pwc-archive/papers-with-abstracts huggingface dataset:
Loss Allocation in Submarine Armored Three-core HVAC Power Cables
Authors: Juan Carlos del-Pino-López, Pedro Cruz-Romero, Luis Carlos Sánchez-Díaz
Date: 2024-01-24 00:00:00

Loss allocation of the three different components (conductor, sheaths and armor) of solidly bonded three-core separated lead-sheathed armored cables, frequently employed in offshore wind farms, is challenging due to the lack of accurate enough analytical expressions in the IEC standard. Also, loss allocation through experimental tests leads to inaccurate results since it is based on questionable assumptions. This paper improves both the IEC formulae and experimental methods by means of different analytical corrections in the conductor and sheath loss expressions. To this aim, an ad hoc application interface (Virtual Lab) based on 3D numerical simulations (finite element method) has been developed. This tool virtualizes and automates different test setups to emulate, in few seconds, the most employed experimental procedures in this type of cable. The analytical corrections have been derived from an in-depth analysis of a first set of 368 cables, ranging from 30 to 275 kV. The new loss expressions were successfully applied to a second set of 645 armored cables of quite diverse features (voltages from 10 to 275 kV, sections and dimensional parameters), hence bringing a general framework for any kind of three-core armored cable.
A Market-Clearing-based Sensitivity Model for Locational Marginal and Average Carbon Emission
Authors: Zelong Lu
Date: 2024-01-24 00:00:00
Tasks: Sensitivity

This letter proposes a market-clearing-based locational marginal carbon emission (LMCE) metric to assess the marginal carbon emission effect of nodal load demand. Unlike the prevalent carbon emission flow (CEF) method that relies on a hypothetical power-flow tracking process, the proposed LMCE metric depends on a novel sensitivity analysis of market-clearing results, capable of revealing both energy-dependent and network-dependent impacts on emissions. Additionally, we introduce a locational average carbon emission (LACE) metric, derived from LMCE, to effectively measure the general emission effect. It offers insights into demand-side carbon emission effects, such as a negative LMCE and LACE indicating emission reduction even as load increases. It can also prevent excessive demand-side emission allocations. Overall, the proposed method provides a clear perspective for the ongoing decarbonization policies.
Polynomial Lyapunov Functions and Invariant Sets from a New Hierarchy of Quadratic Lyapunov Functions for LTV Systems
Authors: Hassan Abdelraouf, Eric Feron, Jeff S. Shamma
Date: 2024-01-23 00:00:00

We introduce a new class of quadratic functions based on a hierarchy of linear time-varying (LTV) dynamical systems. These quadratic functions in the higher order space can be also seen as a non-homogeneous polynomial Lyapunov functions for the original system, i.e the first system in the hierarchy. These non-homogeneous polynomials are used to obtain accurate outer approximation for the reachable set given the initial condition and less conservative bounds for the impulse response peak of linear, possibly time-varying systems. In addition, we pose an extension to the presented approach to construct invariant sets that are not necessarily Lyapunov functions. The introduced methods are based on elementary linear systems theory and offer very much flexibility in defining arbitrary polynomial Lyapunov functions and invariant sets for LTV systems.
Smart Grids: A Comprehensive Survey of Challenges, Industry Applications, and Future Trends
Authors: Jadyn Powell, Alex McCafferty-Leroux, Waleed Hilal, Stephen Andrew Gadsden
Date: 2024-01-23 00:00:00

With the increased energy demands of the 21st century, there is a clear need for developing a more sustainable method of energy generation, distribution, and transmission. The popularity of Smart Grid continues to grow as it presents its benefits, including interconnectivity, improved efficiency, the ability to integrate renewable energy sources, and many more. However, it is not without its challenges. This survey aims to provide an introductory background of smart grids, detail some of the main aspects and current challenges, and review the most recent papers and proposed solutions. It will also highlight the current state of implementation of the smart grid by describing various prototypes, as well as various countries and continents implementation plans and projects.
A Novel Policy Iteration Algorithm for Nonlinear Continuous-Time H$\infty$ Control Problem
Authors: Qi Wang
Date: 2024-01-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

H{\infty} control of nonlinear continuous-time system depends on the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which has been proved impossible to obtain a closed-form solution due to the nonlinearity of HJI equation. In order to solve HJI equation, many iterative algorithms were proposed, and most of the algorithms were essentially Newton method when the fixed-point equation was constructed in a Banach space. Newton method is a local optimization method, it has small convergence region and needs the initial guess to be sufficiently close to the solution. Whereas damped Newton method enhances the robustness with respect to initial condition and has larger convergence region. In this paper, a novel reinforcement learning method which is named {\alpha}-policy iteration ({\alpha}-PI) is introduced for solving HJI equation. First, by constructing a damped Newton iteration operator equation, a generalized Bellman equation (GBE) is obtained. The GBE is an extension of bellman equation. And then, by iterating on the GBE, an on-policy {\alpha}-PI reinforcement learning method without using knowledge regarding to the system internal dynamics is proposed. Third, based on the on-policy {\alpha}-PI reinforcement learning method, we develop an off-policy {\alpha}-PI reinforcement learning method without requiring any knowledge of the system dynamics. Finally, the neural-network based adaptive critic implementation schemes of on-policy and off-policy {\alpha}-PI algorithms are derived respectively, and the batch least-squares method is used for calculating the weight parameters of neural networks. The effectiveness of the off-policy {\alpha}-PI algorithm is verified through computer simulation.
Temperature Compensation through Kinetic Regulation in Biochemical Oscillators
Authors: Haochen Fu, Chenyi Fei, Qi Ouyang, Yuhai Tu
Date: 2024-01-25 00:00:00

Nearly all circadian clocks maintain a period that is insensitive to temperature changes, a phenomenon known as temperature compensation (TC). Yet, it is unclear whether there is any common feature among different systems that exhibit TC. From a general timescale invariance, we show that TC relies on existence of certain period-lengthening reactions wherein the period of the system increases strongly with the rates in these reactions. By studying several generic oscillator models, we show that this counter-intuitive dependence is nonetheless a common feature of oscillators in the nonlinear (far-from-onset) regime where the oscillation can be separated into fast and slow phases. The increase of the period with the period-lengthening reaction rates occurs when the amplitude of the slow phase in the oscillation increases with these rates while the progression-speed in the slow phase is controlled by other rates of the system. The positive dependence of the period on the period-lengthening rates balances its inverse dependence on other kinetic rates in the system, which gives rise to robust TC in a wide range of parameters. We demonstrate the existence of such period-lengthening reactions and their relevance for TC in all four model systems we considered. Theoretical results for a model of the Kai system are supported by experimental data. A study of the energy dissipation also shows that better TC performance requires higher energy consumption. Our study unveils a general mechanism by which a biochemical oscillator achieves TC by operating at regimes far from the onset where period-lengthening reactions exist.
Detecting local perturbations of networks in a latent hyperbolic embedding space
Authors: Alice Longhena, Martin Guillemaud, Mario Chavez
Date: 2024-01-24 00:00:00
Tasks: Network Embedding

Graph theoretical approaches have been proven to be effective in the characterization of connected systems, as well as in quantifying their dysfunction due to perturbation. In this paper, we show the advantage of a non-Euclidean (hyperbolic) representation of networks to identify local connectivity perturbations and to characterize the induced effects on a large scale. We propose two perturbation scores based on representations of the networks in a latent geometric space, obtained through an embedding onto the hyperbolic Poincar\'e disk. We numerically demonstrate that these methods are able to localize perturbations in networks with homogeneous or heterogeneous degree connectivity. We apply this framework to identify the most perturbed brain areas in epileptic patients following surgery. This study is conceived in the effort of developing more powerful tools to represent and analyze brain networks, and it is the first to apply geometric network embedding techniques to the case of epilepsy.
Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding
Authors: Jie Tian, Jixin Hou, Zihao Wu, Peng Shu, Zhengliang Liu, Yujie Xiang, Beikang Gu, Nicholas Filla, Yiwei Li, Ning Liu, Xianyan Chen, Keke Tang, Tianming Liu, Xianqiao Wang
Date: 2024-01-13 00:00:00
Tasks: Multiple-choice, Prompt Engineering

This study is a pioneering endeavor to investigate the capabilities of Large Language Models (LLMs) in addressing conceptual questions within the domain of mechanical engineering with a focus on mechanics. Our examination involves a manually crafted exam encompassing 126 multiple-choice questions, spanning various aspects of mechanics courses, including Fluid Mechanics, Mechanical Vibration, Engineering Statics and Dynamics, Mechanics of Materials, Theory of Elasticity, and Continuum Mechanics. Three LLMs, including ChatGPT (GPT-3.5), ChatGPT (GPT-4), and Claude (Claude-2.1), were subjected to evaluation against engineering faculties and students with or without mechanical engineering background. The findings reveal GPT-4's superior performance over the other two LLMs and human cohorts in answering questions across various mechanics topics, except for Continuum Mechanics. This signals the potential future improvements for GPT models in handling symbolic calculations and tensor analyses. The performances of LLMs were all significantly improved with explanations prompted prior to direct responses, underscoring the crucial role of prompt engineering. Interestingly, GPT-3.5 demonstrates improved performance with prompts covering a broader domain, while GPT-4 excels with prompts focusing on specific subjects. Finally, GPT-4 exhibits notable advancements in mitigating input bias, as evidenced by guessing preferences for humans. This study unveils the substantial potential of LLMs as highly knowledgeable assistants in both mechanical pedagogy and scientific research.
Inverse analysis of granular flows using differentiable graph neural network simulator
Authors: Yongjin Choi, Krishna Kumar
Date: 2024-01-17 00:00:00
Tasks: Computational Efficiency, Graph Neural Network

Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. Traditional high-fidelity simulators for these inverse problems are computationally demanding, restricting the number of simulations possible. Additionally, their non-differentiable nature makes gradient-based optimization methods, known for their efficiency in high-dimensional problems, inapplicable. While machine learning-based surrogate models offer computational efficiency and differentiability, they often struggle to generalize beyond their training data due to their reliance on low-dimensional input-output mappings that fail to capture the complete physics of granular flows. We propose a novel differentiable graph neural network simulator (GNS) by combining reverse mode automatic differentiation of graph neural networks with gradient-based optimization for solving inverse problems. GNS learns the dynamics of granular flow by representing the system as a graph and predicts the evolution of the graph at the next time step, given the current state. The differentiable GNS shows optimization capabilities beyond the training data. We demonstrate the effectiveness of our method for inverse estimation across single and multi-parameter optimization problems, including evaluating material properties and boundary conditions for a target runout distance and designing baffle locations to limit a landslide runout. Our proposed differentiable GNS framework offers an orders of magnitude faster solution to these inverse problems than the conventional finite difference approach to gradient-based optimization.
Identification of Nonseparable Models with Endogenous Control Variables
Authors: Kaicheng Chen, Kyoo il Kim
Date: 2024-01-25 00:00:00

We study identification of the treatment effects in a class of nonseparable models with the presence of potentially endogenous control variables. We show that given the treatment variable and the controls are measurably separated, the usual conditional independence condition or availability of excluded instrument suffices for identification.
New accessibility measures based on unconventional big data sources
Authors: G. Arbia, V. Nardelli, N. Salvini, I. Valentini
Date: 2024-01-24 00:00:00

In health econometric studies we are often interested in quantifying aspects related to the accessibility to medical infrastructures. The increasing availability of data automatically collected through unconventional sources (such as webscraping, crowdsourcing or internet of things) recently opened previously unconceivable opportunities to researchers interested in measuring accessibility and to use it as a tool for real-time monitoring, surveillance and health policies definition. This paper contributes to this strand of literature proposing new accessibility measures that can be continuously feeded by automatic data collection. We present new measures of accessibility and we illustrate their use to study the territorial impact of supply-side shocks of health facilities. We also illustrate the potential of our proposal with a case study based on a huge set of data (related to the Emergency Departments in Milan, Italy) that have been webscraped for the purpose of this paper every 5 minutes since November 2021 to March 2022, amounting to approximately 5 million observations.
Realized Stochastic Volatility Model with Skew-t Distributions for Improved Volatility and Quantile Forecasting
Authors: Makoto Takahashi, Yuta Yamauchi, Toshiaki Watanabe, Yasuhiro Omori
Date: 2024-01-24 00:00:00

Forecasting volatility and quantiles of financial returns is essential for accurately measuring financial tail risks, such as value-at-risk and expected shortfall. The critical elements in these forecasts involve understanding the distribution of financial returns and accurately estimating volatility. This paper introduces an advancement to the traditional stochastic volatility model, termed the realized stochastic volatility model, which integrates realized volatility as a precise estimator of volatility. To capture the well-known characteristics of return distribution, namely skewness and heavy tails, we incorporate three types of skew-t distributions. Among these, two distributions include the skew-normal feature, offering enhanced flexibility in modeling the return distribution. We employ a Bayesian estimation approach using the Markov chain Monte Carlo method and apply it to major stock indices. Our empirical analysis, utilizing data from US and Japanese stock indices, indicates that the inclusion of both skewness and heavy tails in daily returns significantly improves the accuracy of volatility and quantile forecasts.
Inference under partial identification with minimax test statistics
Authors: Isaac Loh
Date: 2024-01-23 00:00:00

We provide a means of computing and estimating the asymptotic distributions of statistics based on an outer minimization of an inner maximization. Such test statistics, which arise frequently in moment models, are of special interest in providing hypothesis tests under partial identification. Under general conditions, we provide an asymptotic characterization of such test statistics using the minimax theorem, and a means of computing critical values using the bootstrap. Making some light regularity assumptions, our results augment several asymptotic approximations that have been provided for partially identified hypothesis tests, and extend them by mitigating their dependence on local linear approximations of the parameter space. These asymptotic results are generally simple to state and straightforward to compute (esp.\ adversarially).
Environmental impacts, nutritional profiles, and retail prices of commonly sold retail food items in 181 countries: an observational study
Authors: Elena M. Martinez, Nicole Tichenor Blackstone, Parke E. Wilde, Anna W. Herforth, William A. Masters
Date: 2024-01-24 00:00:00
Tasks: Attribute

Affordability is often seen as a barrier to consuming sustainable diets. This study provides the first worldwide test of how retail food prices relate to empirically estimated environmental impacts and nutritional profile scores between and within food groups. We use prices for 811 retail food items commonly sold in 181 countries during 2011 and 2017, matched to estimated carbon and water footprints and nutritional profiles, to test whether healthier and more environmentally sustainable foods are more expensive between and within food groups. We find that within almost all groups, less expensive items have significantly lower carbon and water footprints. Associations are strongest for animal source foods, where each 10% lower price is associated with 20 grams lower CO2-equivalent carbon and 5 liters lower water footprint per 100kcal. Gradients between price and nutritional profile vary by food group, price range, and nutritional attribute. In contrast, lower-priced items have lower nutritional value in only some groups over some price ranges, and that relationship is sometimes reversed. These findings reveal opportunities to reduce financial and environmental costs of diets, contributing to transitions towards healthier, more environmentally sustainable food systems.
The Arrival of Fast Internet and Employment in Africa: Comment
Authors: David Roodman
Date: 2024-01-17 00:00:00

Hjort and Poulsen (2019) frames the staggered arrival of submarine Internet cables on the shores of Africa circa 2010 as a difference-in-differences natural experiment. The paper finds positive impacts of broadband on individual- and firm-level employment and nighttime light emissions. These results largely are not robust to alternative geocoding of survey locations, to correcting for a satellite changeover at end-2009, and to revisiting a definition of the treated zone that has no clear technological basis, is narrower than the spatial resolution of nearly all the data sources, and is empirically suboptimal as a representation of the geography of broadband.
In the Aftermath of Oil Prices Fall of 2014/2015-Socioeconomic Facts and Changes in the Public Policies in the Sultanate of Oman
Authors: Osama A. Marzouk
Date: 2023-12-29 00:00:00
Tasks: Miscellaneous

Since the start of its national renaissance in 1970, the Sultanate of Oman (Oman) has gone over a major development in several areas, such as education, infrastructure, and urbanization. This has been powered by the revenues from exporting crude oil and natural gas, which together form the skeleton of the country's economy. In the second half of 2014, the oil prices declined strongly to about 50% of its price. This was followed by another moderate decline in the second half of 2015 and the beginning of 2016, leaving the barrel price at a low level below 30 US$ in January 2016 (as compared to above 110 US$ in June 2014). This drop had direct impacts on the economy of Oman, manifested in a large budget deficit, reduced governmental expenditure, reduced or cancelled subsidy of fuels and electricity, increase in the water tariff, and decline in deposits in banks. The country is coping with this through its 9th five-year plan (2016-2020), which adopts a strategy of diversifying the income and relying less on the traditional oil and gas sector. The country has also taken measures to facilitate private businesses. This article sheds light on these topics as well as miscellaneous data about Oman.
Econometric Approach to Analyzing Determinants of Sustained Prosperity
Authors: Anika Dixit
Date: 2023-12-22 00:00:00
Tasks: Econometrics

Every year, substantial resources are allocated to foreign aid with the aim of catalyzing prosperity and development in recipient countries. The diverse body of research on the relationship between aid and gross domestic product (GDP) has yielded varying results, finding evidence of both positive, negative, and negligible associations between the two. This study employs econometric techniques, namely Fully Modified Ordinary Least Squares Regression (FMOLS) and the Generalized Method of Moments (GMM), to explore the intricate links between innovation and different types of official development assistance (ODA) with the overarching construct of prosperity. The paper also reviews the linkages between foundational metrics, such as the rule of law, education, and economic infrastructure and services, in enabling self-sustaining prosperity. Drawing upon panel data of relevant determinants for 74 countries across the years 2013 to 2021, the study found that there was a negligible relationship between both ODA and innovation indices with prosperity. Notably, foreign aid targeted specifically toward education was observed to have a positive impact on prosperity, as was the presence of rule of law in a state. The results of the study are then examined through the lens of a case-study on Reliance Jio, exemplifying how the company engineered an ecosystem that harnessed resources and facilitated infrastructure development, thereby contributing to self-sustaining economic growth and prosperity in India.
Capturing the Tax-Revenue Bracketing System via a predator-prey model: Evidence from South Africa
Authors: Leonard Mushunje
Date: 2023-12-22 00:00:00

Revenues obtained from the corporate tax heads play significant roles in any economy as they can be prioritized for producing public goods and employment creations, among others. As such, corporate tax revenue should be paid enough attention. This study, therefore, explores the tax-revenue harvesting system of an economy where we focused on the corporate tax head. The system comprises three players; the government and formal and informal firms. We applied the predator-prey model to model the effect of the government-gazetted tax rate on corporate survivability. It is a new approach to modeling economic system relations and games. Critical combinatory points are derived, with stability analysis provided after that. Dynamics associated with the tax-revenue system are established and critically analyzed. Lastly, we provide the mathematical way the system can be optimized for the government to harvest as much Revenue as possible, including optimal conditions.