pwc-archive/papers-with-abstracts huggingface dataset:
Programmable Transimpedance Amplifier with Integrated Bandgap Reference for Glucose Concentration Measurement
Authors: Riyaz Ahmad, Amit M. Joshi, Dharmendra Boolchandani
Date: 2023-05-21 00:00:00

For glucose electrochemical sensors, a comprehensive electronics interface is designed and constructed in 0.18 um, CMOS process technology, and 1.5 V supply voltage. This interface includes a programmable readout amplifier and bandgap reference voltage potentiostat circuit. The programmable transimpedance amplifier (PTIA), the proposed readout circuit, provides a large dynamic range and low noise. The overall transimpedance increase for the PTIA is 17.3-50.5 kohm. For an input current range of 4.2-180 uA, the PTIA response has a linear output voltage range of 0.55-1.44 V. The output rms noise value is calculated to be 5.101 Vrms, and the overall power consumption of the design is 2.33 mW. The THD percentage spans from 7.6 to 10.2 in the current range mentioned above. All bandgap reference voltage potentiostat measurements are made using the reference potential of 0.6 V. The working electrode was a glassy carbon electrode (GCE) loaded with a CuO/Cu0:76CO2:25O4 (copper cobaltite) coating. An electrochemical glucose sensing setup has been used to measure glucose concentrations between 1 and 10 mM, and an emulated circuit has been used to verify the viability of the proposed glucose sensing design. The suggested glucose sensor architecture has a total size of 0.0684 mm2.
Deadline-Constrained Opportunistic Spectrum Access With Spectrum Handoff
Authors: Zhaolong Xue, Aoyu Gong, Yuan-Hsun Lo, Sirui Tian, Yijin Zhang
Date: 2023-05-21 00:00:00

This paper considers designing an optimal policy for deadline-constrained access in cognitive radio networks, where a secondary user needs to complete a packet transmission over the vacant spectrum within a delivery deadline. To minimize the total access cost, it is desirable to design an optimal opportunistic access policy by utilizing channel dynamics and sensing outcomes. We take non-negligible switching overheads, a state-dependent overtime penalty, and practical switching operations into consideration in the Markov decision process formulation of such an access problem under wide-band sensing. Moreover, we establish the existence of monotone optimal decision rules to reduce the complexity of computing an optimal policy. Simulation results verify our theoretical studies and the cost advantage over other policies.
Motion planning for parabolic equations using flatness and finite-difference approximations
Authors: Soham Chatterjee, Vivek Natarajan
Date: 2023-05-21 00:00:00
Tasks: Motion Planning

We consider the problem of finding an input signal which transfers a linear boundary controlled 1D parabolic partial differential equation with spatially-varying coefficients from a given initial state to a desired final state. The initial and final states have certain smoothness and the transfer must occur over a given time interval. We address this motion planning problem by first discretizing the spatial derivatives in the parabolic equation using the finite-difference approximation to obtain a linear ODE in time. Then using the flatness approach we construct an input signal that transfers this ODE between states determined by the initial and final states of the parabolic equation. We prove that, as the discretization step size converges to zero, this input signal converges to a limiting input signal which can perform the desired transfer for the parabolic equation. While earlier works have applied this motion planning approach to constant coefficient parabolic equations, this is the first work to investigate and establish the efficacy of this approach for parabolic equations with discontinuous spatially-varying coefficients. Using this approach we can construct input signals which transfer the parabolic equation from one steady-state to another. We show that this approach yields a new proof for the null controllability of 1D linear parabolic equations containing discontinuous coefficients and also present a numerical scheme for constructing a null control input signal when the initial state is piecewise continuous.
A Unifying Passivity-Based Framework for Pressure and Volume Flow Rate Control in District Heating Networks
Authors: Felix Strehle, Juan E. Machado, Michele Cucuzzella, Albertus J. Malan, Jacquelien M. A. Scherpen, Sören Hohmann
Date: 2023-05-20 00:00:00

A fundamental precondition for the secure and efficient operation of district heating networks (DHNs) is a stable hydraulic behavior. However, the ongoing transition towards a sustainable heat supply, especially the rising integration of distributed heat sources and the increasingly meshed topologies, introduce complex and potentially destabilizing hydraulic dynamics. In this work, we propose a unifying, passivity-based framework which guarantees asymptotic stability of any forced hydraulic DHN equilibrium while allowing for meshed, time-varying topologies and different, dynamically interacting distributed heat sources. To establish the desired hydraulic equilibria, we propose decentralized, passivity-based pressure and volume flow rate controllers for the pumps and valves in the actuated DHN subsystems. In particular, we leverage the equilibrium-independent passivity (EIP) properties of the DHN subsystems, the skew-symmetric nature of their interconnections, and LaSalle's Invariance principle to assess asymptotic stability in a modular manner. The obtained results hold for the state-of-the-art as well as future DHN generations featuring, for example, multiple distributed heat sources, asymmetric pipe networks, and multiple temperature layers. We verify our findings by means of simulations.
Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
Authors: Wenqi Cui, Guanya Shi, Yuanyuan Shi, Baosen Zhang
Date: 2023-05-20 00:00:00
Tasks: Load Forecasting

Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controllers have been designed to optimize frequency control. These controllers, however, almost always assume that the net load in the system remains constant over a sufficiently long time. Given the intermittent and uncertain nature of renewable resources, it is becoming important to explicitly consider net load that is time-varying. This paper proposes an adaptive approach to frequency control in power systems with significant time-varying net load. We leverage the advances in short-term load forecasting, where the net load in the system can be accurately predicted using weather and other features. We integrate these predictions into the design of adaptive controllers, which can be seamlessly combined with most existing controllers including conventional droop control and emerging neural network-based controllers. We prove that the overall control architecture achieves frequency restoration decentralizedly. Case studies verify that the proposed method improves both transient and frequency-restoration performances compared to existing approaches.
Psychotic Markov Blankets: Striking a Free Energy Balance for Complex Adaptation
Authors: Inès Hipolito
Date: 2023-05-20 00:00:00

This paper proposes a framework for optimising the adaptation and attunement of a Complex Adaptive System (CAS) with its environments. The tendency towards stability can be explained by minimising free energy but high variability, noise, and over-specialized rigidity can lead to a "stuck state" in a CAS. Without perturbation (increasing free energy), the system remains stuck and unable to adapt to changing circumstances. The paper introduces the concept of 'psychotic' Markov blankets to understand and specify factors contributing to maladjustment conditions moving away from the minimum stuck state. The paper offers directions for optimising adaptation and attunement to be applied to real-world problems, from cells to behaviour, societies and ecosystems.
Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management
Authors: John S. Schreck, William Petzke, Pedro A. Jimenez, Thomas Brummet, Jason C. Knievel, Eric James, Branko Kosovic, David John Gagne
Date: 2023-05-17 00:00:00
Tasks: Management

Monitoring the fuel moisture content (FMC) of vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations with numerical weather prediction (NWP) models and satellite retrievals has enabled the development of machine learning (ML) models to estimate dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model and the High-Resolution Rapid Refresh (HRRR) NWP models, and static variables characterizing the surface properties, as well as surface reflectances and land surface temperature (LST) retrievals from the VIIRS instrument on board the Suomi-NPP satellite system. Extensive hyper-parameter optimization yielded skillful FMC models compared to a daily climatography RMSE (+44\%) and to an hourly climatography RMSE (+24\%). Furthermore, VIIRS retrievals were important predictors for estimating FMC, contributing significantly as a group due to their high band-correlation. In contrast, individual predictors in the HRRR group had relatively high importance according to the explainability techniques used. When both HRRR and VIIRS retrievals were not used as model inputs, the performance dropped significantly. If VIIRS retrievals were not used, the RMSE performance was worse. This highlights the importance of VIIRS retrievals in modeling FMC, which yielded better models compared to MODIS. Overall, the importance of the VIIRS group of predictors corroborates the dynamic relationship between the 10-h fuel and the atmosphere and soil moisture. These findings emphasize the significance of selecting appropriate data sources for predicting FMC with ML models, with VIIRS retrievals and selected HRRR variables being critical components in producing skillful FMC estimates.
Long-lead forecasts of wintertime air stagnation index in southern China using oceanic memory effects
Authors: Chenhong Zhou, Xiaorui Zhang, Meng Gao, Shanshan Liu, Yike Guo, Jie Chen
Date: 2023-05-16 00:00:00
Tasks: Management

Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants. To measure the atmosphere's ability to dilute air pollutants, Air Stagnation Index (ASI) has been introduced as an important meteorological index. Therefore, making long-lead ASI forecasts is vital to make plans in advance for air quality management. In this study, we found that autumn Ni\~no indices derived from sea surface temperature (SST) anomalies show a negative correlation with wintertime ASI in southern China, offering prospects for a prewinter forecast. We developed an LSTM-based model to predict the future wintertime ASI. Results demonstrated that multivariate inputs (past ASI and Ni\~no indices) achieve better forecast performance than univariate input (only past ASI). The model achieves a correlation coefficient of 0.778 between the actual and predicted ASI, exhibiting a high degree of consistency.
Properties of the ENCE and other MAD-based calibration metrics
Authors: Pascal Pernot
Date: 2023-05-17 00:00:00

The Expected Normalized Calibration Error (ENCE) is a popular calibration statistic used in Machine Learning to assess the quality of prediction uncertainties for regression problems. Estimation of the ENCE is based on the binning of calibration data. In this short note, I illustrate an annoying property of the ENCE, i.e. its proportionality to the square root of the number of bins for well calibrated or nearly calibrated datasets. A similar behavior affects the calibration error based on the variance of z-scores (ZVE), and in both cases this property is a consequence of the use of a Mean Absolute Deviation (MAD) statistic to estimate calibration errors. Hence, the question arises of which number of bins to choose for a reliable estimation of calibration error statistics. A solution is proposed to infer ENCE and ZVE values that do not depend on the number of bins for datasets assumed to be calibrated, providing simultaneously a statistical calibration test. It is also shown that the ZVE is less sensitive than the ENCE to outstanding errors or uncertainties.
Novel deep learning methods for 3D flow field segmentation and classification
Authors: Xiaorui Bai, Wenyong Wang, Jun Zhang, Yueqing Wang, Yu Xiang
Date: 2023-05-10 00:00:00
Tasks: Classification, Deep Learning, Segmentation

Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow. Existing deep learning methods mainly based on global information and focused on 2D circumstance. Based on flow field theory, we propose novel flow field segmentation and classification deep learning methods in three-dimensional space. We construct segmentation criterion based on local velocity information and classification criterion based on the relationship between local vorticity and vortex wake, to identify vortex structure in 3D flow field, and further classify the type of vortex wakes accurately and rapidly. Simulation experiment results showed that, compared with existing methods, our segmentation method can identify the vortex area more accurately, while the time consumption is reduced more than 50%; our classification method can reduce the time consumption by more than 90% while maintaining the same classification accuracy level.
Megahertz X-ray Multi-projection imaging
Authors: Pablo Villanueva-Perez, Valerio Bellucci, Yuhe Zhang, Sarlota Birnsteinova, Rita Graceffa, Luigi Adriano, Eleni Myrto Asimakopoulou, Ilia Petrov, Zisheng Yao, Marco Romagnoni, Andrea Mazzolari, Romain Letrun, Chan Kim, Jayanath C. P. Koliyadu, Carsten Deiter, Richard Bean, Gabriele Giovanetti, Luca Gelisio, Tobias Ritschel, Adrian Mancuso, Henry N. Chapman, Alke Meents, Tokushi Sato, Patrik Vagovic
Date: 2023-05-19 00:00:00

X-ray time-resolved tomography is one of the most popular X-ray techniques to probe dynamics in three dimensions (3D). Recent developments in time-resolved tomography opened the possibility of recording kilohertz-rate 3D movies. However, tomography requires rotating the sample with respect to the X-ray beam, which prevents characterization of faster structural dynamics. Here, we present megahertz (MHz) X-ray multi-projection imaging (MHz-XMPI), a technique capable of recording volumetric information at MHz rates and micrometer resolution without scanning the sample. We achieved this by harnessing the unique megahertz pulse structure and intensity of the European X-ray Free-electron Laser with a combination of novel detection and reconstruction approaches that do not require sample rotations. Our approach enables generating multiple X-ray probes that simultaneously record several angular projections for each pulse in the megahertz pulse burst. We provide a proof-of-concept demonstration of the MHz-XMPI technique's capability to probe 4D (3D+time) information on stochastic phenomena and non-reproducible processes three orders of magnitude faster than state-of-the-art time-resolved X-ray tomography, by generating 3D movies of binary droplet collisions. We anticipate that MHz-XMPI will enable in-situ and operando studies that were impossible before, either due to the lack of temporal resolution or because the systems were opaque (such as for MHz imaging based on optical microscopy).
Identification and Estimation of Production Function with Unobserved Heterogeneity
Authors: Hiroyuki Kasahara, Paul Schrimpf, Michio Suzuki
Date: 2023-05-20 00:00:00

This paper examines the nonparametric identifiability of production functions, considering firm heterogeneity beyond Hicks-neutral technology terms. We propose a finite mixture model to account for unobserved heterogeneity in production technology and productivity growth processes. Our analysis demonstrates that the production function for each latent type can be nonparametrically identified using four periods of panel data, relying on assumptions similar to those employed in existing literature on production function and panel data identification. By analyzing Japanese plant-level panel data, we uncover significant disparities in estimated input elasticities and productivity growth processes among latent types within narrowly defined industries. We further show that neglecting unobserved heterogeneity in input elasticities may lead to substantial and systematic bias in the estimation of productivity growth.
The Missing Link: Exploring the Relationship Between Transformational Leadership and Change in team members in Construction
Authors: M. R. Ibrahim
Date: 2023-05-22 00:00:00

This study aimed to investigate how transformational leadership affects team processes, mediated by change in team members. A self-administered questionnaire was distributed to construction project team members in Abuja and Kaduna, and statistical analysis revealed a significant positive relationship between transformational leadership and team processes, transformational leadership and change in team members, changes in team members and team processes, and changes in team members mediating the relationship between transformational leadership and team processes. Future studies should consider cultural differences.
The Key to Organizational and construction Excellence: A Study of Total Quality Management
Authors: M. R. Ibrahim, D. U. Muhammad, B. Muhammad, J. O. Alaezi, J. Agidani
Date: 2023-05-22 00:00:00
Tasks: Management

This study examines the impact of Total Quality Management (TQM) practices on organizational outcomes. Results show a significant relationship between TQM practices such as top executive commitment, education and teaching, process control, and continuous progress, and how they can be leveraged to enhance performance outcomes.
Ownership Chains in Multinational Enterprises
Authors: Stefania Miricola, Armando Rungi, Gianluca Santoni
Date: 2023-05-22 00:00:00
Tasks: Management

This study examines how multinational enterprises (MNEs) structure ownership chains to coordinate subsidiaries across multiple national borders. Using a unique global dataset, we first document key stylized facts: 54% of subsidiaries are controlled through indirect ownership, and ownership chains can span up to seven countries. In particular, we find that subsidiaries further down the hierarchy tend to be more geographically distant from the parent and often operate in different time zones. This suggests that the ease of communication along ownership chains is a critical determinant of their structure. Motivated by these findings, we develop a location choice model in which parent firms compete for corporate control of final subsidiaries. When monitoring is costly, they may delegate control to an intermediate affiliate in another jurisdiction. The model generates a two-stage empirical strategy: (i) a trilateral equation that determines the location of an intermediate affiliate conditional on the location of final subsidiaries; and (ii) a bilateral equation that predicts the location of final investment. Our empirical estimates confirm that the ease of communication at the country level significantly influences the location decisions of affiliates along ownership chains. These findings underscore the importance of organizational frictions in shaping global corporate structures and provide new insights into the geography of multinational ownership networks.
The Emergence of Economic Rationality of GPT
Authors: Yiting Chen, Tracy Xiao Liu, You Shan, Songfa Zhong
Date: 2023-05-22 00:00:00

As large language models (LLMs) like GPT become increasingly prevalent, it is essential that we assess their capabilities beyond language processing. This paper examines the economic rationality of GPT by instructing it to make budgetary decisions in four domains: risk, time, social, and food preferences. We measure economic rationality by assessing the consistency of GPT's decisions with utility maximization in classic revealed preference theory. We find that GPT's decisions are largely rational in each domain and demonstrate higher rationality score than those of human subjects in a parallel experiment and in the literature. Moreover, the estimated preference parameters of GPT are slightly different from human subjects and exhibit a lower degree of heterogeneity. We also find that the rationality scores are robust to the degree of randomness and demographic settings such as age and gender, but are sensitive to contexts based on the language frames of the choice situations. These results suggest the potential of LLMs to make good decisions and the need to further understand their capabilities, limitations, and underlying mechanisms.
The Federal Reserve's Response to the Global Financial Crisis and Its Long-Term Impact: An Interrupted Time-Series Natural Experimental Analysis
Authors: Arnaud Cedric Kamkoum
Date: 2023-05-21 00:00:00
Tasks: Time Series

This paper examines the monetary policies the Federal Reserve implemented in response to the Global Financial Crisis. More specifically, it analyzes the Federal Reserve's quantitative easing (QE) programs, liquidity facilities, and forward guidance operations conducted from 2007 to 2018. The essay's detailed examination of these policies culminates in an interrupted time-series (ITS) analysis of the long-term causal effects of the QE programs on U.S. inflation and real GDP. The results of this formal design-based natural experimental approach show that the QE operations positively affected U.S. real GDP but did not significantly impact U.S. inflation. Specifically, it is found that, for the 2011Q2-2018Q4 post-QE period, real GDP per capita in the U.S. increased by an average of 231 dollars per quarter relative to how it would have changed had the QE programs not been conducted. Moreover, the results show that, in 2018Q4, ten years after the beginning of the QE programs, real GDP per capita in the U.S. was 14% higher relative to what it would have been during that quarter had there not been the QE programs. These findings contradict Williamson's (2017) informal natural experimental evidence and confirm the conclusions of VARs and new Keynesian DSGE models that the Federal Reserve's QE policies positively affected U.S. real GDP. The results suggest that the current U.S. and worldwide high inflation rates are likely not because of the QE programs implemented in response to the financial crisis that accompanied the COVID-19 pandemic. They are likely due to the unprecedentedly large fiscal stimulus packages used, the peculiar nature of the financial downturn itself, the negative supply shocks from the war in Ukraine, or a combination of these factors. This paper is the first study to measure the macroeconomic effects of QE using a design-based natural experimental approach.
Volatility jumps and the classification of monetary policy announcements
Authors: Giampiero M. Gallo, Demetrio Lacava, Edoardo Otranto
Date: 2023-05-20 00:00:00
Tasks: Classification

Central Banks interventions are frequent in response to exogenous events with direct implications on financial market volatility. In this paper, we introduce the Asymmetric Jump Multiplicative Error Model (AJM), which accounts for a specific jump component of volatility within an intradaily framework. Taking the Federal Reserve (Fed) as a reference, we propose a new model-based classification of monetary announcements based on their impact on the jump component of volatility. Focusing on a short window following each Fed's communication, we isolate the impact of monetary announcements from any contamination carried by relevant events that may occur within the same announcement day.