<p> This thesis is a discussion and analysis of the piece I presented at Signal Flow, the graduate music festival at Mills College, on March 8th, 2018 called pls don&rsquo;t(!) silence ur cellphones. This analysis will be punctuated with various theoretical asides meant to shed light on different aspects of the work and to present a clearer view of my own artistic mission. Since one of the hardest parts of doing work similar to mine is finding the right ways to do it, I hope this paper will at least serve as a resource for those with similar goals. In the spirit of open source, I want the tools and the processes to be as transparent as possible in order to encourage other artists and to expand a now relatively small community. My piece uses only the audience&rsquo;s cellphones as sound sources. I use a centrally-located projected display supplemented by the cellphone displays themselves to choreograph the audience&rsquo;s movements around the space. The piece aims to encourage interesting, fulfilling interactions for the audience while producing a complex sonic result through these interactions. In doing this, there arose many intersecting (and often extramusical) concerns and issues that I needed to address. This paper then serves largely to examine the failures and successes of this pursuit in the hopes of outlining future directions for the project. For this piece, audience members are invited to log onto a website on their cellphone. This website contains a brief set of instructions as well as a &ldquo;start&rdquo; button which, when pressed, activates a Web Audio app which produces sound. Each phone then basically becomes a mobile speaker in a large speaker array composed of the aggregate of all of the audience members&rsquo; phones. The interface on the phones is intentionally spare and minimal in order to encourage audience members to keep their focus elsewhere; it merely displays a solid block of color indicating which group the audience member belongs to at different parts of the piece and flashes white briefly when a new instruction is sent out. The main interface which all audience members react to is a projected image which acts as a sort of topographical map of the performance space. The audience is directed through a sequence of different spatial orientations which are accompanied by changes in the sonic material presented on their phones. The main goals of the piece are 1) to quickly and cheaply create an accessible &ldquo;high-tech&rdquo; listening experience, 2) to encourage and foster social contact (while problematizing and questioning the role of instruction and suggestion), and 3) to present various compositional ideas which are inherent to the work&rsquo;s form and sonic affect.</p><p>

<p> Although the traditional intrusion detection problem has been well studied with the release of the KDD&rsquo;99 and NSL-KDD datasets, recent intrusion detection has expanded to include wireless 802.11 networks and Industrial Control Systems &amp; Supervisory Control and Data Acquisition (ICS/SCADA) systems. This research investigates the application of two novel models to multi-domain intrusion detection. The first model is hybrid ensemble that uses complementary-based diversity measures in an efficient greedy search pruning process. The proposed hybrid ensemble is constructed from a heterogeneous combination of decision tree and Naive Bayes classifiers and evaluated for intrusion detection performance on an 802.11 wireless system, a power generation system, and a gas pipeline system. The second model is based on a one-versus-all (OVA) binary framework comprising multiple nested sub-ensembles. To provide good generalization ability, each sub-ensemble contains a collection of sub-learners, and only a portion of the sub-learners implement boosting. A class weight based on the sensitivity metric (true positive rate), learned from the training data only, is assigned to the sub-ensembles of each class. The second model is applied to traditional and 802.11 wireless network intrusion detection. Overall, the proposed models achieve higher detection rates and good overall false positive performance when evaluating the model compared to state-of-the-art methods for effective multi-domain intrusion detection.</p><p>

<p> Classification is a ubiquitous decision activity. Regardless of whether it is predicting the future, e.g., a weather forecast, determining an existing state, e.g., a medical diagnosis, or some other activity, classifier outputs drive future actions. Because of their importance, classifier research and development is an active field.</p><p> Regardless of whether one is a classifier developer or an end user, evaluating and comparing classifier output quality is important. Intuitively, classifier evaluation may seem simple, however, it is not. There is a plethora of classifier summary statistics and new summary statistics seem to surface regularly. Summary statistic users appear not to be satisfied with the existing summary statistics. For end users, many existing summary statistics do not provide actionable information. This dissertation addresses the end user's quandary. </p><p> The work consists of four parts: 1. Considering eight summary statistics with regard to their purpose (what questions do they quantitatively answer) and efficacy (as defined by measurement theory). 2. Characterizing the classification problem from the end user's perspective and identifying four axioms for end user efficacious classifier evaluation summary statistics. 3. Applying the axia and measurement theory to evaluate eight summary statistics and create two compliant (end user efficacious) summary statistics. 4. Using the compliant summary statistics to show the actionable information they generate.</p><p> By applying the recommendations in this dissertation, both end users and researchers benefit. Researchers have summary statistic selection and classifier evaluation protocols that generate the most usable information. End users can also generate information that facilitates tool selection and optimal deployment, if classifier test reports provide the necessary information. </p><p>

In this dissertation, I develop a new theoretical framework for the modeling, pricing analysis, and computation of solutions to electric power supply chains with power generators, suppliers, transmission service providers, and the inclusion of consumer demands. In particular, I advocate the application of finite-dimensional variational inequality theory, projected dynamical systems theory, game theory, network theory, and other tools that have been recently proposed for the modeling and analysis of supply chain networks (cf. Nagurney (2006)) to electric power markets. This dissertation contributes to the extant literature on the modeling, analysis, and solution of supply chain networks, including global supply chains, in general, and electric power supply chains, in particular, in the following ways. It develops a theoretical framework for modeling, pricing analysis, and computation of electric power flows/transactions in electric power systems using the rationale for supply chain analysis. The models developed include both static and dynamic ones. The dissertation also adds a new dimension to the methodology of the theory of projected dynamical systems by proving that, irrespective of the speeds of adjustment, the equilibrium of the system remains the same. Finally, I include alternative fuel suppliers, along with their behavior into the supply chain modeling and analysis framework. This dissertation has strong practical implications. In an era in which technology and globalization, coupled with increasing risk and uncertainty, complicate electricity demand and supply within and between nations, the successful management of electric power systems and pricing become increasingly pressing topics with relevance not only for economic prosperity but also national security. This dissertation addresses such related topics by providing models, pricing tools, and algorithms for decentralized electric power supply chains. This dissertation is based heavily on the following coauthored papers: Nagurney, Cruz, and Matsypura (2003), Nagurney and Matsypura (2004, 2005, 2006), Matsypura and Nagurney (2005), Matsypura, Nagurney, and Liu (2006).

Several models in econometrics and finance have been proven to be computationally intractable due to their complexity. In this dissertation, we propose an evolutionary-genetic-algorithm for solving these types of problems. We extend the models so that less restrictive assumptions are required and we cope with the increased complexity by using a modified version of the evolutionary algorithm proposed for the simpler cases. More specifically, we study closer the estimation of switching regression models as introduced by Quandt (1958). The applicability of the proposed algorithms is examined through disequilibrium models; models that provide supply and demand functions for markets, when the price is not adjusted so that the quantity supplied equals the quantity demanded. We focus on the computational aspect of the deterministic switching regression models and we suggest a self-evolving genetic algorithm for solving these types of problems. As an illustration, we present results from Monte Carlo simulations and thereafter we apply the algorithm to the disequilibrium model proposed for the gasoline market during the “energy crisis”. We further extend the “general model” for markets in disequilibrium by incorporating dynamic relationships, and we examine the applicability of the proposed genetic algorithm in this more complex and realistic problem. Subsequently, the proposed genetic algorithm for the markets in disequilibrium is applied to financial models, where the structure and computational complexity are comparable with those of the switching regression models. As example, we apply the algorithm to minimizing portfolio tracking error with respect to a pre-specified index. The proposed genetic algorithm possesses unique characteristics that maximize the fitness of the algorithm itself for each individual problem. This is achieved through a Self-Evolving process that teaches the genetic algorithm what internal parameters improve the algorithm's fitness.

Flow injection and liquid chromatographic sample introduction techniques have been coupled to inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP-OES). The analytical performances of these combinations have been evaluated. Several applications have also been developed. The fundamental FI parameter, response dispersion coefficient (D$\rm\sb{r}),$ has been studied for various elements with FI-ICP-MS in multi-element detection mode. Some interesting phenomena were observed and possible explanations have been made. A recirculating loop FI manifold which provides on-line successive dilutions has been coupled to ICP-MS. The analytical performance of the manifold was evaluated. The dilution behavior of various elements was examined. The manifold was successfully applied to the recovery study of cerium in wine. Factors affecting the speed of the dilution process were also investigated. High performance liquid chromatography including ion exchange chromatography (IC) and reverse phase liquid chromatography have also been coupled to ICP-MS for selenium speciation in selenium-enriched vegetables. The methods developed efficiently identified and semi-quantified several biologically important selenium species. The advantages and disadvantages of these methods were addressed as well. Flow injection sample introduction has also been coupled to an axially viewed ICP-OES with a segmented - array charge - coupled device detector. Various traditional pneumatic nebulizer-spray chamber systems have been examined. The best nebulizer-spray chamber combination has been identified for FI-ICP-OES. Some experiments and data treatments have been conducted under the guidance of simulation in order to achieve better analytical figures of merit.

The high efficiency of capillary electrophoresis (CE) coupled with universality of direct UV absorbance detection allowed the quantification of the following matrices: (1) oxalate in numerous biological systems such as: human albumin, urine, parental nutrition solutions (intralipid infusion, multi-vitamin infusion), fifteen premature baby formulas produced by the two leading baby food companies, commercial skim and 1% fat milk emulsions; (2) histidine-containing antioxidants, carnosine and anserine, in pork and chicken muscle tissue extracts; (3) melatonin in the presence of the products of tryptophan metabolism and (4) taxol in the presence of such interferents as cephalomannine and baccatine III. The importance of accurate oxalate quantification in various biological systems is directly connected to the formation of kidney and, possibly, gall bladder stones in premature babies and adults, since oxalate is a direct precursor in the formation of calcium oxalate, the final product of metabolism of oxalic acid. The method developed allowed quantification of underivatized samples with a 6 ppb detection limit for oxalate using direct photometric detection. The naturally occurring histidine-containing dipeptides, carnosine and anserine, are directly responsible for the following properties of muscle tissues of pork, chicken, fish, etc.: freshness, color, texture, and flavor. The present methods of antioxidant quantification based on HPLC with fluorescence detection lack sensitivity, specificity and reproducibility. A reliable CE method using direct UV absorbance detection was developed for fast, accurate, reproducible, sensitive and selective quantification of the analytes at ppm levels in the muscle extracts and at ppb levels in aqueous standards without derivatization of the muscle extracts. Much attention has been focused lately on the healing properties of the naturally occurring hormone melatonin. The analyte is currently quantified by HPLC with fluorescence and/or electrochemical detection, which, unfortunately, lacks specificity and sensitivity for melatonin in the presence of tryptophan and its products of metabolism. An attempt was made to develop a specific, sensitive, fast and simple method of analyte quantification without any kind of sample derivatization, solid phase and/or liquid phase extraction, and/or preconcentration. This allowed the separation and quantification of melatonin in the presence of major HPLC interferents such as tryptophan, hydroxytryptophan, and seratonin. Taxol, a highly functionalized taxane diterpene amide, has recently emerged as an effective chemotherapeutic agent for treatment of ovarian tumors, breast carcinomas and malignant melanoma. Micellar electrokinetic chromatography was successfully applied to separate several taxanes in about 11 minutes using minute amounts of sample and minimal amounts of buffer to decrease the solvent waste. (Abstract shortened by UMI.)

This work is devoted to the study of a class of queueing networks with certain properties, namely those with discontinuous performance measures. An important subclass consists of networks with real-time constraints, where jobs must arrive at their destination within given deadlines, otherwise they are considered lost. Performance in such networks is measured by the probability a job is lost. For a network of K parallel links with a probabilistic routing scheme, consider the problem of optimal routing. Assuming the availability of a closed-form expression for the probability of loss across each link, the problem is solved under general conditions and properties of the optimal flow allocation are given. However, an analytical expression often cannot be derived, in which case other optimization approaches are necessary. One alternative is to use approximate analysis. Two suboptimal heuristic routing schemes are presented and compared. A second alternative involves implementing an on-line gradient-based stochastic optimization algorithm. Such algorithms require performance gradient estimates with respect to the control parameter. The methods of Perturbation Analysis (PA) and the Likelihood Ratio (LR) have been developed to supply the derivative estimates required. Smoothed Perturbation Analysis (SPA) is a specific PA approach suited to gradient estimation problems with discontinuous sample performance functions. SPA overcomes the normal difficulty of discontinuities by defining alternative sample performance functions that are conditioned forms of the original sample functions, such that the discontinuities are smoothed out. Here, a new SPA approach is developed that extends the applicability of SPA to cover additional performance measures including the probability of loss in real-time networks. New SPA gradient estimators are presented for this and other problematic performance measures such as the probability an arriving customer finds an idle server and the network occupancy as seen by an arrival to a tandem network. The convergence rate of SPA and LR gradient estimators are compared experimentally. Furthermore, the effectiveness of SPA gradient estimation in on-line stochastic optimization problems is demonstrated by coupling an SPA algorithm with a sampling-controlled stochastic version of Gallager's algorithm for the routing problem described above.

Queueing Theory has been successfully and extensively applied to the scheduling, control, and analysis of complex stochastic systems. In this dissertation, the problems of optimal scheduling, control and analysis of bulk service queueing systems are studied. A Dynamic Programming formulation is provided for the optimal service strategy of a two-server bulk queue. An extension of the general bulk service rule is shown to be optimal in the sense of minimizing either the finite discounted or average waiting cost. It is shown that the optimal dispatching rule is a multi-stage threshold type where servers are dispatched only when the number of waiting customers exceeds certain threshold values depending both on the number of waiting customers and the number of servers available at decision epochs. It is conjectured that the result is extendable to the case for more than two servers. Exact analysis of the state probability in equilibrium is carried out under the optimal policy obtained for a queue with two bulk servers. Comparison of the optimal threshold policy is carried out by evaluating a single stage vs. a two-stage threshold two-server system. By calculating the mean number of customers waiting in the queue of both systems, it is shown that a two-stage threshold policy yields optimal performance over the general bulk service rule under any operating condition. Examples for different parameter sets are provided. A network of two bulk service queues served by a common transport carrier with finite capacity is analyzed where the general bulk service rule is applied only at one queue. Decomposition is employed to provide an exact analysis of the steady-state probability distribution, mean waiting time distribution, and mean number of customers waiting at both queues in equilibrium. Networks of more than two bulk service queues can be analyzed by direct extension of the methodology. An optimization procedure for the optimal threshold value to minimize total mean waiting cost is also discussed.

Today's Continuous Flow Manufacturing (CFM) systems are the integration of several manufacturing components found in traditional manufacturing facilities. They contain groups of machines (facilities, work centers or cells) linked together by transport systems (material handlers, conveyors, etc.). Series of parts are transported from one facility to the next depending on operation sequences. Usually, a major problem encountered in the optimal design of a CFM system is the assignment of these manufacturing components onto appropriate locations in the layout to obtain efficient CFM configurations with preferable flow of products and resource utilization. In this research, optimal operation allocations to groups of machines in a facility is termed the Resource Assignment Subproblem or RAS. The task of locating facilities onto predefined locations in the layout, taking into consideration operation sequences, is termed the Location Assignment Subproblem or LAS. Both the RAS and LAS generally involve complicated discrete mathematical models, thus, many past researchers have chosen to investigate them separately. Recent research conducted by Ketcham (1992a) has led to a mathematical representation which integrates LAS and RAS into a single model, called the Configuration Problem (CP). The solution method, which is a heuristic called the Resource Assignment algorithm, is also found to provide acceptably good solutions for complex models using reasonable computational efforts. The current implementation of the Resource Assignment algorithm, however, becomes inadequate for today's large-scale CFM facilities, where many products are produced at different sites. Thus, enhanced methodologies are developed in this research so that systems of various complexity and characteristics can be optimized more efficiently. The study of algorithm performance and solution characteristics has led to several enhancement techniques. The collection of these techniques, called the Meta-algorithm is a set of decision rules that suggests an optimization strategy, for the Resource Assignment algorithm, based on the characteristics of a given CFM system. The robustness of the Meta-algorithm is tested against a wide range of trial cases representing large-scale CFM systems found in industrial practice. Overall improved performance has been achieved by the Meta-algorithm, which is found most effective for segmenting and solving large-scale problems infeasible for the existing Resource Assignment algorithm.