Hassan Abdalla

The engineering properties of particulate materials are the collective manifestation of interactions among their constituent particles and are structures within which particles adopt their spatial arrangement. For the first time in the literature, this paper employs an extended concept of ‘fractals’ to show that materials constituting particles of a certain size can be rationalized in three universal fractals. Within each fractal, materials build repeatable, reproducible, and predictable traits, and exhibit the stress-strain behaviors of nondifferentiable, self-similar trajectories. We present experimental evidence for such repeatable traits by subjecting six different particulate materials to static undrained isotropic, static undrained anisotropic, and cyclic undrained isotropic stresses. This paper shows that universal fractals are associated with fractal structures; herein, we explore the matters that influence their spatial arrangement. Within the context of sustainable design, ways of engineering natural particulate systems to improve a product’s physical and hydromechanical properties are already well established. In this review, a novel extended concept of fractals is introduced to inform the biomimetic design of particulate systems, to show how biomimicry can benefit in preserving general behavioral traits, and how biomimicry can offer predicated forms, thereby enhancing the design efficiency. To pursue such an ideal, processes that lead to the engineering of natural materials should not compromise their loyalty to the parent universal fractal.

Congestion is an ongoing problem for many urban centres worldwide (such as London), leading to excessive delays, noise and air pollution, frustrated drivers, and high energy consumption. The carbon footprint of conventional transport systems can be high as a result and transport is among the highest contributors of greenhouse gas emissions. Therefore, with the growing interest in developing connected fully autonomous vehicles (ConFAVs), there is a pressing need to consider their effects within the congested urban setting. To address this, the current research study was designed to investigate the potential for ConFAVs in providing a sustainable transport solution. During this research, a simulation model was developed, calibrated, and validated using field data collected from several sites in East London, using the graphical user interface (GUI) simulation software PTV VISSIM to simulate the proposed driving and car following behaviour, which included the platooning of these ConFAVs, to assess how they could improve the level of service of the roads. Using the new model, this research addresses the shortcomings of two other adaptations of the Wiedemann 99 car-following models by changing the ConFAV’s behaviour to be more cautious when travelling behind a human driven vehicle, and less cautious when behind another ConFAV. As little is known about the transitional period from zero autonomy to full autonomy on the already congested road network, due to the fact that these vehicles are typically tested in small numbers (often one at a time in a controlled environment), the present research study introduced ConFAVs to the simulated network gradually and in large numbers at 20% intervals (namely 0% where there are no ConFAVs, 20%, 40%, 60%, 80%, and finally 100% where all vehicles within the network were ConFAVs). The average delays and subsequent level of service for the roads within the networks were then assessed against each ConFAV penetration level. This helped understand how the network’s efficiency changes when the number of ConFAVs increases, and the potential benefits for these self-driving vehicles on congestion and the ensuing greenhouse gas emissions. The model showed that a reduction in delay of up to 100% can be achieved by introducing ConFAVs, which translates to a significant reduction in greenhouse gas emissions. This, coupled with the fact that ConFAVs are predominantly electric, points to a future sustainable road transport system. The primary purpose of this research would be to investigate the potential of ConFAVs in reducing traffic congestion and, as a result, greenhouse gas emissions.

Gait impairments during real-world locomotion are common in neurological diseases. However, very little is currently known about the neural correlates of walking in the real world and on which regions of the brain are involved in regulating gait stability and performance. As a first step to understanding how neural control of gait may be impaired in neurological conditions such as Parkinson’s disease, we investigated how regional brain activation might predict walking performance in the urban environment and whilst engaging with secondary tasks in healthy subjects. We recorded gait characteristics including trunk acceleration and brain activation in 14 healthy young subjects whilst they walked around the university campus freely (single task), while conversing with the experimenter and while texting with their smartphone. Neural spectral power density (PSD) was evaluated in three brain regions of interest, namely the pre-frontal cortex (PFC) and bilateral posterior parietal cortex (right/left PPC). We hypothesized that specific regional neural activation would predict trunk acceleration data obtained during the different walking conditions. Vertical trunk acceleration was predicted by gait velocity and left PPC theta (4–7 Hz) band PSD in single-task walking (R-squared = 0.725, p = 0.001) and by gait velocity and left PPC alpha (8–12 Hz) band PSD in walking while conversing (R-squared = 0.727, p = 0.001). Medio-lateral trunk acceleration was predicted by left PPC beta (15–25 Hz) band PSD when walking while texting (R-squared = 0.434, p = 0.010). We suggest that the left PPC may be involved in the processes of sensorimotor integration and gait control during walking in real-world conditions. Frequency-specific coding was operative in different dual tasks and may be developed as biomarkers of gait deficits in neurological conditions during performance of these types of, now commonly undertaken, dual tasks.

A new servo drive for electro discharge machining industrial applications is presented in this paper. The development processes of the servo feed drive have passed through three main stages. The first stage focused on design and development of a linear piezoelectric ultrasonic motor. The second one concentrated on development of an electronic driver and its embedded software. The integration, testing, and validation in electro discharge machining system, was the last stage of the development lifecycle. The linear piezoelectric ultrasonic motor consists of three main parts, the stator, rotor, and sliding element. The motor design process, basic configuration, principles of motion, finite element analysis, and experimental examination of the main characteristics are discussed in this paper. The electronic driver of the ultrasonic motor consists of two main stages, the booster and piezoelectric amplifier. The piezo amplifier consists of four output transistors, a push-pull and bridge, connected in order to achieve the necessary electrical parameters to drive and control the motor servo feed drive traveling speed. The essential experimental arrangement to implement and examine the developed ultrasonic servo feed drive in an electro discharge machining system was carried out. The initial results showed that the servo drive is able to provide: a reversible directional of motion, no-load traveling speed equal to 28 mm per s, maximum load of 0.78 N, a resolution <50 μm, and a dynamic time response <10 ms. The electron microscopic micro examination into the machined samples showed that: ultrasonic servo drive showed a clear improvement in the surface profile finish, a notable reduction in the stability, processing time, material removal rate, arcing, and short-circuiting teething phenomena. This was verified by assessing the electrode movements, the variations of the inter electrode gap voltage, current, and feedback control signals.