pwc-archive/papers-with-abstracts huggingface dataset:
Smooth Reference Command Generation and Control for Transition Flight of VTOL Aircraft Using Time-Varying Optimization
Authors: Jinrae Kim, John L. Bullock, Sheng Cheng, Naira Hovakimyan
Date: 2025-01-01 00:00:00

Vertical take-off and landing (VTOL) aircraft pose a challenge in generating reference commands during transition flight. While sparsity between hover and cruise flight modes can be promoted for effective transitions by formulating $\ell_{1}$-norm minimization problems, solving these problems offline pointwise in time can lead to non-smooth reference commands, resulting in abrupt transitions. This study addresses this limitation by proposing a time-varying optimization method that explicitly considers time dependence. By leveraging a prediction-correction interior-point time-varying optimization framework, the proposed method solves an ordinary differential equation to update reference commands continuously over time, enabling smooth reference command generation in real time. Numerical simulations with a two-dimensional Lift+Cruise vehicle validate the effectiveness of the proposed method, demonstrating its ability to generate smooth reference commands online.
Performance-Barrier Event-Triggered PDE Control of Traffic Flow
Authors: Peihan Zhang, Bhathiya Rathnayake, Mamadou Diagne, Miroslav Krstic
Date: 2025-01-01 00:00:00

For stabilizing stop-and-go oscillations in traffic flow by actuating a variable speed limit (VSL) at a downstream boundary of a freeway segment, we introduce event-triggered PDE backstepping designs employing the recent concept of performance-barrier event-triggered control (P-ETC). Our design is for linearized hyperbolic Aw-Rascle-Zhang (ARZ) PDEs governing traffic velocity and density. Compared to continuous feedback, ETC provides a piecewise-constant VSL commands-more likely to be obeyed by human drivers. Unlike the existing regular ETC (R-ETC), which enforces conservatively a strict decrease of a Lyapunov function, our performance-barrier (P-ETC) approach permits an increase, as long as the Lyapunov function remains below a performance barrier, resulting in fewer control updates than R-ETC. To relieve VSL from continuously monitoring the triggering function, we also develop periodic event-triggered (PETC) and self-triggered (STC) versions of both R-ETC and P-ETC. These are referred to as R/P-PETC and R/P-STC, respectively, and we show that they both guarantee Zeno-free behavior and exponential convergence in the spatial $L^2$ norm. With comparative simulations, we illustrate the benefits of the performance-barrier designs through traffic metrics (driver comfort, safety, travel time, fuel consumption). The proposed algorithms reduce discomfort nearly in half relative to driver behavior without VSL, while tripling the driver safety, measured by the average dwell time, relative to the R-ETC frequent-switching VSL schedule.
Privacy-Preserving Distributed Defense Framework for DC Microgrids Against Exponentially Unbounded False Data Injection Attacks
Authors: Yi Zhang, Mohamadamin Rajabinezhad, Yichao Wang, Junbo Zhao, Shan Zuo
Date: 2024-12-31 00:00:00
Tasks: Privacy Preserving

This paper introduces a novel, fully distributed control framework for DC microgrids, enhancing resilience against exponentially unbounded false data injection (EU-FDI) attacks. Our framework features a consensus-based secondary control for each converter, effectively addressing these advanced threats. To further safeguard sensitive operational data, a privacy-preserving mechanism is incorporated into the control design, ensuring that critical information remains secure even under adversarial conditions. Rigorous Lyapunov stability analysis confirms the framework's ability to maintain critical DC microgrid operations like voltage regulation and load sharing under EU-FDI threats. The framework's practicality is validated through hardware-in-the-loop experiments, demonstrating its enhanced resilience and robust privacy protection against the complex challenges posed by quick variant FDI attacks.
Event-Triggered Observer-Based Fixed-Time Consensus Control for Uncertain Nonlinear Multiagent Systems with Unknown States
Authors: Kewei Zhou, ZiMing Wang, Zhihao Chen, Xin Wang
Date: 2024-12-31 00:00:00

This paper introduces a novel approach for achieving fixed-time tracking consensus control in multiagent systems (MASs). Departing from the reliance on traditional controllers, our innovative controller integrates modified tuning and Lyapunov functions to guarantee stability and convergence. Furthermore, we have implemented an event-triggered strategy aimed at reducing the frequency of updates, alongside an output-feedback observer to manage unmeasured states effectively. To address the challenges posed by unknown functions and algebraic-loop problems, we opted for radial basis function neural networks (RBF NNs), chosen for their superior performance. Our methodology successfully mitigates Zeno's behavior and ensures stability within a narrowly defined set. The efficacy of our proposed solution is validated through two illustrative simulation examples.
Lyapunov-based Resilient Secondary Synchronization Strategy of AC Microgrids Under Exponentially Energy-Unbounded FDI Attacks
Authors: Mohamadamin Rajabinezhad, Nesa Shams, Asad Ali Khan, Omar A. Beg, Shan Zuo
Date: 2024-12-31 00:00:00

This article presents fully distributed Lyapunov-based attack-resilient secondary control strategies for islanded inverter-based AC microgrids, designed to counter a broad spectrum of energy-unbounded False Data Injection (FDI) attacks, including exponential attacks, targeting control input channels. While distributed control improves scalability and reliability, it also increases susceptibility to cyber threats. The proposed strategies, supported by rigorous Lyapunov-based proofs, ensure uniformly ultimately bounded (UUB) convergence for frequency regulation, voltage containment, and power sharing, even under severe cyber attacks. The effectiveness of the proposed approach has been demonstrated through case studies on a modified IEEE 34-bus system, leveraging simulations and real-time Hardware-in-the-Loop experiments with OPAL-RT.
A Complex Frequency-Based Control for Inverter-Based Resources
Authors: R. Bernal, F. Milano
Date: 2024-12-31 00:00:00

This paper proposes a novel control for Inverter-based Resources (IBRs) based on the Complex Frequency (CF) concept. The controller's objective is to maintain a constant CF of the voltage at the terminals of the IBR by adjusting its current reference. This current is imposed based on the well-known power flow equation, the dynamics of which are calculated through the estimation of the CF of the voltages of the adjacent buses. Performance is evaluated by analyzing local variations in frequency and magnitude of the voltage, as well as the response of the system's Center of Inertia (CoI) frequency, and then compared with conventional frequency droop, PI voltage controllers and virtual inertia. The case study utilizes the WSCC 9-bus system and a 1479-bus model of the Irish transmission grid and considers various contingencies and sensitivities such as the impact of limiters, delays, noise, R/X ratio, and EMT dynamics. Results show that the proposed scheme consistently outperforms the conventional controllers, leading to significant improvements in the overall dynamic response of the system.
Integrating Cascade Pumped Micro-Hydro Storage: A Sustainable Approach to Energy and Water Management
Authors: Oraib Dawaghreh, Sharaf K. Magableh, Xuesong Wang, Mohammad Adnan Magableh, Caisheng Wang
Date: 2024-12-31 00:00:00
Tasks: Management

As traditional large hydropower has been extensively exploited, micro-hydro systems have caught research increasing interest. New engineering challenges arise in developing micro-hydro systems in areas with significant elevation but prohibitive horizontal distances between primary reservoirs. This study addresses these challenges by proposing a cascade-pumped micro-hydro storage (CPMHS) system that leverages intermediate reservoirs to bridge long horizontal distances, enabling efficient energy transfer and storage. The methodology utilizes naturally occurring lakes with substantial head heights but limited feasibility for direct pumped storage due to horizontal separations. Integrating smaller, strategically placed intermediate reservoirs maximizes energy capture along the cascading path, making pumped storage viable in geographically constrained locations. The proposed system will enhance energy generation potential and provide additional benefits for water management. Using geographical data and a detailed case study focused on Mountain Lake and surrounding lakes, this paper demonstrates the energy efficiency and viability of cascade-based micro-hydro storage. A practical methodology for implementing CPMHS systems is proposed and validated by case studies. An optimization framework is developed for efficient energy capture in regions with challenging topography.
A Universal Controller for Grid-Tied Inverters
Authors: Fariba Fateh
Date: 2024-12-31 00:00:00

This paper presents the development of "Control-Sync," a novel firmware for universal inverters in microgrids, designed to enhance grid stability and flexibility. As hybrid PV-battery systems become increasingly prevalent, there is a critical need for inverters capable of efficiently transitioning between grid-forming (GFM) and grid-following (GFL) modes. Our firmware introduces dual control paths that allow for seamless transitions without reliance on external control devices, reducing communication overhead and increasing operational reliability. Key features include direct phase-angle detection and frequency restoration capabilities, essential for managing asymmetrical power grids and dynamic load changes. The efficacy of Control-Sync is demonstrated through rigorous testing with grid emulators and multi-phase inverters, confirming its potential to improve microgrid reliability and efficiency. This study offers a scalable solution to enhance inverter adaptability in various grid conditions, fostering a more resilient energy infrastructure.
Autonomous Minibus Service with Semi-on-demand Routes in Grid Networks
Authors: Max T. M. Ng, Hani S. Mahmassani
Date: 2024-12-31 00:00:00

This paper investigates the potential of autonomous minibuses which take on-demand directional routes for pick-up and drop-off in a grid network of wider area with low density, followed by fixed routes in areas with demand. Mathematical formulation for generalized costs demonstrates its benefits, with indicators proposed to select existing bus routes for conversion with the options of zonal express and parallel routes. Simulations on modeled scenarios and case studies with bus routes in Chicago show reductions in both passenger costs and generalized costs over existing fixed-route bus service between suburban areas and CBD.
On Functional Observability of Nonlinear Systems and the Design of Functional Observers with Assignable Error Dynamics
Authors: Costas Kravaris
Date: 2024-12-30 00:00:00

This paper proposes a novel approach for designing functional observers for nonlinear systems, with linear error dynamics and assignable poles. Sufficient conditions for functional observability are first derived, leading to functional relationships between the Lie derivatives of the output to be estimated and the ones of the measured output. These are directly used in the proposed design of the functional observer. The functional observer is defined in differential input-output form, satisfying an appropriate invariance condition that emerges from the state-space invariance conditions of the literature. A concept of functional observer index is also proposed, to characterize the lowest feasible order of functional observer with pole assignment. Two chemical reactor applications are used to illustrate the proposed approach.
A data-driven biophysical network model reproduces C. elegans premotor neural dynamics
Authors: Megan Morrison, Lai-Sang Young
Date: 2024-12-31 00:00:00

C. elegans locomotion is composed of switches between forward and reversal states punctuated by turns. This locomotory capability is necessary for the nematode to move towards attractive stimuli, escape noxious chemicals, and explore its environment. Although experimentalists have identified a number of premotor neurons as drivers of forward and reverse motion, how these neurons work together to produce the behaviors observed remains to be understood. Towards a better understanding of C. elegans neurodynamics, we present in this paper a minimally parameterized, biophysical dynamical systems model of the premotor network. Our model consists of a recurrently connected collection of premotor neurons (the core group) driven by over a hundred sensory and interneurons that provide diverse feedforward inputs to the core group. It is data-driven in the sense that the choice of neurons in the core group follows experimental guidance, anatomical structures are dictated by the connectome, and physiological parameters are deduced from whole-brain imaging and voltage clamps data. When simulated with realistic input signals, our model produces premotor activity that closely resembles experimental data: from the seemingly random switching between forward and reversal behaviors to the synchronization of subnetworks to various higher-order statistics. We posit that different roles are played by gap junctions and synaptic connections in switching dynamics. The model correctly predicts behavior such as dwelling versus roaming as a result of the synaptic inputs received, and we demonstrate that it can be used to study how the activity level of certain individual neurons impacts behavior.
Evolution of mutating pathogens in networked populations
Authors: Aviel Ivry, Amikam Patron, Reuven Cohen
Date: 2025-01-01 00:00:00

Epidemic spreading over populations networks has been an important subject of research for several decades, and especially during the Covid-19 pandemic. Most epidemic outbreaks are likely to create multiple mutations during their spreading over the population. In this paper, we study the evolution of a pathogen which can mutate continuously during the epidemic spreading. We consider pathogens whose mutating parameter is the mortality mean-time, and study the evolution of this parameter over the spreading process. We use analytical methods to compute the dynamic equation of the epidemic and the conditions for it to spread. We also use numerical simulations to study the pathogen flow in this case, and to understand the mutation phenomena. We show that the natural selection leads to less violent pathogens becoming predominant in the population. We discuss a wide range of network structures and show how different effects are manifested in each case.
Poisson Network SIR Epidemic Model
Authors: Josephine K. Wairimu, Andrew Gothard, Grzegorz A. Rempala
Date: 2024-12-30 00:00:00
Tasks: model

We extend the classical Susceptible-Infected-Recovered (SIR) model to a network-based framework where the degree distribution of nodes follows a Poisson distribution. This extension incorporates an additional parameter representing the mean node degree, allowing for the inclusion of heterogeneity in contact patterns. Using this enhanced model, we analyze epidemic data from the 2018-20 Ebola outbreak in the Democratic Republic of the Congo, employing a survival approach combined with the Hamiltonian Monte Carlo method. Our results suggest that network-based models can more effectively capture the heterogeneity of epidemic dynamics compared to traditional compartmental models, without introducing unduly overcomplicated compartmental framework.
The Impact of Socio-Economic Challenges and Technological Progress on Economic Inequality: An Estimation with the Perelman Model and Ricci Flow Methods
Authors: Davit Gondauri
Date: 2025-01-01 00:00:00

The article examines the impact of 16 key parameters of the Georgian economy on economic inequality, using the Perelman model and Ricci flow mathematical methods. The study aims to conduct a deep analysis of the impact of socio-economic challenges and technological progress on the dynamics of the Gini coefficient. The article examines the following parameters: income distribution, productivity (GDP per hour), unemployment rate, investment rate, inflation rate, migration (net negative), education level, social mobility, trade infrastructure, capital flows, innovative activities, access to healthcare, fiscal policy (budget deficit), international trade (turnover relative to GDP), social protection programs, and technological access. The results of the study confirm that technological innovations and social protection programs have a positive impact on reducing inequality. Productivity growth, improving the quality of education, and strengthening R&D investments increase the possibility of inclusive development. Sensitivity analysis shows that social mobility and infrastructure are important factors that affect economic stability. The accuracy of the model is confirmed by high R^2 values (80-90%) and the statistical reliability of the Z-statistic (<0.05). The study uses Ricci flow methods, which allow for a geometric analysis of the transformation of economic parameters in time and space. Recommendations include the strategic introduction of technological progress, the expansion of social protection programs, improving the quality of education, and encouraging international trade, which will contribute to economic sustainability and reduce inequality. The article highlights multifaceted approaches that combine technological innovation and responses to socio-economic challenges to ensure sustainable and inclusive economic development.
Copula Central Asymmetry of Equity Portfolios
Authors: Lorenzo Frattarolo
Date: 2024-12-31 00:00:00

Financial crises are usually associated with increased cross-sectional dependence between asset returns, causing asymmetry between the lower and upper tail of return distribution. The detection of asymmetric dependence is now understood to be essential for market supervision, risk management, and portfolio allocation. I propose a non-parametric test procedure for the hypothesis of copula central symmetry based on the Cram\'er-von Mises distance of the empirical copula and its survival counterpart, deriving the asymptotic properties of the test under standard assumptions for stationary time series. I use the powerful tie-break bootstrap that, as the included simulation study implies, allows me to detect asymmetries with up to 25 series and the number of observations corresponding to one year of daily returns. Applying the procedure to US portfolio returns separately for each year shows that the amount of copula central asymmetry is time-varying and less present in the recent past. Asymmetry is more critical in portfolios based on size and less in portfolios based on book-to-market and momentum. In portfolios based on industry classification, asymmetry is present during market downturns, coherently with the financial contagion narrative.
Panel Estimation of Taxable Income Elasticities with Heterogeneity and Endogenous Budget Sets
Authors: Soren Blomquist, Anil Kumar, Whitney K. Newey
Date: 2024-12-31 00:00:00

This paper introduces an estimator for the average of heterogeneous elasticities of taxable income (ETI), addressing key econometric challenges posed by nonlinear budget sets. Building on an isoelastic utility framework, we derive a linear-in-logs taxable income specification that incorporates the entire budget set while allowing for individual-specific ETI and productivity growth. To account for endogenous budget sets, we employ panel data and estimate individual-specific ridge regressions, constructing a debiased average of ridge coefficients to obtain the average ETI.
Regression discontinuity aggregation, with an application to the union effects on inequality
Authors: Kirill Borusyak, Matan Kolerman-Shemer
Date: 2024-12-31 00:00:00

We extend the regression discontinuity (RD) design to settings where each unit's treatment status is an average or aggregate across multiple discontinuity events. Such situations arise in many studies where the outcome is measured at a higher level of spatial or temporal aggregation (e.g., by state with district-level discontinuities) or when spillovers from discontinuity events are of interest. We propose two novel estimation procedures - one at the level at which the outcome is measured and the other in the sample of discontinuities - and show that both identify a local average causal effect under continuity assumptions similar to those of standard RD designs. We apply these ideas to study the effect of unionization on inequality in the United States. Using credible variation from close unionization elections at the establishment level, we show that a higher rate of newly unionized workers in a state-by-industry cell reduces wage inequality within the cell.
Inequality of opportunity under current China's license plate policy
Authors: Nina Xiaochun Sun, Zaifang He, Yi Pang
Date: 2025-01-02 00:00:00

Measuring inequality of opportunities has long been a challenging and open problem, primarily due to the limitations associated with individual-level data. In this study, we utilize data obtained from vehicle license plates in a comprehensive survey (17258 vehicles from 6 major cities in China) to evaluate the inequality of opportunities in the country. In our context, we define inequality of opportunity as the scenario where relatively expensive vehicles have a higher likelihood of being paired with license plates featuring 'Lucky Numbers'. To quantify this, we propose a lucky-number-based opportunity Gini coefficient. Through the calculation of the opportunity Gini coefficient, we observe a significant and positive correlation between opportunity inequality and income inequality. Particularly noteworthy is our finding that the advancement of technology, exemplified by the widespread adoption of new energy vehicles, can substantially reduce the inequality of opportunity. Taking incorporation of a random lottery process before acquiring a motor vehicle in Beijing and Shanghai as a natural experiment, our empirical results support the argument that, in terms of equality, employing random drawing is a fair and equitable approach for allocating scarce resources.
The Learning Crisis: Three Years After COVID-19
Authors: Tomasz Gajderowicz, Maciej Jakubowski, Alec Kennedy, Christian Christrup Kjeldsen, Harry Anthony Patrinos, Rolf Strietholt
Date: 2025-01-02 00:00:00

The COVID-19 pandemic caused widespread disruptions to education, with school closures affecting over one billion children. These closures, aimed at reducing virus transmission, resulted in significant learning losses, particularly in mathematics and science. Using data from TIMSS 2023, which assesses fourth and eighth-grade achievements across 71 education systems, this study analyzes the impact of school closure duration on learning outcomes. Mixed-effect models estimate deviations from pre-pandemic trends, adjusting for demographic factors. Results show a global average decline of 0.11 standard deviations (SD) in student achievement, with longer closures linked to more severe losses. The effects on low performers, girls, and linguistic minorities show effect sizes up to 0.22 SD. These findings highlight the lasting impact of school closures and emphasize the need for targeted recovery strategies and international cooperation to promote equitable educational outcomes post-pandemic.