Delve into the intricate world of travel demand modeling systems, where complexity arises from dynamic feedback, stochastic effects, uncertainty, and system structure. Discover the balance needed to minimize complicatedness while maximizing behavioral complexity in regional travel modeling. Uncover

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Explore the innovative methods and practical applications of Stochastic Storm Transposition (SST) in the context of HEC-HMS. Delve into the history, fundamentals, simulation procedures, and benefits of using SST for watershed-averaged precipitation frequency analysis. Learn about the non-parametric

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The U.S. Army Modeling and Simulation Office (AMSO) serves as the lead activity in developing strategy and policy for the Army Modeling and Simulation Enterprise. It focuses on effective governance, resource management, coordination across various community areas, and training the Army Analysis, Mod

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This presentation unveils the Capacity Zone modeling calculations for Forward Capacity Auction 17 associated with the 2026-2027 Capacity Commitment Period by ISO-NE PUBLIC. It delves into boundary definitions, import-constrained zone modeling, and market rules guiding the assessments and modeling pr

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This document delves into the modeling, optimization, and simulation of power distribution systems, specifically focusing on Distribution Feeder Modeling and Analysis. It covers the components of a typical distribution feeder, series components, Wye-Connected Voltage Regulator modeling, and equation

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Data modeling involves exploring data-oriented structures, identifying entity types, and assigning attributes similar to class modeling in object-oriented development. Object models should not be solely based on existing data schemas due to impedance mismatches between object and relational paradigm

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Workshop on joint efforts between IEEE 802 and ITU-T Study Group 15 focused on information modeling, data modeling, and system control in the realm of transport systems and equipment. The mandate covers technology architecture, function management, and modeling methodologies like UML to YANG generat

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Geometric modeling in computer-aided design (CAD) is crucially done in three key ways: wireframe modeling, surface modeling, and solid modeling. Wireframe modeling represents objects by their edges, whereas surface modeling uses surfaces, vertices, and edges to construct components like a box. Each

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Mathematical modeling plays a crucial role in solving engineering problems efficiently. Numerical methods are powerful tools essential for problem-solving and learning. This chapter explores the importance of studying numerical methods, the concept of mathematical modeling, and the evaluation proces

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Dynamic Structural Equation Modeling (DSEM) is a powerful analytical tool used to analyze intensive longitudinal data, combining multilevel modeling, time series modeling, structural equation modeling, and time-varying effects modeling. By modeling correlations and changes over time at both individu

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Introducing xtsfkk, a new Stata command for fitting panel stochastic frontier models with endogeneity, offering better control for endogenous variables in the frontier and/or the inefficiency term in longitudinal settings compared to standard estimators. Learn about the significance of stochastic fr

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This study delves into the generalization of Empirical Risk Minimization (ERM) in stochastic convex optimization, focusing on minimizing true objective functions while considering generalization errors. It explores the application of ERM in machine learning and statistics, particularly in supervised

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Object modeling is a crucial concept in software development, capturing the static structure of a system by depicting objects, their relationships, attributes, and operations. This modeling method aids in demonstrating systems to stakeholders and promotes a deeper understanding of real-world entitie

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Coupled ocean-atmosphere modeling on horizontally icosahedral and vertically hybrid-isentropic/isopycnic grids is a cutting-edge approach to modeling climate variability. The design goals aim to achieve a global domain with no grid mismatch at the ocean-atmosphere interface, with key indicators such

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SCRUM2 project aims to enhance CMEMS through regional/coastal ocean-biogeochemical uncertainty modelling, ensemble consistency verification, probabilistic forecasting, and data assimilation. The research team plans to contribute significant advancements in ensemble techniques and reliability assessm

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FASMEE is a collaborative project aimed at assessing and advancing fire and smoke modeling systems through critical measurement techniques and observational data. Led by key technical leads, FASMEE focuses on diverse modeling areas such as fire growth, effects, coupled fire-atmosphere behavior, smok

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Explore the simplest model of population growth and the assumptions it relies on. Delve into the challenges of real-world scenarios, such as stochastic effects caused by demographic and environmental variations in birth and death rates. Learn how these factors impact predictions and models.

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In this lesson, we delve into subarea and corridor studies focusing on travel demand model refinements, highway network coding, corridor congestion relief, and trip assignment theory. Subarea modeling plays a crucial role in forecasting travel within smaller regions with detailed traffic patterns, t

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Ensure clarity on modeling goals and uncertainties. Select sample areas strategically based on interest and available data. Determine appropriate resolution for modeling. Define variables to model and validate the model effectively. Assess sample data adequacy and predictor variables availability. E

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This presentation by Justine Carroll, P.E., Project Manager, focuses on the hydraulic modeling and water quality considerations related to water storage tanks. It covers topics such as water age evaluation, steady state modeling, extended period simulations, pump controls, demand patterns, EPS verif

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Irina Tezaur leads the Quantitative Modeling & Analysis Department, focusing on computational modeling and simulation of complex multi-scale, multi-physics problems. Her work benefits DOE nuclear weapons, national security, and climate missions. By employing innovative techniques like model order re

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A new flexible framework for forward and inverse modeling of stormwater lids is presented. It includes governing equations, hydraulic and contaminant transport, numerical methods, and demonstration cases for various green infrastructure components. The importance of different processes in modeling i

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This presentation delves into the analysis and optimality of multiserver stochastic scheduling, focusing on the theory of large-scale computing systems, queueing theory, and prior work on single-server and multiserver scheduling. It explores optimizing response time and resource efficiency in modern

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This piece discusses approximation algorithms for stochastic optimization problems, focusing on modeling uncertainty in inputs, adapting to stochastic predictions, and exploring different optimization themes. It covers topics such as weakening the adversary in online stochastic optimization, two-sta

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Understanding the importance of plasma etching in semiconductor fabrication, this discourse delves into the challenges faced in modeling modern etch processes. Topics covered include stochastic defect detection, reactor-level plasma physics, and an integrated model hierarchy approach. Techniques suc

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Explore the world of Automated Speech Recognition (ASR), including setup, basics, observations, preprocessing, language modeling, acoustic modeling, and Hidden Markov Models. Learn about the process of converting speech signals into transcriptions, the importance of language modeling in ASR accuracy

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Stochastic dynamic programming with Markov chains is used for optimal control of the forest sector, focusing on continuous cover forestry. This approach optimizes forest industry production, harvest levels, and logistic solutions based on market conditions. The method involves solving quadratic prog

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Exploring the use of a stochastic weather generator combined with downscaled General Circulation Models for climate change analysis in the California Department of Water Resources. The presentation outlines the motivation, weather-regime based generator description, scenario generation, and a case s

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Knowledge-based approaches are crucial in genetic data analysis to enhance the accuracy and scope of genome-wide association studies (GWAS). Stochastic Relational AI (Star-AI) offers a solution by leveraging probability theory and First Order Logic to capture complex genetic interactions. By integra

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Weekly coordination calls with Ramboll and discussions with EPA OAQPS leads focused on Regional Haze and modeling adjustments. Final draft of procedures document with visibility projections and glide slope adjustments. Upcoming RTOWG meetings covering methodology, projections, and modeling evaluatio

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Introduction to data modeling and design in Industrial Information Systems at King Saud University College of Engineering. The chapter covers the significance of data modeling, capturing data characteristics, structural information, and the role of databases and DBMS. Data modeling fills crucial gap

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Mega-Modeling is a comprehensive approach that encompasses model construction, evaluation, composition, evolution, and search to address challenges in various areas such as social and economic resilience, health, transportation, and energy management. The pillars of Mega-Modeling include Model-Drive

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Explore the concepts of Brownian motion, integration of stochastic differential equations, and derivations by Einstein and Langevin. Learn about the assumptions, forces, and numerical integration methods in the context of stochastic processes. Discover the key results and equations that characterize

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Explore Generalized Stochastic Petri Nets (GSPN) to model manufacturing systems and evaluate steady-state performances. Learn about stochastic Petri nets, inhibitors, priorities, and their applications through examples. Delve into models of unreliable machines, productions systems with priorities, a

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NetLogo is a powerful and versatile programmable modeling environment created by Uri Wilensky in 1999. It allows users to simulate natural and social phenomena by giving instructions to multiple agents operating independently, making it ideal for modeling complex systems evolving over time. NetLogo

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Stochastic algorithms, including Monte Carlo and Las Vegas variations, leverage randomness to tackle complex tasks efficiently. While Monte Carlo algorithms prioritize speed with some margin of error, Las Vegas algorithms guarantee accuracy but with variable runtime. They play a vital role in primal

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Explore an optimal stopping approach for early drought detection, focusing on setting trigger levels based on precipitation measures. The goal is to determine the best time to send humanitarian aid by maximizing expected rewards and minimizing expected costs through suitable gain/risk functions. Tas

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Explore the world of viral marketing and user behavior optimization through stochastic optimal control in the realm of human-centered machine learning. Discover strategies to maximize user activity in social networks by steering behaviors and understanding endogenous and exogenous events. Dive into

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Explore the relationship between sample and space complexity in stochastic streams to estimate distribution properties and solve various problems. The research delves into the tradeoff between the number of samples required to solve a problem and the space needed for the algorithm, covering topics s

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Training dense linear models on FPGA with low-precision data offers increased hardware efficiency while maintaining statistical efficiency. This approach leverages stochastic rounding and multivariate trade-offs to optimize performance in machine learning tasks, particularly using Stochastic Gradien

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