pwc-archive/papers-with-abstracts huggingface dataset:
Hydrogen and Battery Storage Technologies for Low Cost Energy Decarbonization in Distribution Networks
Authors: Hamed Haggi, Paul Brooker, Wei Sun, James M. Fenton
Date: 2022-02-06 00:00:00

Deep energy decarbonization cannot be achieved without high penetration of renewables. At higher renewable energy penetrations, the variability and intermittent nature of solar photovoltaic (PV) electricity can cause ramping issues with existing fossil fuel generation, requiring longer term energy storage to increase the reliability of grid operation. A proton exchange membrane electrolyzer can produce H2and serves as a utility controllable load. The produced H2 can then be stored and converted back into electricity, or mixed with natural gas, or used as transportation fuel, or chemical feedstock. This paper considers the perspective of the distribution system operator that operates the distributed energy resources on a standard IEEE 33-node distribution network considering the technical and physical constraints with the goal of minimizing total investment and operation cost. Different case studies, at very high PV penetrations are considered to show the challenges and path to net-zero emission energy production using H2 energy. Sensitivity of utility PV costs and electrolyzer capital costs on producing H2 at $1/kg are presented showing that the distribution network could produce 100% renewable electricity and H2 could be produced with the same price by 2050 with conservative cost estimates and by 2030 with accelerated cost declines.
Mitigating Coriolis Effects in Centrifuge Simulators Through Allowing Small, Unperceived G-Vector Misalignments
Authors: Tigran Mkhoyan, Mark Wentink, Bernd de Graaf, M. M., van Paassen, Max Mulder
Date: 2022-02-06 00:00:00

When coupled with additional degrees of freedom, centrifuge-based motion platforms can combine the agility of hexapod-based platforms with the ability to sustain higher G-levels and an extended motion space, required for simulating extreme maneuvers. However, the false and often nauseating sensations of rotation, by Coriolis effects induced by the centrifuge rotation in combination with rotations of the centrifuge cabin or the pilot's head, are a major disadvantage. This paper discusses the development of a motion filter, the Coherent Alignment Method (COHAM), which aims at reducing Coriolis effects by allowing small mismatches in the G-vector alignment, reducing cabin rotations. Simulations show that as long as these mismatches remain within a region where humans perceive the G-vector as 'coherent', the Coherent Alignment Zone (CAZ), the cabin angular accelerations can indeed be reduced. COHAM was tested in a high G-maneuver task with a fixed CAZ threshold obtained in a previous study. It was experimentally compared to an existing motion filter, using metrics such as sickness, comfort and false cues. Results show that sickness, dizziness and discomfort are reduced, making the centrifuge sessions more bearable. It is recommended to further improve the filter design and tuning, and test it with more fighter pilots.
Spatio-Temporal Failure Propagation in Cyber-Physical Power Systems
Authors: Osman Boyaci, M. Rasoul Narimani, Katherine Davis, Erchin Serpedin
Date: 2022-02-05 00:00:00

Cascading failure in power systems is triggered by a small perturbation that leads to a sequence of failures spread through the system. The interconnection between different components in a power system causes failures to easily propagate across the system. The situation gets worse by considering the interconnection between cyber and physical layers in power systems. A plethora of work has studied the cascading failure in power systems to calculate its impact on the system. Understanding how failures propagate into the system in time and space can help the system operator to take preventive actions and upgrade the system accordingly. Due to the nonlinearity of the power flow equation as well as the engineering constraints in the power system, it is essential to understand the spatio-temporal failure propagation in cyber-physical power systems (CPPS). This paper proposes an asynchronous algorithm for investigating failure propagation in CPPS. The physics of the power system is addressed by the full AC power flow equations. Various practical constraints including load shedding, load-generation balance, and island operation are considered to address practical constraints in power system operation. The propagation of various random initial attacks of different sizes is analyzed and visualized to elaborate on the applicability of the proposed approach. Our findings shed light on the cascading failure evolution in CPPS.
Connected and Automated Vehicle Platoon Formation Control via Differential Games
Authors: Hossein B. Jond, Aykut Yıldız
Date: 2022-02-05 00:00:00

In this study, the connected and automated vehicles (CAVs) platooning problem is resolved under a differential game framework. Three information topologies are considered here. Firstly, Predecessor-following (PF) topology is utilized where the vehicles control the distance with respect to the merely nearest predecessor via a sensor link-based information flow. Secondly, Two-predecessor-following topology (TPF) is exploited where each vehicle controls the distance with respect to the two nearest predecessors. In this topology, the second predecessor is communicated via a Vehicle-to-vehicle (V2V) link. The individual trajectories of CAVs under the Nash equilibrium are derived in closed-form for these two information topologies. Finally, general information topology is examined and the differential game is formulated in this context. In all these options, Pontryagin's principle is employed to investigate the existence and uniqueness of the Nash equilibrium and obtain its corresponding trajectories. In the general topology, we suppose numerical computation of eigenvalues and eigenvectors. All these approaches represent promising and powerful analytical representations of the CAV platoons under the differential games. Simulation experiments have verified the efficiency of the proposed models and their solutions.
AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle
Authors: Catalin Stefan Teodorescu, Tom Carlson
Date: 2022-02-05 00:00:00

In this article we design a physically-inspired model-based assist-as-needed semi-autonomous control (ASC) algorithm to address the problem of safely driving a vehicle (a power wheelchair) in an environment with static obstacles. Once implemented online, the proposed algorithm requires limited computing power and relies on pre-computed (offline) maps (look-up tables). These are readily available by implementing policy iteration that minimizes the expected time to termination (safely stopping near an obstacle), by taking into account: (i) the vehicle dynamics; (ii) the drivers' intention modeled as three separate stochastic processes. We call them the expert driver, the naughty child and the blind driver models. A study with healthy participants confirmed that ASC outperforms a baseline rule-based control (a statistically significant result).
On discretization of continuous-time LPV control solutions
Authors: Yorick Broens, Hans Butler, Roland Tóth
Date: 2022-02-04 00:00:00

In recent years, the Linear Parameter-Varying (LPV) framework has become increasingly useful for analysis and control of time-varying systems. Generally, LPV control synthesis is performed in the continuous-time (CT) domain due to significantly more intuitive performance shaping methods in CT. However, the main complication of CT synthesis approaches is the successive implementation of the resulting CT control solutions on physical hardware. In the literature, several discretization methods have been developed for LPV systems. However, most of these approaches necessitate heavy nonlinear operations introduced by the discretization of these time-varying matrices or can introduce significant approximation error, thereby severely limiting implementation capabilities of CT LPV control solutions. Alternatively, the $w'$ discretization approach has been introduced in the LTI case to allow for preservation of the CT control. Based on these observations, this paper aims at extending the $w'$ discretization approach to LPV systems, such that implementation of CT LPV control solutions on physical hardware is simplified.
Stable cooperation emerges in stochastic multiplicative growth
Authors: Lorenzo Fant, Onofrio Mazzarisi, Emanuele Panizon, Jacopo Grilli
Date: 2022-02-06 00:00:00
Tasks: Sociology

Understanding the evolutionary stability of cooperation is a central problem in biology, sociology, and economics. There exist only a few known mechanisms that guarantee the existence of cooperation and its robustness to cheating. Here, we introduce a new mechanism for the emergence of cooperation in the presence of fluctuations. We consider agents whose wealth change stochastically in a multiplicative fashion. Each agent can share part of her wealth as public good, which is equally distributed among all the agents. We show that, when agents operate with long time-horizons, cooperation produce an advantage at the individual level, as it effectively screens agents from the deleterious effect of environmental fluctuations.
A mathematical model for inter-specific seagrass interactions: reproducing field observations for C. nodosa and C. prolifera
Authors: Eva Llabrés, Elvira Mayol, Núria Marbá, Tomás Sintes
Date: 2022-02-07 00:00:00

Seagrasses are vital organisms in coastal waters, and the drastic demise of their population in the last decades has worrying implications for marine ecosystems. Spatial models for seagrass meadows provide a mathematical framework to study their dynamical processes and emergent collective behavior. These models are crucial to predict the response of seagrasses to different global warming scenarios, analyze the resilience of existing seagrass distributions, and optimize restoration strategies. In this article, we propose a model that includes interactions among different species based on the clonal growth of seagrasses. We present a theoretical analysis of the model considering the specific case of the seagrass-seaweed interaction between {\it Cymodocea nodosa} and {\it Caulerpa prolifera}. Our simulations successfully reproduce field observations of shoot densities in mixed meadows in the Ebro River Delta in the Mediterranean Sea. Besides, the proposed model allows us to investigate the possible underlying mechanisms that mediate the interaction among the two macrophytes.
Paper-based printed CPW-fed antenna for Wi-Fi applications
Authors: Nitheesh M. Nair, Debdutta Ray, Parasuraman Swaminathan
Date: 2022-01-27 00:00:00

A paper-based co-planar waveguide (CPW) fed monopole antenna for Wi-Fi applications is proposed. The antenna is fabricated by printing a commercial silver nanoparticle (Ag NP) based ink on photo paper substrate. The antenna is designed as a single layer for the ease of fabrication, and it is designed to radiate at two frequencies, 2.4 and 5.8 GHz, which are suitable for Wi-Fi applications. The printed film exhibits good electrical conductivity, with a low sheet resistance of 114 m{\Omega}/sq comparable with commonly used conductive metal lines. The fabricated antenna demonstrates good radiative properties at both flat and bent conditions. This confirms the good flexible properties of the antenna making it compatible with mounting on curved surfaces. The performance of the fabricated antenna is also compared with a commercial rigid antenna by interfacing with a USB dongle. The printed antenna demonstrates better performance with respect to signal strength at specific distances when compared with the commercial antenna. This work demonstrates that rigid and long commercial antennas can be replaced with paper-based flexible and cheap antennas and incorporated with wearable technologies. Additionally, replacing Ag NPs with nanowires provides transparency without compromising on the electrical properties.
Mechanobiology of Collective Cell Migration in 3D Microenvironments
Authors: Alex M. Hruska, Haiqian Yang, Susan E. Leggett, Ming Guo, Ian Y. Wong
Date: 2022-02-06 00:00:00

Tumor cells invade individually or in groups, mediated by mechanical interactions between cells and their surrounding matrix. These multicellular dynamics are reminiscent of leader-follower coordination and epithelial-mesenchymal transitions (EMT) in tissue development, which may occur via dysregulation of associated molecular or physical mechanisms. However, it remains challenging to elucidate such phenotypic heterogeneity and plasticity without precision measurements of single cell behavior. The convergence of technological developments in live cell imaging, biophysical measurements, and 3D biomaterials are highly promising to reveal how tumor cells cooperate in aberrant microenvironments. Here, we highlight new results in collective migration from the perspective of cancer biology and bioengineering. First, we review the biology of collective cell migration. Next, we consider physics-inspired analyses based on order parameters and phase transitions. Further, we examine the interplay of metabolism and heterogeneity in collective migration. We then review the extracellular matrix, and new modalities for mechanical characterization of 3D biomaterials. We also explore epithelial-mesenchymal plasticity and implications for tumor progression. Finally, we speculate on future directions for integrating mechanobiology and cancer cell biology to elucidate collective migration.
Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence
Authors: Haichao Zhan, Le Wang, Wennai Wang, Shengmei Zhao
Date: 2022-02-06 00:00:00

Vortex beam carrying orbital angular momentum (OAM) is disturbed by oceanic turbulence (OT) when propagating in underwater wireless optical communication (UWOC) system. Adaptive optics (AO) is used to compensate for distortion and improve the performance of the UWOC system. In this work, we propose a diffractive deep neural network (DDNN) based AO scheme to compensate for the distortion caused by OT, where the DDNN is trained to obtain the mapping between the distortion intensity distribution of the vortex beam and its corresponding phase screen representating OT. The intensity pattern of the distorted vortex beam obtained in the experiment is input to the DDNN model, and the predicted phase screen can be used to compensate the distortion in real time. The experiment results show that the proposed scheme can extract quickly the characteristics of the intensity pattern of the distorted vortex beam, and output accurately the predicted phase screen. The mode purity of the compensated vortex beam is significantly improved, even with a strong OT. Our scheme may provide a new avenue for AO techniques, and is expected to promote the communication quality of UWOC system.
Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model
Authors: Isuru Ratnayake, V. A. Samaranayake
Date: 2022-02-07 00:00:00
Tasks: model, Time Series, Time Series Analysis

This paper introduces a Threshold Asymmetric Conditional Autoregressive Range (TACARR) formulation for modeling the daily price ranges of financial assets. It is assumed that the process generating the conditional expected ranges at each time point switches between two regimes, labeled as upward market and downward market states. The disturbance term of the error process is also allowed to switch between two distributions depending on the regime. It is assumed that a self-adjusting threshold component that is driven by the past values of the time series determines the current market regime. The proposed model is able to capture aspects such as asymmetric and heteroscedastic behavior of volatility in financial markets. The proposed model is an attempt at addressing several potential deficits found in existing price range models such as the Conditional Autoregressive Range (CARR), Asymmetric CARR (ACARR), Feedback ACARR (FACARR) and Threshold Autoregressive Range (TARR) models. Parameters of the model are estimated using the Maximum Likelihood (ML) method. A simulation study shows that the ML method performs well in estimating the TACARR model parameters. The empirical performance of the TACARR model was investigated using IBM index data and results show that the proposed model is a good alternative for in-sample prediction and out-of-sample forecasting of volatility. Key Words: Volatility Modeling, Asymmetric Volatility, CARR Models, Regime Switching.
Predicting Default Probabilities for Stress Tests: A Comparison of Models
Authors: Martin Guth
Date: 2022-02-07 00:00:00

Since the Great Financial Crisis (GFC), the use of stress tests as a tool for assessing the resilience of financial institutions to adverse financial and economic developments has increased significantly. One key part in such exercises is the translation of macroeconomic variables into default probabilities for credit risk by using macrofinancial linkage models. A key requirement for such models is that they should be able to properly detect signals from a wide array of macroeconomic variables in combination with a mostly short data sample. The aim of this paper is to compare a great number of different regression models to find the best performing credit risk model. We set up an estimation framework that allows us to systematically estimate and evaluate a large set of models within the same environment. Our results indicate that there are indeed better performing models than the current state-of-the-art model. Moreover, our comparison sheds light on other potential credit risk models, specifically highlighting the advantages of machine learning models and forecast combinations.
Detecting Structural Breaks in Foreign Exchange Markets by using the group LASSO technique
Authors: Mikio Ito
Date: 2022-02-07 00:00:00
Tasks: Time Series, Time Series Analysis

This article proposes an estimation method to detect breakpoints for linear time series models with their parameters that jump scarcely. Its basic idea owes the group LASSO (group least absolute shrinkage and selection operator). The method practically provides estimates of such time-varying parameters of the models. An example shows that our method can detect each structural breakpoint's date and magnitude.
Difference in Differences with Time-Varying Covariates
Authors: Carolina Caetano, Brantly Callaway, Stroud Payne, Hugo Sant'Anna Rodrigues
Date: 2022-02-07 00:00:00
Tasks: Econometrics, Imputation

This paper considers identification and estimation of causal effect parameters from participating in a binary treatment in a difference in differences (DID) setup when the parallel trends assumption holds after conditioning on observed covariates. Relative to existing work in the econometrics literature, we consider the case where the value of covariates can change over time and, potentially, where participating in the treatment can affect the covariates themselves. We propose new empirical strategies in both cases. We also consider two-way fixed effects (TWFE) regressions that include time-varying regressors, which is the most common way that DID identification strategies are implemented under conditional parallel trends. We show that, even in the case with only two time periods, these TWFE regressions are not generally robust to (i) time-varying covariates being affected by the treatment, (ii) treatment effects and/or paths of untreated potential outcomes depending on the level of time-varying covariates in addition to only the change in the covariates over time, (iii) treatment effects and/or paths of untreated potential outcomes depending on time-invariant covariates, (iv) treatment effect heterogeneity with respect to observed covariates, and (v) violations of strong functional form assumptions, both for outcomes over time and the propensity score, that are unlikely to be plausible in most DID applications. Thus, TWFE regressions can deliver misleading estimates of causal effect parameters in a number of empirically relevant cases. We propose both doubly robust estimands and regression adjustment/imputation strategies that are robust to these issues while not being substantially more challenging to implement.
Adaptive information-based methods for determining the co-integration rank in heteroskedastic VAR models
Authors: H. Peter Boswijk, Giuseppe Cavaliere, Luca De Angelis, A. M. Robert Taylor
Date: 2022-02-05 00:00:00

Standard methods, such as sequential procedures based on Johansen's (pseudo-)likelihood ratio (PLR) test, for determining the co-integration rank of a vector autoregressive (VAR) system of variables integrated of order one can be significantly affected, even asymptotically, by unconditional heteroskedasticity (non-stationary volatility) in the data. Known solutions to this problem include wild bootstrap implementations of the PLR test or the use of an information criterion, such as the BIC, to select the co-integration rank. Although asymptotically valid in the presence of heteroskedasticity, these methods can display very low finite sample power under some patterns of non-stationary volatility. In particular, they do not exploit potential efficiency gains that could be realised in the presence of non-stationary volatility by using adaptive inference methods. Under the assumption of a known autoregressive lag length, Boswijk and Zu (2022) develop adaptive PLR test based methods using a non-parameteric estimate of the covariance matrix process. It is well-known, however, that selecting an incorrect lag length can significantly impact on the efficacy of both information criteria and bootstrap PLR tests to determine co-integration rank in finite samples. We show that adaptive information criteria-based approaches can be used to estimate the autoregressive lag order to use in connection with bootstrap adaptive PLR tests, or to jointly determine the co-integration rank and the VAR lag length and that in both cases they are weakly consistent for these parameters in the presence of non-stationary volatility provided standard conditions hold on the penalty term. Monte Carlo simulations are used to demonstrate the potential gains from using adaptive methods and an empirical application to the U.S. term structure is provided.
Is Metaverse LAND a good investment? It depends on your unit of account!
Authors: Voraprapa Nakavachara, Kanis Saengchote
Date: 2022-02-07 00:00:00
Tasks: Management, Sand

The Sandbox metaverse LAND non-fungible token (NFT) prices increased by than 300 times (in USD) between December 2019 and January 2022, but when measured in its native utility token (SAND), the increase is only 3 times. Depending on how prices are denominated, investment returns and effective transaction prices vary. We analyze more than 71,000 transactions and find that users are willing to pay 3-4% more when transactions are settled in SAND, and 30% less when settled in wETH (a smart contract version of ETH) when compared to ETH, so unit of account matters. Our results contribute to the discussions of blockchain-based, virtual economy management and the digitalization of money (Brunnermeier et al., 2019).