaluation was performed to assess the generalization capability of the proposed Enhanced Nanonet model across various publicly available polyp datasets for potential real-world applications. The result of this study shows that using CRF (Conditional Random Fields) and TTA (Test-Time Augmentation) enhances performance within the same dataset and also across diverse datasets with a model size of just 132,049 parameters. Also, the proposed method indicates improved results in detecting smaller and sessile polyps (flats) that are significant contributors to the high miss rates.

2024, Transportation Research Procedia, Amsterdam, Elsevier B.V.), p. 278-284), ISSN 2352-1465

Vertical take-off and landing unmanned aerial vehicles (VTOL UAVs) are becoming increasingly important in the modern aviation industry. With their growing use, it is essential to find solutions to newly emerging technical problems related to ensuring operational safety. One of these unresolved issues is the accumulation of ice on propeller blades during flight in adverse meteorological conditions. For VTOL UAVs, the propeller blades are the only lifting surfaces, and any disruption of their geometry by an ice layer can lead to the crash of the entire aircraft. A promising solution to this problem, currently being explored experimentally, is the use of hydrophobic coatings, which successfully delay or completely prevent the accumulation of ice. However, hydrophobic coatings currently lack sufficient durability and have a low ability to withstand normal operational wear and tear, causing them to lose their hydrophobic properties quickly. The primary goal of this work is to propose a concept and testing methodology that would allow propeller blades treated with hydrophobic coatings to be exposed to conditions as close as possible to operational wear and tear.

2024, Aerospace, 11 (12), ISSN 2226-4310

The rapid growth in air traffic has led to increasing congestion at airports, creating bottlenecks that disrupt ground operations and compromise the efficiency of air traffic management (ATM). Ensuring the predictability of ground operations is vital for maintaining the sustainability of the ATM sector. Flight efficiency is closely tied to adherence to assigned airport arrival and departure slots, which helps minimize primary delays and prevents cascading reactionary delays. Significant deviations from scheduled arrival times—whether early or late—negatively impact airport operations and air traffic flow, often requiring the imposition of Air Traffic Flow Management (ATFM) regulations to accommodate demand fluctuations. This study leverages a data-driven machine learning approach to enhance the predictability of in-block and landing times. A Bidirectional Long Short-Term Memory (BiLSTM) neural network was trained using a dataset that integrates flight trajectories, meteorological conditions, and airport operations data. The model demonstrated high accuracy in predicting landing time deviations, achieving a Root-Mean-Square Error (RMSE) of 8.71 min and showing consistent performance across various long-haul flight profiles. In contrast, in-block time predictions exhibited greater variability, influenced by limited data on ground-level factors such as taxi-in delays and gate availability. The results highlight the potential of deep learning models to optimize airport resource allocation and improve operational planning. By accurately predicting landing times, this approach supports enhanced runway management and the better alignment of ground handling resources, reducing delays and increasing efficiency in high-traffic airport environments. These findings provide a foundation for developing predictive systems that improve airport operations and air traffic management, with benefits extending to both short- and long-haul flight operations.

2024, 2024 New Trends in Aviation Development (NTAD), IEEE (Institute of Electrical and Electronics Engineers)), p. 150-158), ISBN 979-8-3315-2774-7, ISSN 2836-2764

Runway slope illusions, a significant challenge in aviation, can distort pilots’ perception during critical phases of flight, particularly approach and landing. This study explores the susceptibility of pilots to these illusions in a controlled simulator environment using virtual reality technology. A systematic review of existing literature was conducted using the PRISMA methodology, revealing a notable gap in experimental studies directly addressing runway slope illusions. Subsequently, an experimental investigation was designed where 15 pilots performed repeated approaches to runways with varying slopes. Key performance metrics such as mean deviation from the ideal glide slope, standard deviation, RMSE, and time spent below the glide slope were analyzed. Although no statistically significant differences were found across the different runway slopes, patterns emerged, highlighting performance variability, especially on upslope runways. The findings underscore the need for further research into the effects of runway slope illusions on pilot performance, particularly under varying flight conditions.

2024, New Trends in Civil Aviation: Proceedings of the 24th International Conference on New Trends in Civil Aviation 2024, Praha, České vysoké učení technické v Praze), p. 53-58), ISBN 978-80-01-07181-6, ISSN 2694-7854

The approach to landing and the landing of an aircraft represent one of the most critical phases of flight. Under certain conditions, a pilot's activity in this phase can be influenced by the visual illusion of the runway aspect ratio. In this context, an experiment was designed to determine the impact of the runway aspect ratio illusion on pilot performance based on its objective evaluation. The experiment was conducted on a flight simulator using a virtual reality headset. Flight data, collected during visual flight rules approaches, were subsequently analyzed. The deviation of the descent angle during visual approaches from the optimal angle for visual aircraft landing was examined. Data analysis using the repeated measures ANOVA confirmed the influence of this illusion on pilots during flight in a simulated environment, as indicated by the root mean square error and maximum absolute error.