pwc-archive/papers-with-abstracts huggingface dataset:
CAV Traffic Control to Mitigate the Impact of Congestion from Bottlenecks: A Linear Quadratic Regulator Approach and Microsimulation Study
Authors: Suyash C. Vishnoi, Junyi Ji, MirSaleh Bahavarnia, Yuhang Zhang, Ahmad F. Taha, Christian G. Claudel, Daniel B. Work
Date: 2023-06-17 00:00:00
Tasks: Model Predictive Control

This work investigates traffic control via controlled connected and automated vehicles (CAVs) using novel controllers derived from the linear-quadratic regulator (LQR) theory. CAV-platoons are modeled as moving bottlenecks impacting the surrounding traffic with their speeds as control inputs. An iterative controller algorithm based on the LQR theory is proposed along with a variant that allows for penalizing abrupt changes in platoons speeds. The controllers use the Lighthill-Whitham-Richards (LWR) model implemented using an extended cell transmission model (CTM) which considers the capacity drop phenomenon for a realistic representation of traffic in congestion. The impact of various parameters of the proposed controller on the control performance is analyzed. The effectiveness of the proposed traffic control algorithms is tested using a traffic control example and compared with existing proportional-integral (PI)- and model predictive control (MPC)- based controllers from the literature. A case study using the TransModeler traffic microsimulation software is conducted to test the usability of the proposed controller as well as existing controllers in a realistic setting and derive qualitative insights. It is observed that the proposed controller works well in both settings to mitigate the impact of the jam caused by a fixed bottleneck. The computation time required by the controller is also small making it suitable for real-time control.
Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data
Authors: Huandong Wang, Huan Yan, Can Rong, Yuan Yuan, Fenyu Jiang, Zhenyu Han, Hongjie Sui, Depeng Jin, Yong Li
Date: 2023-06-17 00:00:00

Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability to overcome the challenges of complex system simulation with unknown mechanisms and expensive computational costs. In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data. Firstly, we will present background knowledge about simulating complex system simulation and the scales in complex systems. Then, we divide the main objectives of multi-scale modeling and simulation into five categories by considering scenarios with clear scale and scenarios with unclear scale, respectively. After summarizing the general methods for multi-scale simulation based on the clues of knowledge and data, we introduce the adopted methods to achieve different objectives. Finally, we introduce the applications of multi-scale simulation in typical matter systems and social systems.
Output Voltage Response Improvement and Ripple Reduction Control for Input-parallel Output-parallel High-Power DC Supply
Authors: Jianhui Meng, Xiaolong Wu, Tairan Ye, Jingsen Yu, Likang Gu, Zili Zhang, Yang Li
Date: 2023-06-16 00:00:00

A three-phase isolated AC-DC-DC power supply is widely used in the industrial field due to its attractive features such as high-power density, modularity for easy expansion and electrical isolation. In high-power application scenarios, it can be realized by multiple AC-DC-DC modules with Input-Parallel Output-Parallel (IPOP) mode. However, it has the problems of slow output voltage response and large ripple in some special applications, such as electrophoresis and electroplating. This paper investigates an improved Adaptive Linear Active Disturbance Rejection Control (A-LADRC) with flexible adjustment capability of the bandwidth parameter value for the high-power DC supply to improve the output voltage response speed. To reduce the DC supply ripple, a control strategy is designed for a single module to adaptively adjust the duty cycle compensation according to the output feedback value. When multiple modules are connected in parallel, a Hierarchical Delay Current Sharing Control (HDCSC) strategy for centralized controllers is proposed to make the peaks and valleys of different modules offset each other. Finally, the proposed method is verified by designing a 42V/12000A high-power DC supply, and the results demonstrate that the proposed method is effective in improving the system output voltage response speed and reducing the voltage ripple, which has significant practical engineering application value.
Mutual Interference Mitigation in PMCW Automotive Radar
Authors: Zahra Esmaeilbeig, Arindam Bose, Mojtaba Soltanalian
Date: 2023-06-16 00:00:00

This paper addresses the challenge of mutual interference in phase-modulated continuous wave (PMCW) millimeter-wave (mmWave) automotive radar systems. The increasing demand for advanced driver assistance systems (ADAS) has led to a proliferation of vehicles equipped with mmWave radar systems that operate in the same frequency band, resulting in mutual interference that can degrade radar performance creating safety hazards. We consider scenarios involving two similar PMCW radar systems and propose an effective technique for a cooperative design of transmit waveforms such that the mutual interference between them is minimized. The proposed approach is numerically evaluated via simulations of a mmWave automotive radar system. The results demonstrate that the proposed technique notably reduces mutual interference and enhances radar detection performance while imposing very little computational cost and a negligible impact on existing infrastructure in practical automotive radar systems
Chaotic turnover of rare and abundant species in a strongly interacting model community
Authors: Emil Mallmin, Arne Traulsen, Silvia De Monte
Date: 2023-06-19 00:00:00
Tasks: Diversity

The composition of ecological communities varies not only between different locations but also in time. Understanding the fundamental processes that drive species towards rarity or abundance is crucial to assessing ecosystem resilience and adaptation to changing environmental conditions. In plankton communities in particular, large temporal fluctuations in species abundances have been associated with chaotic dynamics. On the other hand, microbial diversity is overwhelmingly sustained by a `rare biosphere' of species with very low abundances. We consider here the possibility that interactions within a species-rich community can relate both phenomena. We use a Lotka-Volterra model with weak immigration and strong, disordered, and mostly competitive interactions between hundreds of species to bridge single-species temporal fluctuations and abundance distribution patterns. We highlight a generic chaotic regime where a few species at a time achieve dominance, but are continuously overturned by the invasion of formerly rare species. We derive a focal-species model that captures the intermittent boom-and-bust dynamics that every species undergoes. Although species cannot be treated as effectively uncorrelated in their abundances, the community's effect on a focal species can nonetheless be described by a time-correlated noise characterized by a few effective parameters that can be estimated from time series. The model predicts a non-unitary exponent of the power-law abundance decay, which varies weakly with ecological parameters, consistent with observation in marine protist communities. The chaotic turnover regime is thus poised to capture relevant ecological features of species-rich microbial communities.
Conditionally unutilized proteins and their profound effects on growth and adaptation across microbial species
Authors: Rohan Balakrishnan, Jonas Cremer
Date: 2023-06-19 00:00:00

Protein synthesis is an important determinant of microbial growth and response that demands a high amount of metabolic and biosynthetic resources. Despite these costs, microbial species from different taxa and habitats massively synthesize proteins that are not utilized in the conditions they currently experience. Based on resource allocation models, recent studies have begun to reconcile the costs and benefits of these conditionally unutilized proteins (CUPs) in the context of varying environmental conditions. Such massive synthesis of CUPs is crucial to consider in different areas of modern microbiology, from the systematic investigation of cell physiology, via the prediction of evolution in laboratory and natural environments, to the rational design of strains in biotechnology applications.
Determining cancer cells division strategy
Authors: Mattia Miotto, Simone Scalise, Marco Leonetti, Giancarlo Ruocco, Giovanna Peruzzi, Giorgio Gosti
Date: 2023-06-19 00:00:00

Heterogeneity in the size distribution of cancer cell populations has been recently linked to drug resistance and invasiveness. However, despite many progresses have been made in understanding how such heterogeneous size distributions arise in fast-proliferating cell types -like bacteria and yeast-, comprehensive investigations on cancer cell populations are still lacking mainly due to the difficulties of monitoring the proliferation of the time scales typical of mammalian cells. From a reductionist cell dynamics point of view, the strategies allowing size homeostasis are roughly grouped into three classes, \emph{i.e.} timer, sizer, or adder. These strategies are empirically distinguishable given the phenomenological measurable relationship between the cell size at birth and at division, which requires following the proliferation at the single-cell level. Here, we show how it is possible to infer the growth regime and division strategy of leukemia cell populations using live cell fluorescence labeling and flow cytometry in combination with a quantitative analytical model where both cell growth and division rates depend on powers of the cell size. Using our novel approach, we found that the dynamics of the size distribution of leukemia Jurkat T-cells is quantitatively reproduced by (i) a sizer-like division strategy, with (ii) division times following an Erlang distribution given by the sum of at least three independent exponentially-distributed times and (iii) fluctuations up to 15\% of the inherited fraction of size at division with respect to the mother cell size. Finally, we note that our experimental and theoretical apparatus can be easily extended to other cell types and environmental conditions, allowing for a comprehensive characterization of the growth and division model different cells can adopt.
Frequent asymmetric migrations suppress natural selection in spatially structured populations
Authors: Alia Abbara, Anne-Florence Bitbol
Date: 2023-06-19 00:00:00

Natural microbial populations often have complex spatial structures. This can impact their evolution, in particular the ability of mutants to take over. While mutant fixation probabilities are known to be unaffected by sufficiently symmetric structures, evolutionary graph theory has shown that some graphs can amplify or suppress natural selection, in a way that depends on microscopic update rules. We propose a model of spatially structured populations on graphs directly inspired by batch culture experiments, alternating within-deme growth on nodes and migration-dilution steps, and yielding successive bottlenecks. This setting bridges models from evolutionary graph theory with Wright-Fisher models. Using a branching process approach, we show that spatial structure with frequent migrations can only yield suppression of natural selection. More precisely, in this regime, circulation graphs, where the total incoming migration flow equals the total outgoing one in each deme, do not impact fixation probability, while all other graphs strictly suppress selection. Suppression becomes stronger as the asymmetry between incoming and outgoing migrations grows. Amplification of natural selection can nevertheless exist in a restricted regime of rare migrations and very small fitness advantages, where we recover the predictions of evolutionary graph theory for the star graph.
Evaluation of an automated choroid segmentation algorithm in a longitudinal kidney donor and recipient cohort
Authors: Jamie Burke, Dan Pugh, Tariq Farrah, Charlene Hamid, Emily Godden, Tom MacGillivray, Neeraj Dhaun, J. Kenneth Baillie, Stuart King, Ian J. C. MacCormick
Date: 2023-06-19 00:00:00

Purpose: To evaluate the performance of an automated choroid segmentation algorithm in optical coherence tomography (OCT) data using a longitudinal kidney donor and recipient cohort. Methods: We assessed 22 donors and 23 patients requiring renal transplantation over up to 1 year post-transplant. We measured choroidal thickness (CT) and area and compared our automated CT measurements to manual ones at the same locations. We estimated associations between choroidal measurements and markers of renal function (estimated glomerular filtration rate (eGFR), serum creatinine and urea) using correlation and linear mixed-effects (LME) modelling. Results: There was good agreement between manual and automated CT. Automated measures were more precise because of smaller measurement error over time. External adjudication of major discrepancies were in favour of automated measures. Significant differences were observed in the choroid pre- and post-transplant in both cohorts, and LME modelling revealed significant linear associations observed between choroidal measures and renal function in recipients. Significant associations were mostly stronger with automated CT (eGFR P<0.001, creatinine P=0.004, urea P=0.04) compared to manual CT (eGFR P=0.002, creatinine P=0.01, urea P=0.03). Conclusions: Our automated approach has greater precision than human-performed manual measurements, which may explain stronger associations with renal function compared to manual measurements. To improve detection of meaningful associations with clinical endpoints in longitudinal studies of OCT, reducing measurement error should be a priority, and automated measurements help achieve this. Translational relevance: We introduce a novel choroid segmentation algorithm which can replace manual grading for studying the choroid in renal disease, and other clinical conditions.
Hypoxia-related radiotherapy resistance in tumours: treatment efficacy investigation in an eco-evolutionary perspective
Authors: Giulia Chiari, Giada Fiandaca, Marcello Edoardo Delitala
Date: 2023-06-10 00:00:00

In the study of therapeutic strategies for the treatment of cancer, eco-evolutionary dynamics are of particular interest, since characteristics of the tumour population, interaction with the environment and effects of the treatment, influence the geometric and epigenetic characterization of the tumour with direct consequences on the efficacy of the therapy and possible relapses. In particular, when considering radiotherapy, oxygen concentration plays a central role both in determining the effectiveness of the treatment and the selective pressure due to hypoxia. We propose a mathematical model, settled in the framework of epigenetically-structured population dynamics and formulated in terms of systems of coupled non-linear integro-differential equations, that aims to catch these phenomena and to provide a predictive tool for the tumour mass evolution and therapeutic effects. The outcomes of the simulations show how the model is able to explain the impact of environmental selection and therapies on the evolution of the mass, motivating observed dynamics such as relapses and therapeutic failures. Furthermore it offers a first hint for the development of therapies which can be adapted to overcome problems of resistance and relapses.
Agent-based modeling of the COVID-19 pandemic in Florida
Authors: Alexander N. Pillai, Kok Ben Toh, Dianela Perdomo, Sanjana Bhargava, Arlin Stoltzfus, Ira M. Longini Jr., Carl A. B. Pearson, Thomas J. Hladish
Date: 2023-06-19 00:00:00

The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like "Should we lockdown?" During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands.
On the Analytical Solution of Fractional SIR Epidemic Model
Authors: Ahmad Qazza, Rania Saadeh
Date: 2023-05-22 00:00:00

This article presents the solution of the fractional SIR epidemic model using the Laplace residual power series method. We introduce the fractional SIR model in the sense of Caputo's derivative, it is presented by three fractional differential equations, in which the third one depends on the first coupled equations. The Laplace residual power series method is implemented in this research to solve the proposed model, in which we present the solution in a form of convergent series expansion that converges rapidly to the exact one. We analyze the results and compare the obtained approximate solutions to those obtained from other methods. Figures and tables are illustrated to show the efficiency of the LRPSM in handling the proposed SIR model.
Formal Covariate Benchmarking to Bound Omitted Variable Bias
Authors: Deepankar Basu
Date: 2023-06-18 00:00:00
Tasks: Benchmarking, Sensitivity

Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking after residualizing the unobserved confounder on the set of observed covariates. In this paper, I explain the rationale and details of this procedure. I clarify some important details of the process of formal covariate benchmarking and highlight some of the difficulties of interpretation that researchers face in reasoning about the residualized part of unobserved confounders. I explain all the points with several empirical examples.
Marijuana on Main Streets? The Story Continues in Colombia: An Endogenous Three-part Model
Authors: A. Ramirez-Hassan, C. Gomez, S. Velasquez, K. Tangarife
Date: 2023-06-06 00:00:00

Cannabis is the most common illicit drug, and understanding its demand is relevant to analyze the potential implications of its legalization. This paper proposes an endogenous three-part model taking into account incidental truncation and access restrictions to study demand for marijuana in Colombia, and analyze the potential effects of its legalization. Our application suggests that modeling simultaneously access, intensive and extensive margin is relevant, and that selection into access is important for the intensive margin. We find that younger men that have consumed alcohol and cigarettes, living in a neighborhood with drug suppliers, and friends that consume marijuana face higher probability of having access and using this drug. In addition, we find that marijuana is an inelastic good (-0.45 elasticity). Our results are robust to different specifications and definitions. If marijuana were legalized, younger individuals with a medium or low risk perception about marijuana use would increase the probability of use in 3.8 percentage points, from 13.6% to 17.4%. Overall, legalization would increase the probability of consumption in 0.7 p.p. (2.3% to 3.0%). Different price settings suggest that annual tax revenues fluctuate between USD 11.0 million and USD 54.2 million, a potential benchmark is USD 32 million.
Uniform taxation of electricity: incentives for flexibility and cost redistribution among household categories
Authors: Philipp Andreas Gunkel, Febin Kachirayil, Claire-Marie Bergaentzlé, Russell McKenna, Dogan Keles, Henrik Klinge Jacobsen
Date: 2023-06-20 00:00:00

Recent years have shown a rapid adoption of residential solar PV with increased self-consumption and self-sufficiency levels in Europe. A major driver for their economic viability is the electricity tax exemption for the consumption of self-produced electricity. This leads to large residential PV capacities and partially overburdened distribution grids. Furthermore, the tax exemption that benefits wealthy households that can afford capital-intense investments in solar panels in particular has sparked discussions about energy equity and the appropriate taxation level for self-consumption. This study investigates the implementation of uniform electricity taxes on all consumption, irrespective of the origin of the production, by means of a case study of 155,000 hypothetical Danish prosumers. The results show that the new taxation policy redistributes costs progressively across household sizes. As more consumption is taxed, the tax level can be reduced by 38%, leading to 61% of all households seeing net savings of up to 23% off their yearly tax bill. High-occupancy houses save an average of 116 Euro per year at the expense of single households living in large dwellings who pay 55 Euro per year more. Implementing a uniform electricity tax in combination with a reduced overall tax level can (a) maintain overall tax revenues and (b) increase the interaction of batteries with the grid at the expense of behind-the-meter operations. In the end, the implicit cross-subsidy is removed by taxing self-consumption uniformly, leading to a cost redistribution supporting occupant-dense households and encouraging the flexible behavior of prosumers. This policy measure improves economic efficiency and greater use of technology with positive system-wide impacts.
Public Finance or Public Choice? The Scholastic Political Economy As an Essentialist Synthesis
Authors: Mohammadhosein Bahmanpour-Khalesi, Mohammadjavad Sharifzadeh
Date: 2023-06-19 00:00:00

Nowadays, it is thought that there are only two approaches to political economy: public finance and public choice; however, this research aims to introduce a new insight by investigating scholastic sources. We study the relevant classic books from the thirteenth to the seventeenth centuries and reevaluate the scholastic literature by doctrines of public finance and public choice. The findings confirm that the government is the institution for realizing the common good according to scholastic attitude. Therefore, scholastic thinkers saw a common mission for the government based on their essentialist attitude toward human happiness. Social conflicts and lack of social consent are the product of diversification in ends and desires; hence, if the end of humans were unified, there would be no conflict of interest. Accordingly, if the government acts according to its assigned mission, the lack of public consent is not significant. Based on the scholastic point of view this study introduces the third approach to political economy, which can be, consider an analytical synthesis among classical doctrines.