Knowledge, Strategy, and Momentum in Presidential Primaries

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1432 A Combined Electrical and Mechanical Engineering Senior Design Laboratory M. E. Parten and D. L. Vines T. T. Maxwell and J. C. Jones Electrical Engineering Mechanical Engineering Texas Tech University Lubbock, TX 79409 Abstract This paper describes a multidisciplinary capstone design laboratory course offered in the Electrical and Mechanical Engineering Departments at Texas Tech University. The course uses projects from industry, research efforts and other faculty initiatives. The projects for the course come from industry, research efforts and other faculty initiatives. The courses are team taught by both departments. This paper describes the structure of the program along with example projects and results. Both positive and negative aspects of the program are presented. Introduction In the fall semester of 1994 the Electrical and Mechanical Engineering Departments at Texas Tech University began a multidisciplinary senior design project laboratory program. Two courses were established by integrating the Electrical Engineering Department's Senior Project Laboratory courses (two 3-semester credit hour courses) with the Mechanical Engineering Department's Design I and II capstone design courses (two 3-semester credit hour courses). The Electrical Engineering Department has a long history of project laboratories.1-5 The Mechanical Engineering Department has been involved in alternative fueled vehicles for a number of years. Both departments had worked together on a number of special projects and felt the need, as have many others6-11, for an increased interdisciplinary program for engineering students. The goals of these new courses were: to have the students develop an understanding of engineering design projects from recognition of a need and definition of design objectives through completion of the project to foster student creativity to broaden the students concept of engineering problems to include other engineering disciplines and other nonengineering factors that have an impact on the final problem solution to provide a unique educational experience for students on project teams to enhance the students communication skills The projects for the course come from industry, research efforts and other faculty initiatives.

Inferring Strategic Voting

Row over tactical voting site

Guest Editorial: Situational awareness of integrated energy systems

Strategic voting in the 1984 democratic presidential primaries

This research aims to determine the warehouse receipt system policy strategy partially and globally that occurs between the ideal and actual types in the field of seaweed receipt system (SRG) in South Sulawesi. Using dynamic systems. The study aims to illustrate the ideal type of SRG, illustrate facts and realities in the field, analyze the SRG gap in South Sulawesi (ideal type and facts in the field), and SRG gap scenario both globally and partially. Held in August 2019- August 2021. The research was conducted in 3 regencies in South Sulawesi, including Gowa Regency, and East Luwuk Regency. The results showed that the income obtained from the actual (existing) model using SRG is higher than direct income (selling grain directly) without entering the warehouse (SRG), as well as real income (where 100% of grain entering the warehouse is directly sold at a time without delaying sale). The results showed that there is a difference in revenue earned (lost profit / benefit loss) if it does not optimize the existence of warehouses, in other words that the average revenue loss per year is 177.64 billion. The model shows that by delaying sales, there is a difference in profit (difference in revenue) of 36 billion to 320 billion or an average of 177.64 billion per year. The scenario model was developed with the ideal model optimization considerations, namely optimizing the difference in income obtained from the 30% delay in selling with strategies to increase agricultural productivity through increasing farmer productivity, obtaining reliable price information, adequate storage space, efficient quality testing procedures, multi-layer intermediaries, absence of hidden costs, and transportation costs of conducting training and socialization of training / training or field schools about Warehouse Receipt Systems. It appears that grain production is increasing very well. It appears that production in 2021 increased by 2,275.13 tons or up about 0.56% from the previous year. This condition indicates that the well-developed scenario model provides significant improvement.

Riker's view of the dynamical forces of political systems singles out disequilibrium, strategic voting and agenda‐manipulation as the pervasive features determining the nature of political change. We shall investigate these characteristics in some detail and thereafter evaluate the methodological aspects of Riker's theory. Some observations on the general relevance of the equilibrium analysis of multi‐dimensional spatial voting models for the study of political institutions are also made.

This article deals with strategic voting under incomplete information. We propose a descriptive model, inspired by political elections, where the information about the vote intentions of the electorate comes from public opinion polls and a social network, modeled as a graph over the voters. The voters are assumed to be confident in the poll and they update the communicated results with the information they get from their relatives in the social network. We consider an iterative voting model based on this behavior and study the associated “poll-confident” dynamics. In this context, we ask the question of manipulation by the polling institute.

Reservoir simulation models aim to reproduce at well, sector and field level the pressure and production behavior observed in the historical data. The size and resolution of the models are essentially capped by the computational resources as the numerical computations are quite complex and hardware demanding. For this reason, the use of simulation models to understand inter-field communications at regional level have been always a challenge, rarely pursued, referring those analyses to simple material balance to evaluate influxes, lacking lateral vectors to identify where volumes are coming from, especially on cases of multiple field interactions. The work presented in this paper illustrates the value of merging existing field level simulations models into a large scale regional simulation grids, in order to understand pressure disturbances observed in multiple fields Offshore Abu Dhabi. The process of merging simulation models represents a big challenge considering the high variety of approaches used in the original models, different geology complexity, fluid characteristics, different depletion regimes and field development strategies. In this study, thousands of wells, 6 structures with different fluid and equilibrium regions were used to build the biggest reservoir simulation model in Abu Dhabi. The integration of the data pursues the replication of the existing static and dynamic models, addressing in parallel lateral and vertical upscaling issues when moving from very fine into coarser grids. Implications on the change of scale on the repeatability of the HCIIP volumes and the impact of pseudo relative permeability curves on the history match were carefully analyzed during the process. Evaluation of the impact of the simplifications over the overall quality of the model was of paramount importance, interrogating whether the simplifications affects the capability of the model for assessing the pressure communication and influxes among the fields. The regional simulation model allowed to understand the effects of the peripheral water injection of a giant field on the nearby satellite fields, also the effects of these interactions on the pressure and oil saturation changes through time. Fields and Structures separated way far (20 and 40 Km away) can eventually see pressure disturbances after very long periods of time (up to 300 psi in couple of decades in some cases). Although evidences for changes in pressure are very clear and supported by RFT/MDT time lapsed data, the work also proved that changes on saturations are not very evident or can be considered very marginal on fields separated by large distances. This work represents an alternative and more accurate approach for evaluating nearby field communications and to quantify the boundary conditions to restore models at original stage before nearby interferences, allowing proper initialization of the fine scaled simulation models on pre-production status.

Electrical-circuit schematics are a foundational tool in electrical engineering. A method that can automatically digitalize them is desirable since a knowledge base of such schematics could preserve their functional information as well as provide a database that one can mine to predict more operationally efficient electrical circuits using data analytics and machine learning. We present a workflow that contains a novel pattern-recognition methodology and a custom-trained Optical Character Recognition (OCR) model that can digitalize images of electrical-circuit schematics with minimal configuration. The pattern-recognition and OCR stages of the workflow yield 86.4% and 99.6% success rates, respectively. We also present an extendable option toward predictive circuit-design efficiencies, subject to a large database of images being available. Thereby, data gathered from our pattern-recognition workflow are used to draw network graphs, which are in turn employed to form matrix equations that contain the voltages and currents for all nodes in the circuit in terms of component values. These equations could be applied to a database of electrical-circuit schematics to predict new circuit designs or circuit modifications that offer greater operational efficiency. Alternatively, these network graphs could be converted into simulation programs with integrated circuit emphasis netlists to afford more accurate and computationally automated simulations.

Plan recognition strategies for language understanding

Detecting motor bearing faults by stator currents is of great importance as it improves the adaptability of measurement means to different environments and reduces the number of sensors. Therefore, many studies have been conducted to investigate bearing faults by constructing motor models, most of which have used signal models to simulate the dynamics of the bearings. However, the signal model may be exposed to the issue that the nonlinearities in the bearing operation are neglected, thus oversimplifying the coupling effects between the electromagnetic and dynamics models. Hence, a coupled electromagnetic–dynamic modeling method for induction motors based on multiple coupled circuit theory and the rotor-bearing dynamics model is proposed in this study to implement the coupled simulation of electromagnetic and dynamic models. The air gap length and rotor velocity are used as coupled parameters for the calculation of stator–rotor mutual inductance and ball contact deformation, respectively. The simulation results show that the proposed model can effectively implement the electromagnetic–dynamic coupled and reflect the bearing fault characteristics in the current signal. Experiments were conducted on induction motors with typical winding configurations under laboratory conditions. The comparison results verify the effectiveness of the proposed modeling method.

The responses of visual cortical neurons during fixation tasks can be significantly modulated by stimuli from beyond the classical receptive field. Modulatory effects in neural responses have also been recently reported in a task where a monkey freely views a natural scene. In this article, we describe a hierarchical network model of visual recognition that explains these experimental observations by using a form of the extended Kalman filter as given by the minimum description length (MDL) principle. The model dynamically combines input-driven bottom-up signals with expectation-driven top-down signals to predict current recognition state. Synaptic weights in the model are adapted in a Hebbian manner according to a learning rule also derived from the MDL principle. The resulting prediction-learning scheme can be viewed as implementing a form of expectation-maximization (EM) algorithm. The architecture of the model posits an active computational role of the reciprocal connections between adjoining visual cortical areas in determining neural response properties. In particular, the model demonstrates the possible role of feedback from higher cortical areas in mediating neurophysiological effects due to stimuli from beyond the classical receptive field. Simulations of the model are provided that help explain the experimental observations regarding neural responses in both free viewing and fixation conditions.

Intelligent recuperation mode represents a novel approach to elderly care, leveraging the capabilities of the Internet of Things (IoT) and big data. In this paper, based on the SOM-improved K-means algorithm, combined with association rule algorithm mining and analysis with time constraint factor, we establish an analysis model of elderly behavior recognition and make a preliminary judgment on the health status of older people based on the physiological parameters of the model. At the same time, an outlier detection algorithm based on normal distribution is being used to evaluate the abnormal behavior of elderly individuals and an intelligent recreational home ecosystem is being built and tested. In the evaluation of functional modules, except for the nutrition management and lighting control modules, which are rated as “average,” the evaluation values of the remaining eight modules are all in the range of [3,4], and their evaluation ratings are “good” and above. The evaluation values of all dimensions in the comprehensive evaluation of functions are all greater than 3, among which the evaluation values of the dimensions of perceived usefulness and tendency to use are 4.04 and 4.17, respectively, and the evaluation grade reaches the level of “excellent.” The average satisfaction value of the test experience is 3.77, which is 0.98 higher than the satisfaction value of using traditional household products.

Models of opinion dynamics describe how opinions are shaped in various environments. While these models are able to replicate general opinion distributions observed in real-world scenarios, their capacity to align with data at the user level remains mostly untested. We evaluate the capacity of the multistate voter model with zealots to capture individual opinions in a fine-grained Twitter dataset collected during the 2017 French presidential elections. Our findings reveal a strong correspondence between individual opinion distributions in the equilibrium state of the model and ground-truth political leanings of the users. Additionally, we demonstrate that discord probabilities accurately identify pairs of like-minded users. These results emphasize the validity of the voter model in complex settings and advocate for further empirical evaluations of opinion dynamics models at the user level.