Adaptive Airport Architecture

The architecture of Airport terminals is unique in nature as it is linked with a wide range of concerns that go hand in hand to demonstrate the efficient functioning of the building. From an inconvenient mode of travel to the city center to the congestions in the security screening, concerns varying from urban design to systems engineering have an impact on the user experience. Along with these concerns, the spatial organization of the airport terminals accommodates various commercial, leisure, retail, and service-based spaces in addition to the core function of aviation. Where the guiding parameters for determining the spatial requirements are dependent on the projected life span, security restrictions, and other socio-political influences. An airport terminal is bound to maintain a stable balance between all of the above parameters and disruption in any of the above can cause major fluctuations in the performance of the airport terminals. According to the United States department of transportation, federal aviation administration(advisory circular 2014) the initial stages of the design for any existing or new airport are derived from the ‘Master planning report’. This report is comprised of airport layout, environmental studies, analysis of runway orientation, land use plans, activity forecasts, capacity analysis, estimates of facilities, and more. To achieve a balanced environment capable of satisfying the concerns of various institutions it is important that the positioning of each amenity is carefully curated and is designed to perform as expected for several years. The vulnerability of terminal buildings to the technological and infrastructural changes is one of the main problems with the airports. This thesis attempts to analyze different components that cause airport terminals to be rigid to the changes. Following the performance analysis for airport terminals, this project proposed a design solution that exhibits a potential way of increasing the efficiency and life span of the airport terminals. While the flexibility of physical infrastructure is one of the ways to absorb the increasing congestions in the building, it also needs to be organized so that it can ease the tensions in a positive way and do not cause unnecessary complexities. To acknowledge this circumstance and find a fact-based resolution to this issue, this project proposes to work out a system of constants and variables where a series of elements can be retained for a comparatively longer period and be more stationary than the variables of the design that can be changed over a shorter period. Based on the analysis of airport terminals in general and a focused analysis of one particular location this project will propose a unique design solution for the medium hub airport terminals and provide a proof of concept by re-imagining the design of Bradley airport at Hartford

Incorporating Industry Needs into the Development of an Undergraduate Degree in Commercial Space Operations

The rapid expansion of the commercial space industry, not unlike the aviation industry in the early 20th century, has left the industry facing unique challenges. As companies continue to expand, the need for a well-trained workforce becomes critical. The needed workforce must be specifically educated to enter the commercial space industry at graduation. To have a successful industry, a workforce must be trained in skills that meet the industry\u27s needs. In that regard, this study consisted of a survey of leaders in the commercial space industry to identify the different skill-sets sought by the industry. The results of the industry surveys were used in the development of an undergraduate degree in Commercial Space Operations in the College of Aviation at Embry-Riddle Aeronautical University. The findings indicated that the needs of the industry are dynamic and multi-disciplinary in nature and ranged from business planning and space policy to human factors and propulsion. The broad spectrum of needs identified indicate that the industry is fluid with evolving needs. To remain on the forefront of commercial space education, the curriculum must reflect the needs of the industry as the industry evolves. Thus, continual feedback and partnership must be pursued with the industry to ensure that future graduates of the degree possess the skills to pursue a productive career in the commercial space industry

Experimental and Computational Investigation of Ribbed Channels for Gas Turbine Thermal Management

This study focuses on the computational benchmarking as well as validation against experimental results of a rib roughed surface in an internal channel of a stationary turbine blade. STAR-CCM+ was utilized to replace a model from a published article, and to analyze the CFD conjugate heat transfer by determining the turbulence model that best matched the published experimental values. Using those computational conditions and CFD results, an in house experimental rig was validated by comparing convective heat transfer coefficients and pressure profiles. This cooling method, when compared to a smooth channel, enhances turbulent mixing my separating and reattaching the boundary layer which increases the heat transfer. The overall goal is to analyze an effective cooling method, studying the flow physics and effective heat transfer rates as well as minimizing the pressure drop across the channel. V²f turbulence model resulted in matching closest to the experimental results, but doe to its unstable nature at high Reynolds number, the EBk-E model was used for preliminary testing. Results for EBk-E showed shorter reattachment lengths giving higher Nusselt number values between ribs. The heat transfer as well as friction factors match within the uncertainty of 6.8% and 6.6% respectively of the published results. Benchmarked computational results will help validate the experimental setup for further optimization and testing different configurations in rib arrangements

Recent Trends in Deep Learning Based Personality Detection

Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection

On the Stability and Scalability of Node Perturbation Learning

To survive, animals must adapt synaptic weights based on external stimuli and rewards. And they must do so using local, biologically plausible, learning rules – a highly nontrivial constraint. One possible approach is to perturb neural activity (or use intrinsic, ongoing noise to perturb it), determine whether performance increases or decreases, and use that information to adjust the weights. This algorithm – known as node perturbation – has been shown to work on simple problems, but little is known about either its stability or its scalability with respect to network size. We investigate these issues both analytically, in deep linear networks, and numerically, in deep nonlinear ones. We show analytically that in deep linear networks with one hidden layer, both learning time and performance depend very weakly on hidden layer size. However, unlike stochastic gradient descent, when there is model mismatch between the student and teacher networks, node perturbation is always unstable. The instability is triggered by weight diffusion, which eventually leads to very large weights. This instability can be suppressed by weight normalization, at the cost of bias in the learning rule. We confirm numerically that a similar instability, and to a lesser extent scalability, exist in deep nonlinear networks trained on both a motor control task and image classification tasks. Our study highlights the limitations and potential of node perturbation as a biologically plausible learning rule in the brain

ASSOCIATION AND CORRELATION OF MEAN PLATELET VOLUME AND PLATELET COUNT IN ACUTE ISCHEMIC STROKE

Objective: Role of platelets in the pathogenesis of the atherothrombosis and ischemic stroke has been documented. Mean platelet volume (MPV) and platelet count (PC) could be important predictors of acute ischemic stroke (AIS), its severity; therefore we investigated the correlation of MPV & PC in AIS patients. Methods: We studied MPV and PC of 52 AIS patients consecutively admitted in Neurology department at Geetanjali Medical University, India. Platelet variables were measured and compared with control of similar age, sex and without vascular events. Results: Out of 52 patients, 30 (57.69%) had Thirty (57.69%) patients had significantly higher MPV in AIS group (12.45fL compared with normal range of 6–11 fL in control,p<0.001). No significant differences were found between male and females, but the total mean was elevated. The mean of PC was 1.76×105 cells/cumm (normal range) and there was no correlation between the change in PC and AIS in both sexes. Repeated measurements of MPV and PC were also recorded on follow-up which showed no significant changes from the acute phase; however, MPV remained elevated. The comparison of MPV in patients with mRS score 2 versus 4, 2 versus 5, 3 versus 4 and 5, and 4 versus 5 were found to be statistically significant (p<0.05). Conclusion: Increased MPV has an independent association with AIS and its severity and it could not change after acute treatment. It is possible that these changes precede the vascular event, and further studies are warranted to unravel the underlying mechanism