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Continuous transportation demand growth in recent years has led to many traffic issues in urban areas. Among the most challenging ones are traffic congestion and the associated vehicular emissions. Efficient design of traffic signal control systems can be a promising approach to address these problems. This research develops a real-time signal control system, which optimizes signal timings at an under-saturated isolated intersection by minimizing total vehicular emissions. A combination of previously introduced analytical models based on traffic flow theory has been used. These models are able to estimate time spent per driving mode (i.e., time spent accelerating, decelerating, cruising, and idling) as a function of demand, vehicle arrival times, saturation flow, and signal control parameters. Information on vehicle activity is used along with the Vehicle Specific Power (VSP) model, which estimates emission rates per time spent in each operating mode to obtain total emissions per cycle. For the evaluation of the proposed method, data from two real-world intersections of Mesogion and Katechaki Avenues located in Athens, Greece and University and San Pablo Avenues, in Berkeley, CA has been used. The evaluation has been performed through both deterministic (i.e. under the assumption of perfect information for all inputs) and stochastic (i.e. without having perfect information for some inputs) arrival tests. The results of evaluation tests have shown that the proposed emission-based signal control system reduces emissions compared to traditional vehicle-based signal control system in most cases.

<p>Many daily motor tasks involve the precise control of both force level and motor timing. The neural mechanisms concurrently managing these movement parameters remain unclear, as the dominant focus of previous literature has been to examine each in isolation. As a result, little is understood regarding the contribution of various sensory modalities to force output and interval production in sequential motor tasks. This thesis uses a sequential force production task to investigate the roles of visual and somatosensory feedback in the timed control of force. In Chapter 2 we find that removal of visual force feedback resulted in specific force output errors, but leaves motor timing behavior relatively unaffected according to predictions of the two-level timing model by Wing and Kristofferson (1973). In Chapter 3, we show that force output errors exhibited in the absence of a visual reference may be related to the processing of reafferent somatosensation from self-generated force pulses. The results of Chapter 4 reveal evidence that force errors exhibited following visual feedback removal are consistent with a shift in the perceived magnitude of force output and that the direction of error may be determined by prior task constraints. In Chapter 5 we find evidence of effector-specificity in the processing of and compensation for reafferent somatosensation. Lastly, in Chapter 6 we find that the interplay between audition and somatosensation in the control of sound level by the vocal effectors resembles that which is observed between vision and somatosensation in the control of force by the distal effectors.</p> / Doctor of Philosophy (PhD)

Recent years have witnessed a growing concern for the ability of firms to effectively manage their new product innovation in the face of disruptive technological changes, increased global competition, and rising costs of research and development. These concerns notwithstanding, firms are additionally required to launch radical new products to the market, as incremental new products provide their developers with only short-term sales and profitability. In response to these challenges, firms have entered into collaborative alliances to share the risks and costs involved in the new product development (NPD) process and to enhance their product innovation performance. Turning research discoveries into marketable radical new products through collaborative alliances is even more important for relatively small firms operating in technologically intensive industries. Such firms are often underfunded and unable to undertake a full NPD cycle internally due to an inability of assembling the right mix of internal capabilities. The inevitable need to access capabilities from alliance partners may lead some small firms to form collaborative alliances under unfavourable situations, which make alliances prone to failure (70% by some estimates) to reach new product innovation goals. The substantial rate of alliance failure is embedded in a clash between the logic of radical new product innovation management (the need for flexibility between alliance partners), and recommendations for alliance management (the need to determine the responsibilities of each partner from the onset of the alliance). Despite the benefits of alliances in providing required resources, alliances can impose substantial transaction costs to focal small firms. Thus, it is crucial to investigate how firms, particularly small firms, can make a balance between the benefits and costs involved in alliances, to mitigate alliance risks and increase the probability of new product radicalness. In this thesis, I introduce a new typology and demonstrate its application to product performance. The typology categorizes alliance partnerships along two dimensions of partnership timing (the stage of the NPD process during which alliance is formed) and partnership type (the role of alliance partner during the NPD process). I use this this typology to determine the interaction effects of partnership timing and type on the probability of product innovativeness (radicalness). To this end, I rely on insights from Transaction Cost Economics (TCE) and Resource Based View of the firm (RBV) theories as well as the absorptive capacity concept to develop testable hypotheses. I use a sample of 230 drugs developed by 85 biotechnology firms in collaborative alliances with 384 alliances in 1982-2016 with universities and research institutes, other biotechnology firms, and pharmaceutical firms formed during discovery, development, and prelaunch stages of the new drug development process. I find that the probability of drug radicalness increases when alliances with universities and research institutes, as well as other biotech firms, are formed during the discovery or development stages of the new drug development. However, results indicate that partnership with pharma firms during the discovery or development stages reduces the likelihood of drug radicalness. During the prelaunch stage, except for negative relation between alliances with other biotech and drug radicalness, results failed to find a significant relationship between university as well as pharmaceutical partnership and drug radicalness. By disintegrating alliances along two dimensions of partnership type and timing, this thesis substantially increases the understanding of the benefits and costs of each partnership type and during each stage of the NPD process. This helps relatively small firms to better understand when and with whom during the process of NPD they need to initiate alliances to increase their likelihood of product radicalness. This thesis also contributes to the current theoretical insights of TCE and RBV theories by considering costs and benefits of each partnership type variant along different stages of the NPD process. Methodologically, instead of focusing on analysis using firm level outcome variables (count number of new products), this thesis turns the unit of analysis to product level (innovativeness of the product) and links each product to its designated alliance attributes (timing and type) to provide more subtle and fine-grained implications. / Thesis / Doctor of Philosophy (PhD)

Aims. The aim of this work is to investigate any presence of transit timing variations (TTVs) in a sample of observed targets that has light curves in both K2 mission data and TESS mission data.  Methods. The original sample utilised here was one from the doctoral thesis of D. Soto (2020) with candidates from K2 data. Cross-referencing for corresponding light curves in TESS was done with a customised Python script created for the purposes of this work, automating the process of obtaining light curves using only one mission ID. A transit search was performed on the light curves of each mission separately with the Python software package OpenTS. The candidates with transits in both light curves were subjected to a TTV search using the Python software package PyTTV. The PyTTV software utilises both mission light curves in creating a joint light curve.  Results. Orbital periods, Porb, and transit center times, t0, for 30 targets were updated using joint light curves from the K2 and TESS missions. Seventeen of these systems are found to have non-linear trends in their transit times. These also have constraints for the periods and amplitudes of the TTVs.  The disposition distribution of the 30 systems is that 19 candidates are unregistered candidates, ten are registered planetary candidates or TOIs and eight are registered known planets.  Conclusions. Out of the reference sample by D. Soto (2020) consisting of 564 targets, 257 targets had a corresponding TESS light curve. Out of the 257 targets, a new sample of 45 targets was contrived through a transit search where they all show distinct transits in both missions light curves. Out of these, fifteen targets were not suitable for a TTV search. A final sample of 30 targets are presented, with seventeen targets showing signs of TTVs and thirteen targets showing a linear trend. The parameters of orbital period and transit centre times are updated for all 30 final candidates. Seventeen candidates are shown to have variations in the transit times and are presented with diagnostics. The candidates with transit timing variations should be further investigated for potential validation or follow-up observations. The unregistered candidates as well as the planetary candidates and TOIs should be considered for follow-up observations or similar validation, to confirm or discard a planetary status.

Fear memories are necessary to initiate anticipatory fear responses when we are confronted with cues that predict an impending threat. However, when a cue no longer predicts threat, an extinction memory is formed that actively inhibits the expression of the fear memory. Failure to acquire, consolidate, or recall extinction memories causes fear memory expression (i.e., fear responding) in the absence of threat, which is a hallmark characteristic of most anxiety-related disorders and post-traumatic stress disorder (PTSD). Of further importance, these disorders occur approximately twice as often in women than men, which is thought to partially rely on sex hormone mediated differences in fear extinction. Moreover, deficits in extinction memory processing can also hinder the success of extinction-based exposure therapy, which is commonly used to treat these disorders. Thus, a better understanding of the factors determining the quality of extinction memories is of utmost importance. The present thesis focuses on three of these factors including the female sex hormone 17β-estradiol (E2), fear extinction timing, and the noradrenergic arousal system. To examine the role of E2 (Manuscript 1; low E2 levels or high E2 levels) and fear extinction timing (Manuscript 2; either immediately or delayed after the initial fear memory formation), we used a special differential fear conditioning procedure that allowed us to separately assess fear memories and extinction memories via peripheral arousal responses (measured via skin conductance responses [SCR]) and, most importantly, via central neurophysiological indicators (measured via electroencephalography [EEG]). Concerning EEG parameters, we were especially interested in neural oscillations (especially in the theta and gamma range). To further advance the understanding of the neurophysiological foundations of both memory systems, we also aimed at disentangling oscillatory and non-oscillatory brain activity (Manuscript 2). Moreover, the crucial role of the noradrenergic arousal system for the quality of extinction memories is highlighted in a review of relevant rodent and human studies (Manuscript 3). By using the described multi-methodological approach, we were able to demonstrate for the first time that peripheral arousal as well as fear-related theta oscillations are sensitive to E2. This was indicated by less fear responding (attenuated peripheral arousal and attenuated theta oscillations) during the recall of fear and extinction memories under high peripheral E2 levels (Manuscript 1). Concerning the role of fear extinction timing, we demonstrate that delayed extinction is advantageous over immediate extinction in reducing peripheral arousal during the recall of the extinction memory (Manuscript 2). Additionally, by disentangling oscillatory and non-oscillatory brain activity, we demonstrate for the first time that oscillatory and non-oscillatory brain activity is sensitive to fear expression. Moreover, by reviewing different rodent and human studies, we highlight the important role of noradrenergic arousal for the recall of extinction memories and, importantly, provide a detailed mechanistic framework of how extinction deficits might be caused after immediate extinction (Manuscript 3). In sum, the present thesis underscores the important role of E2, fear extinction timing, and the noradrenergic system for the recall quality of fear memories and extinction memories in humans.

Green light optimal speed advisory (GLOSA) systems require reliable estimates of signal switching times to improve vehicle energy/fuel efficiency. Deployment of successful infrastructure to vehicle communication requires Signal Phase and Timing (SPaT) messages to be populated with most likely estimates of switching times and confidence levels in these estimates. Obtaining these estimates is difficult for actuated signals where the length of each green indication changes to accommodate varying traffic conditions and pedestrian requests. This dissertation explores the different ways in which predictions can be made for the most likely switching times. Data are gathered from six intersections along the Gallows Road corridor in Northern Virginia. The application of long-short term memory neural networks for obtaining predictions is explored for one of the intersections. Different loss functions are tried for the purpose of prediction and a new loss function is devised. Mean absolute percentage error is found to be the best loss function in the short-term predictions. Mean squared error is the best for long-term predictions and the proposed loss function balances both well. The amount of historical data needed to make a single accurate prediction is assessed. The assessment concludes that the short-term prediction is accurate with only a 3 to 10 second time window in the past as long as the training dataset is large enough. Long term prediction, however, is better with a larger past time window. The robustness of LSTM models to different demand levels is then assessed utilizing the unique scenario created by the COVID-19 pandemic stay-at-home order. The study shows that the models are robust to the changing demands and while regularization does not really affect their robustness, L1 and L2 regularization can improve the overall prediction performance. An ensemble approach is used considering the use of transformers for SPaT prediction for the first time across the six intersections. Transformers are shown to outperform other models including LSTM. The ensemble provides a valuable metric to show the certainty level in each of the predictions through the level of consensus of the models. Finally, a hybrid approach integrating deep learning and controller logic is proposed by predicting actuations separately and using a digital twin to replicate SPaT information. The approach is proven to be the best approach with 58% less mean absolute error than other approaches. Overall, this dissertation provides a holistic methodology for predicting SPaT and the certainty level associated with it tailored to the existing technology and communication needs. / Doctor of Philosophy / Automated and connected vehicles waste a lot of fuel and energy to stop and go at traffic signals. The ideal case is for them to be able to know when the traffic signal turns green ahead of time and plan to reach the intersection by the time it is green, so they do not have to stop. Not having to stop can save up to 40 percent of the gas used at the intersection. This is a difficult task because the green time is not fixed. It has a minimum and maximum setting, and it keeps extending the green every time a new vehicle arrives. While this is good for adapting to traffic, it makes it difficult to know exactly when the traffic signal turns green to reach the intersection at that time. In this dissertation, different models to know ahead of time when the traffic signal will change are used. A model is chosen known as long-short term memory neural network (LSTM), which is a way to recognize how the traffic signal is expected to behave in the future from its past behavior. The point is to reduce the errors in the predictions. The first thing is to look at the loss function, which is how the model deals with error. It is found that the best thing is to take the average of the absolute value of the error as a percentage of the prediction if the prediction is that traffic signal will change soon. If it is a longer time until the traffic signal changes, the best way is to take the average of the square of the error. Finally, another function is introduced to balance between both. The second thing explored is how far back in time data was needed to be given to the model to predict accurately. For predictions of less than 20 seconds in the future, only 3 to 10 seconds in the past are needed. For predictions further in the future, looking further back can be useful. The third thing explored was how these models would do after rare events like COVID-19 pandemic. It was found that even though much fewer cars were passing through the intersections, the models still had low errors. Techniques were used to reduce the model reliance on specific data known as regularization techniques. This did not help the models to do better after COVID, but two techniques known as L1 and L2 regularization improved overall performance. The study was then expanded to include 6 intersections and used three additional models in addition to LSTM. One of these models, known as transformers, has never been used before for this problem and was shown to make better predictions than other models. The consensus between the models, which is how many of the models agree on the prediction, was used as a measure for certainty in the prediction. It was proven to be a good indicator. An approach is then introduced that combines the knowledge of the traffic signal controller logic with the powerful predictions of machine learning models. This is done by making a computer program that replicates the logic of the traffic signal controller known as a digital twin. Machine learning models are then used to predict vehicle arrivals. The program is then run using the predicted arrivals to provide a replication of the signal timing. This approach is found to be the best approach with 58 percent less error than the other approaches. Overall, this dissertation provides an end-to-end solution that uses real data generated from intersections to predict the time to green and estimate the certainty in prediction that can help automated and connected vehicles be more fuel efficient.

Yes / The civil war in South Sudan raises the all-too familiar problem of the crisis of state formation and nation-building in post-colonial Africa. Based on extensive field research in Sudan and South Sudan between 2005 and 2013, this chapter argues that the international response to post-independence nation-building and post-liberation-war peacebuilding was not predicated on coherent and consistent timing and sequencing. If anything, the case of South Sudan illustrates that the rather inconsistent, uncoordinated post-war peacebuilding and statebuilding, as well as the lack of domestic legitimacy and ownership of the post-liberation-war peacebuilding and nation-building interventions, aggravated the fundamental grievances leading to the outbreak of the December 2013 civil war. What is more, South Sudan demonstrates how events on the ground and the pursuit of the strategic interests of the key national, regional, and international stakeholders framed and determined the nature, scope, timing, and even the sequencing of post-war peacebuilding and nation-building.