Oscillation of Nonlinear Neutral Delay Differential Equations

2000 Mathematics Subject Classification: 34K15, 34C10.

Oscillation Criteria of Second-Order Quasi-Linear Neutral Delay Difference Equations

2000 Mathematics Subject Classification: 39A10.

Design of high channel-count fiber Bragg gratings using tailored group delay

A novel method of fiber Bragg grating design based on tailored group delay is presented. The method leads to designs that are superior to the previously reported results. © OSA 2012.

Trapping and unlimited delay of light pulses at microscale without distortion

A tunable bottle microresonator can trap an optical pulse of the given spectral width, hold it as long as the material losses permit, and release without distortion.

A static traffic assignment model combined with an artificial neural network delay model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Effect of delay of first interview and repetition of interview on accuracy and confidence of recall of a flashbulb -type memory

Hearing of the news of the death of Diana, Princess of Wales, in a traffic accident, is taken as an analogue for being a percipient but uninvolved witness to a crime, or a witness to another person's sudden confession to some illegal act. This event (known in the literature as a “reception event”) has previously been hypothesized to cause one to form a special type of memory commonly known as a “flashbulb memory” (FB) (Brown and Kulik, 1977). FB's are hypothesized to be especially resilient against forgetting, highly detailed including peripheral details, clear, and inspiring great confidence in the individual for their accuracy. FB's are dependent for their formation upon surprise, emotional valence, and impact, or consequentiality to the witness of the initiating event. FB's are thought to be enhanced by frequent rehearsal. FB's are very important in the context of criminal investigation and litigation in that investigators and jurors usually place great store in witnesses, regardless of their actual accuracy, who claim to have a clear and complete recollection of an event, and who express this confidently. Therefore, the lives, or at least the freedom, of criminal defendants, and the fortunes of civil litigants hang on the testimony of witnesses professing to have FB's. ^ In this study, which includes a large and diverse sample (N = 305), participants were surveyed within 2–4 days after hearing of the fatal accident, and again at intervals of 2 and 4 weeks, 6, 12, and 18 months. Contrary to the FB hypothesis, I found that participants' FB's degraded over time beginning at least as early as two weeks post event. At about 12 months the memory trace stabilized, resisting further degradation. Repeated interviewing did not have any negative affect upon accuracy, contrary to concerns in the literature. Analysis by correlation and regression indicated no effect or predictive power for participant age, emotionality, confidence, or student status, as related to accuracy of recall; nor was participant confidence in accuracy predicted by emotional impact as hypothesized. Results also indicate that, contrary to the notions of investigators and jurors, witnesses become more inaccurate over time regardless of their confidence in their memories, even for highly emotional events. ^

A Static Traffic Assignment Model Combined with an Artificial Neural Network Delay Model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Delay-sensitive service request scheduling for cloud computing

Cloud computing realizes the long-held dream of converting computing capability into a type of utility. It has the potential to fundamentally change the landscape of the IT industry and our way of life. However, as cloud computing expanding substantially in both scale and scope, ensuring its sustainable growth is a critical problem. Service providers have long been suffering from high operational costs. Especially the costs associated with the skyrocketing power consumption of large data centers. In the meantime, while efficient power/energy utilization is indispensable for the sustainable growth of cloud computing, service providers must also satisfy a user's quality of service (QoS) requirements. This problem becomes even more challenging considering the increasingly stringent power/energy and QoS constraints, as well as other factors such as the highly dynamic, heterogeneous, and distributed nature of the computing infrastructures, etc. ^ In this dissertation, we study the problem of delay-sensitive cloud service scheduling for the sustainable development of cloud computing. We first focus our research on the development of scheduling methods for delay-sensitive cloud services on a single server with the goal of maximizing a service provider's profit. We then extend our study to scheduling cloud services in distributed environments. In particular, we develop a queue-based model and derive efficient request dispatching and processing decisions in a multi-electricity-market environment to improve the profits for service providers. We next study a problem of multi-tier service scheduling. By carefully assigning sub deadlines to the service tiers, our approach can significantly improve resource usage efficiencies with statistically guaranteed QoS. Finally, we study the power conscious resource provision problem for service requests with different QoS requirements. By properly sharing computing resources among different requests, our method statistically guarantees all QoS requirements with a minimized number of powered-on servers and thus the power consumptions. The significance of our research is that it is one part of the integrated effort from both industry and academia to ensure the sustainable growth of cloud computing as it continues to evolve and change our society profoundly.^

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Determinação da energia de ativação e pureza de medicamentos de referência, genérico e similar utilizando técnicas termoanalíticas

In this work, the reference drugs, generic and similar to the active ingredients acetylsalicylic acid, paracetamol, captopril, hydrochlorothiazide and mebendazole were purchased from local pharmacies and studied by thermogravimetry (TG) and Differential Scanning Calorimetry (DSC). Thermal decomposition was assessed to obtain from the Ozawa method the activation energy in inert atmosphere (nitrogen), using three different heating ratios (5, 10 and 20 o C min-1). The pharmaceutical formulation of the AAS reference was the one who presented different from the others (generic and similar) Thermogravimetric profile indicating likely interaction between the active ingredient and excipients. Was observed at the heating rate of the inverse temperature that no linearity of the data, ie, there was no correlation between the percentage of mass loss and the activation energy involved in the thermal decomposition of the pharmaceutical formulation of the AAS reference log graph. The analysis by differential scanning calorimetry was performed in nitrogen atmosphere with a heating rate of 10 ° C min-1. In the analysis of these same drugs, the data curves found on the melting point were, except for hydrochlorothiazide, are consistent with the literature. Hydrochlorothiazide presented a melting point well below that found in the literature, which may be justified due to the interaction of the active ingredient with the excipient lactose. In the study of purity, using the Van't Hoff equation, the reference drugs hydrochlorothiazide and mebendazole reference generic and showed similar impurity content below the limit established that this equation must be greater than 2.5 mol%

Técnica para segmentação automática de imagens microscópicas de componentes sanguíneos e classificação diferencial de leucócitos baseada em lógica fuzzy

Automatic detection of blood components is an important topic in the field of hematology. The segmentation is an important stage because it allows components to be grouped into common areas and processed separately and leukocyte differential classification enables them to be analyzed separately. With the auto-segmentation and differential classification, this work is contributing to the analysis process of blood components by providing tools that reduce the manual labor and increasing its accuracy and efficiency. Using techniques of digital image processing associated with a generic and automatic fuzzy approach, this work proposes two Fuzzy Inference Systems, defined as I and II, for autosegmentation of blood components and leukocyte differential classification, respectively, in microscopic images smears. Using the Fuzzy Inference System I, the proposed technique performs the segmentation of the image in four regions: the leukocyte’s nucleus and cytoplasm, erythrocyte and plasma area and using the Fuzzy Inference System II and the segmented leukocyte (nucleus and cytoplasm) classify them differentially in five types: basophils, eosinophils, lymphocytes, monocytes and neutrophils. Were used for testing 530 images containing microscopic samples of blood smears with different methods. The images were processed and its accuracy indices and Gold Standards were calculated and compared with the manual results and other results found at literature for the same problems. Regarding segmentation, a technique developed showed percentages of accuracy of 97.31% for leukocytes, 95.39% to erythrocytes and 95.06% for blood plasma. As for the differential classification, the percentage varied between 92.98% and 98.39% for the different leukocyte types. In addition to promoting auto-segmentation and differential classification, the proposed technique also contributes to the definition of new descriptors and the construction of an image database using various processes hematological staining

O ciclo indiano de Marguerite Duras à luz da comparação diferencial: a reconfiguração de um personagem

La présente recherche a pour objectif analyser la reconfiguration d'un personnage dans deux romans et dans un texte dramatique de l’écrivaine française Marguerite Duras, qui font partie du Cicle indien. Ils sont : Le Ravissement de Lol V. Stein (1964), Le vice-consul (1965) e India Song (1975). Adoptant la perspective de la Comparaison Différentielle comme approche littéraire, proposée par Ute Heidmann (2008, 2010, 2012), professeur-docteur de l'Université de Lausanne (Suisse), ce travail cherche étudier le personnage Anne-Marie Stretter présent dans ces trois œuvres. On cherche comprendre de quelle manière se produit la réécriture du personnage à partir de trois axes d'analyse : les modalités de l'énonciation, en considérant l'analyse et la comparaison de l'œuvre littéraire comme un événement énonciatif, partant du concept de la scène d'énonciation, proposé par Dominique Maingueneau (2010) ; les modalités d'inscription générique, étroitement liées aux modalités énonciatives, avec un fondement théorique en Todorov (1980) et Heidmann (2012) ; et les modalités de dialogisme intertextuel et interdiscursif, considérant dans la relation dialogique des nouveaux et différentes propositions de sens. Para la compréhension de cela nous partons de Bakhtine (2010), Kristeva (1974), Todorov (1981) et de l'idée de dialogisme et intertextualité, pour arriver à l'idée d'interdiscours proposée par Heidmann (2010, 2012). À partir de cette étude, nous pouvons observer dans l'œuvre d'un même auteur différentes manières de construction d'un personnage, chacun avec ses spécificités et complexités, variant dans le genre et espaces discursifs. L'intérêt de la comparaison différentielle dans cette étude, partant d'un trait commun entre les trois œuvres, est de rendre visible les différences épistémologiques et les nouvelles propositions de sens.

Semantikos: proposta de interfaces para aplicativo mediador de técnica de diferencial semântico em mobile tablet

This research aims to systematize a proposal of developing a mobile tablet application in order to help implementing the Semantic Differential technique – SD, under the approach of Participatory Design. In 1975, Osgood et al. created the Semantic Differential technique. Since then, many experiments use it to measure the affective perception of individuals concerning objects and concepts by means of compounded scales of bipolar adjectives, based on the theoretical models that support the technique: the conductible, spatial and metric models. During the application of the technique with potential users, the researcher must simultaneously manage several contexts, that is, audio recorder, when authorized, and observe and record spontaneous reports of the respondent. It is noticeable that often occurs a cognitive overload during this event. Thus, the use of a single application whose interface is assigned to its users and respondents could assist researchers in applying the SD technique. This research aimed to understand the processes inherent to the task of implementing the Semantic Differential technique and obeyed the following steps: a) training of users, b) background questionnaire c) interview with Focus Group, and d) cooperative evaluation. Besides these procedures, one can also observe the degrees of facilitation or difficulty concerning the use of the conventional model, which is the development and application of scales with the aid of printed material, pencil, pens, clipboards, and recorder software for editing the document and data analysis. This paper comprises reactions and impressions from the experiences of users of SD technique. Considering the data recollected from the user’s observation, the hypothesis of the experiment proved to be right. It means that the development of the application for mobile tablet employing the technique of Semantic Differential is viable, since it assembles all the steps in one only tool, increases the accomplishment of the task between user/researcher and user/respondent resulting in their mutual satisfaction.

Estudo experimental sobre o comportamento do escoamento bifásico água - ar para a medição de vazão em placa de orifício

The measurement of flow through the prediction of differential pressure is widely used in industrial day-to-day, this happens mainly due to the fact that it is used for various types of fluids, such as gas flow and liquid with viscosity distinct even flow of fluids with particles in suspension. The suitability of this equipment for measuring mass flow in two-phase flow is of paramount importance for technological development and reliability of results. When it comes to two-phase flow the relationship between the fluids and their interactions are of paramount importance in predicting the flow. In this paper, we propose the use of concentric orifice plate used in small diameter pipes of 25.4 mm order where a two-phase flow flows between water-air. The measurement of single-phase flow was made with the use of data in NBR 5167-1 which was used to Stolz equation for measuring discharge coefficient. In the two-phase flow was used two correlations widely used in the prognosis of mass flow, the pattern of Zhang (1992) and the model of Chisholm (1967), to the homogeneous flow model. It was observed that the behavior found in Zhang model are consistent more realistic way the mass flow of two-phase flow, since the model Chisholm extrapolate the parameters for the downstream pressure P2, the orifice plate, and the rated discharge coefficient. The use of the change in pressure drop P1-P2 and discharge coefficient, led to a better convergence of the values obtained for the two-phase air-water stream.