Aerothermal Performance and Soot Emissions of Reacting Flow in a Micro-Gas Turbine Combustor

Micro-gas turbines are used for power generation and propulsion in unmanned aerial vehicles. Despite the growing demand for electric engines in a world striving for a net zero carbon footprint, combustion gas turbines will continue to play a critical role. Hence, there is a need for improved micro-gas turbines that can meet stringent environmental regulations. This paper is the first part of a comprehensive study focused on understanding the fundamental performance and emission characteristics of a micro-gas turbine model, with the aim of finding ways to enhance its operation. The study used a multidisciplinary CFD model to simulate the reacting flow in the combustion chamber and validated the results against experimental data and throughflow simulations. The present work is one of the few work that attempts to address both the aerothermal performance and emissions of the gas turbine. The findings highlight that parameters such as non-uniform outlet pressure, fuel-to-air ratio, and fuel injection velocity can greatly influence the performance and emissions of the micro-gas turbine. These parameters can affect the combustion efficiency, the formation of hot spots at the combustor–turbine interface, and the soot emissions. The results provide valuable insights for optimizing the performance and reducing the emissions of micro-gas turbines and serve as a foundation for further research into the interaction between the combustor and the turbine

Derivation of Electroweak Chiral Lagrangian from One Family Technicolor Model

Based on previous studies deriving the chiral Lagrangian for pseudo scalar mesons from the first principle of QCD in the path integral formalism, we derive the electroweak chiral Lagrangian and dynamically compute all its coefficients from the one family technicolor model. The numerical results of the $p^4$ order coefficients obtained in this paper are proportional to the technicolor number $N_{\rm TC}$ and the technifermion number $N_{\rm TF}$, which agrees with the arguments in previous works, and which confirms the reliability of this dynamical computation.Comment: 6 page

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

Delayed Product Introduction

We investigate the incentives of a monopolistic seller to delay the introduction of a new and improved version of his product. By analyzing a three-period model, we show that the seller may prefer to delay introducing a new product, even though the enabling technologies for the product are already available. The underlying motivation is analogous to that found in the durable goods monopolist literature – the seller suffers from a time inconsistency problem that causes his old and new products to cannibalize each other. Without the ability to remove existing stock of the old product from the market, shorten product durability, or pace research and development (R&D), he may respond by selling the new product later. We characterize the equilibria with delayed introduction, and study their changes with respect to market and product parameters. In particular, we show that delayed introduction could occur regardless of whether the seller can offer upgrade discounts to consumers, that instead, it is related to quality improvement brought about by the new product, durabilities, and discount factors. Further, we show that delayed introduction could bring socially efficient outcomes as well. Based on the insights of the model, we provide practical suggestions on pricing and policies

KINEMATIC AND ELECTROMYOGRAPHIC ANALYSIS OF UPPER EXTREMITY IN ARM WRESTLING

Few studies of the kinematic features of arm wrestling exercise have been published. The purpose of this study was to initiate a concrete analysis of the kinematic characteristics and muscular activities involved in arm wrestling exercise. 12 healthy male volunteers were recruited in this study. The pectoralis major (PMJ) showed significantly higher muscle activity in winning position than in losing position (p=.039) and had significant influence on arm wrestling outcome (