Multi-objective routing and scheduling for airport ground movement

Recent research on airport ground movement introduced an Active Routing framework to support multi-objective trajectory-based operations. This results in edges in the airport taxiway graph having multiple costs such as taxi time, fuel consumption and emissions. In such a graph, multiple edges exist between two nodes reflecting different trade-offs among the multiple costs. Aircraft will have to choose the most efficient edge from multiple edges in order to traverse from one node to another respecting various operational constraints. In this paper, we introduce a multi-objective routing and scheduling algorithm based on the enumerative approach that can be used to solve such a multi-objective multi-graph problem. Results using the proposed algorithm for a range of international airports are presented. Compared with other routing and scheduling algorithms, the proposed algorithm can find a representative set of optimal or near optimal solutions in a single run when the sequence of aircraft is fixed. In order to accelerate the search, heuristic functions and a preference-based approach are introduced. We analyse the performance of different approaches and discuss how the structure of the multi-graph affects computational complexity and quality of solutions

Competitive behavior of airlines at multiple airport systems

February 1995Also issued as an M.S. thesis, Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1995Includes bibliographical references (p. 201-204)The way passenger traffic is distributed at multiple airport systems continues to intrigue air transportation planners, urban planners, and policy-makers as researchers attempt to unravel how airlines, air travelers and airports relate to each other. While previous research efforts have typically concentrated on the air travelers' choice of airports, the current thesis addresses how the competitive behavior of airlines operating in a deregulated environment influences the air traveler's choice of airports and the resulting distribution of passenger traffic in the multiple airport system. The methodology of the research first involves identifying four scenarios under which airlines compete in multiple airport environments, after which an anecdotal analysis of a select number of city-pair markets for each scenario was performed to solicit supporting evidence of competitive behavior of airlines. To keep the preliminary investigation simple, the author has chosen to study the dual-airport systems at Chicago and Houston. Owing to limitations of the data from O&DPlus and ONBOARD, the author used a strict set of criteria to identify 14 city-pair markets to analyze the response of passengers and airlines to challengers entering the city-pair markets between 1984 and 1993. The six quantitative indicators used in the anecdotal analyses include: average fares, average number of nonstop departures per day each way, quarterly origindestination traffic, quarterly non-origin-destination traffic, average quarterly load factors, and the quarterly total airport-to-airport origin-destination traffic. The results of the research indicate that while competition is evident, a general trend of competitive behavior of the airlines in the multiple airport environment is not discernible. The entry of a challenger typically elicits a variety of responses. Significant stimulation of the origin-destination traffic was observed in cases where low-fare carriers entered the market. The fact that the number of non-origin-destination passengers usually exceeds the number of origin-destination passengers may indicate that justification for the service in the airport-pairs examined goes beyond simply satisfying the demand for travel in the origin-destination market. Although quantitative modeling techniques were not used in this study, the author believes that future researchers should contend with the complex, multi-dimensional nature of airline competition before attempting to accurately model the competitive behavior of airlines at multiple airport systems

A design of a PBX to ATM user to network interface over DS1/T1 lines

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 189).by Edmund G. Chen.M.S

Dynamical versus Bayesian Phase Transitions in a Toy Model of Superposition

We investigate phase transitions in a Toy Model of Superposition (TMS) using Singular Learning Theory (SLT). We derive a closed formula for the theoretical loss and, in the case of two hidden dimensions, discover that regular $k$-gons are critical points. We present supporting theory indicating that the local learning coefficient (a geometric invariant) of these $k$-gons determines phase transitions in the Bayesian posterior as a function of training sample size. We then show empirically that the same $k$-gon critical points also determine the behavior of SGD training. The picture that emerges adds evidence to the conjecture that the SGD learning trajectory is subject to a sequential learning mechanism. Specifically, we find that the learning process in TMS, be it through SGD or Bayesian learning, can be characterized by a journey through parameter space from regions of high loss and low complexity to regions of low loss and high complexity

Using Wireless Link Dynamics to Extract a Secret Key in Vehicular Scenarios

Securing a wireless channel between any two vehicles is a crucial component of vehicular networks security. This can be done by using a secret key to encrypt the messages. We propose a scheme to allow two cars to extract a shared secret from RSSI (Received Signal Strength Indicator) values in such a way that nearby cars cannot obtain the same key. The key is information-theoretically secure, i.e., it is secure against an adversary with unlimited computing power. Although there are existing solutions of key extraction in the indoor or low-speed environments, the unique channel conditions make them inapplicable to vehicular environments. Our scheme effectively and efficiently handles the high noise and mismatch features of the measured samples so that it can be executed in the noisy vehicular environment. We also propose an online parameter learning mechanism to adapt to different channel conditions. Extensive real-world experiments are conducted to validate our solution

Disease transmission models for public health decision making: toward an approach for designing intervention strategies for Schistosomiasis japonica.

Mathematical models of disease transmission processes can serve as platforms for integration of diverse data, including site-specific information, for the purpose of designing strategies for minimizing transmission. A model describing the transmission of schistosomiasis is adapted to incorporate field data typically developed in disease control efforts in the mountainous regions of Sichuan Province in China, with the object of exploring the feasibility of model-based control strategies. The model is studied using computer simulation methods. Mechanistically based models of this sort typically have a large number of parameters that pose challenges in reducing parametric uncertainty to levels that will produce predictions sufficiently precise to discriminate among competing control options. We describe here an approach to parameter estimation that uses a recently developed statistical procedure called Bayesian melding to sequentially reduce parametric uncertainty as field data are accumulated over several seasons. Preliminary results of applying the approach to a historical data set in southwestern Sichuan are promising. Moreover, technologic advances using the global positioning system, remote sensing, and geographic information systems promise cost-effective improvements in the nature and quality of field data. This, in turn, suggests that the utility of the modeling approach will increase over time

Spatiotemporal expression of the serine protease inhibitor, SERPINE2, in the mouse placenta and uterus during the estrous cycle, pregnancy, and lactation

<p>Abstract</p> <p>Background</p> <p>SERPINE2, also known as glia-derived nexin or protease nexin-1, belongs to the serine protease inhibitor (SERPIN) superfamily. It is one of the potent serpins that modulates the activity of the plasminogen activator (PA) and was implicated in tissue remodeling. In this study, we investigated the expression patterns of SERPINE2 in the mouse placenta and uterus during the estrous cycle, pregnancy, and lactation.</p> <p>Methods</p> <p>SERPINE2 was purified from mouse seminal vesicle secretion using liquid chromatography (LC) and identified by LC/tandem mass spectrometry. The antiserum against the SERPINE2 protein was raised in rabbits. To reveal the uterine and placental expression of SERPINE2, tissues at various stages were collected for real-time PCR quantification, Western blotting, and immunohistochemical staining.</p> <p>Results</p> <p>Serpine2 mRNA was the major PA inhibitor in the placenta and uterus during the estrous cycle, pregnancy, and lactation, although Serpine1 mRNA had higher expression levels than Serpine2 mRNA in the placenta. Plat seemed to be the major PA in the mouse uterus and placenta. Antiserum against the SERPINE2 protein specifically recognized two forms of SERPINE2 and an extra 75-kDa protein, which was probably a complex of SERPINE2 with a certain protease, from among thousands of protein components in the tissue extract as demonstrated by Western blotting. In the uterus, SERPINE2 was primarily localized in luminal and glandular epithelial cells but it also was detected in circular and longitudinal smooth muscle cells during the estrous cycle and lactation. It was prominently expressed in decidual stroma cells, the metrial gland, and endometrial epithelium of the pregnant uterus. In the placenta, SERPINE2 was expressed in trophoblasts of the labyrinth and spongiotrophoblasts. However, its expression was remarkably reduced in giant cells which existed in the giant cell-decidual junction zone. In contrast, prominent expression of SERPINE2 seemed to be detected on clusters of glycogen cells near the junction zone. In addition, yolk sac membranes also showed high expression of SERPINE2.</p> <p>Conclusions</p> <p>These findings indicate that SERPINE2 is a major PA inhibitor in the placenta and uterus during the estrous cycle, pregnancy, and lactation. It may participate in the PA-modulated tissue remodeling process in the mouse placenta and uterus.</p