pwc-archive/papers-with-abstracts huggingface dataset:
Stability Analysis of Compartmental and Cooperative Systems
Authors: Sondre Wiersdalen, Mike Pereira, Annika Lang, Gabor Szederkenyi, Jean Auriol, Balazs Kulcsar
Date: 2023-12-18 00:00:00
Tasks: model

The present article considers stability of the solutions to nonlinear and nonautonomous compartmental systems governed by ordinary differential equations (ODEs). In particular, compartmental systems with a right-hand side that can be written as a product of a matrix function and vector function. Sufficient, and on occasion necessary, conditions on the matrix function are provided to conclude exponential stability of the null solution. The conditions involve verifying that the matrix function takes its values in a set of compartmental matrices on a certain canonical form, and are easy to check. Similar conditions are provided to establish incremental exponential stability for compartmental systems governed by cooperative systems of ODEs. The solutions to such systems satisfy a so-called ordering. Systems that are cooperative in a box, are shown to be incrementally asymptotically stable if and only if every pair of initially ordered solutions converge to each other. Traffic Reaction Models are used to illustrate the results, which are numerical schemes to solve conservation laws in one spatial dimension. Suitable conditions on the flux function of the conservation law are given such that the numerical scheme gives rise to an incrementally exponentially stable system.
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation
Authors: Zahra Ghasemi, Mehdi Neshat, Chris Aldrich, John Karageorgos, Max Zanin, Frank Neumann, Lei Chen
Date: 2023-12-18 00:00:00
Tasks: Evolutionary Algorithms, feature selection, Outlier Detection

In mineral processing plants, grinding is a crucial step, accounting for approximately 50 percent of the total mineral processing costs. Semi-autogenous grinding mills are extensively employed in the grinding circuit of mineral processing plants. Maximizing SAG mill throughput is of significant importance considering its profound financial outcomes. However, the optimum process parameter setting aimed at achieving maximum mill throughput remains an uninvestigated domain in prior research. This study introduces a hybrid intelligent framework leveraging expert knowledge, machine learning techniques, and evolutionary algorithms to address this research need. In this study, we utilize an extensive industrial dataset comprising 36743 records and select relevant features based on the insights of industry experts. Following the removal of erroneous data, a comprehensive evaluation of 17 diverse machine learning models is undertaken to identify the most accurate predictive model. To improve the model performance, feature selection and outlier detection are executed. The resultant optimal model, trained with refined features, serves as the objective function within three distinct evolutionary algorithms. These algorithms are employed to identify parameter configurations that maximize SAG mill throughput while adhering to the working limits of input parameters as constraints. Notably, our analysis revealed that CatBoost, as an ensemble model, stands out as the most accurate predictor. Furthermore, differential evolution emerges as the preferred optimization algorithm, exhibiting superior performance in both achieving the highest mill throughput predictions and ensuring robustness in predictions, surpassing alternative methods.
Distributed Localization in Dynamic Networks via Complex Laplacian
Authors: Xu Fang, Lihua Xie, Xiaolei Li
Date: 2023-12-18 00:00:00

Different from most existing distributed localization approaches in static networks where the agents in a network are static, this paper addresses the distributed localization problem in dynamic networks where the positions of the agents are time-varying. Firstly, complex constraints for the positions of the agents are constructed based on local relative position (distance and local bearing) measurements. Secondly, both algebraic condition and graph condition of network localizability in dynamic networks are given. Thirdly, a distributed localization protocol is proposed such that all the agents can cooperatively find their positions by solving the complex constraints in dynamic networks. Fourthly, the proposed method is extended to address the problem of integrated distributed localization and formation control. It is worth mentioning that the proposed algorithm can also be applied in the case that only distance and sign of direction measurements are available, where the sign of direction measurement is a kind of one bit local relative measurement and has less information than local bearing.
Nonparametric Stochastic Analysis of Dynamic Frequency in Power Systems: A Generalized Ito Process Model
Authors: Can Wan, Yupeng Ren, Ping Ju
Date: 2023-12-18 00:00:00

The large-scale integration of intermittent renewable energy has brought serious challenges to the frequency security of power systems. In this paper, a novel nonparametric stochastic analysis method of system dynamic frequency is proposed to accurately analyze the impact of renewable energy uncertainty on power system frequency security, independent of any parametric distribution assumption. The nonparametric uncertainty of renewable generation disturbance is quantified based on probabilistic forecasting. Then, a novel generalized Ito process is proposed as a linear combination of several Gaussian Ito processes, which can represent any probability distribution. Furthermore, a stochastic model of power system frequency response is constructed by considering virtual synchronization control of wind power. On basis of generalized Ito process, the complex nonlinear stochastic differential equation is transformed into a linear combination of several linear stochastic differential equations to approximate nonparametric probability distribution of the system dynamic frequency. Finally, the validity of the proposed method is verified by the single-machine system and IEEE 39-Bus system.
A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges and Opportunities
Authors: Chiu Chun Chan, Sheeraz A. Alvi, Xiangyun Zhou, Salman Durrani, Nicholas Wilson, Marta Yebra
Date: 2023-12-18 00:00:00
Tasks: Anomaly Detection

The threat posed by wildfires or bushfires has become a severe global issue due to the increase in human activities in forested areas and the impact of climate change. Consequently, there is a surge in the development of automatic wildfire detection methods. Approaches based on long-distance imagery from satellites or watchtowers encounter limitations, such as restricted visibility, which results in delayed response times. To address and overcome these challenges, research interest has grown in the implementation of ground-based Internet of Things (IoT) sensing systems for early wildfire detection. However, research on energy consumption, detection latency, and detection accuracy of IoT sensing systems, as well as the performance of various anomaly detection algorithms when evaluated using these metrics, is lacking. Therefore, in this article, we present an overview of current IoT ground sensing systems for early wildfire detection. Camera and environmental sensing technologies suitable for early wildfire detection are discussed, as well as vision-based detection algorithms and detection algorithms for environmental sensing. Challenges related to the development and implementation of IoT ground sensing systems for early wildfire detection and the future research directions important for creating a robust detection system to combat the growing threat of wildfires worldwide are discussed.
mRNA translation from a unidirectional traffic perspective
Authors: Binil Shyam T V, Rati Sharma
Date: 2023-12-19 00:00:00
Tasks: Translation

mRNA translation is a crucial process that leads to protein synthesis in living cells. Therefore, it is a process that needs to work optimally for a cell to stay healthy and alive. With advancements in microscopy and novel experimental techniques, a lot of the intricate details about the translation mechanism are now known. However, the why and how of this mechanism are still ill understood, and therefore, is an active area of research. Theoretical studies of mRNA translation typically view it in terms of the Totally Asymmetric Simple Exclusion Process or TASEP. Various works have used the TASEP model in order to study a wide range of phenomena and factors affecting translation, such as ribosome traffic on an mRNA under noisy (codon-dependent or otherwise) conditions, ribosome stalling, premature termination, ribosome reinitiation and dropoff, codon-dependent elongation and competition among mRNA for ribosomes, among others. In this review, we relay the history and physics of the translation process in terms of the TASEP framework. In particular, we discuss the viability and evolution of this model and its limitations while also formulating the reasons behind its success. Finally, we also identify gaps in the existing literature and suggest possible extensions and applications that will lead to a better understanding of ribosome traffic on the mRNA.
On the possibility of engineering social evolution in microfluidic environments
Authors: Gurdip Uppal, Dervis Can Vural
Date: 2023-12-18 00:00:00

Many species of microbes cooperate by producing public goods from which they collectively benefit. However, these populations are under the risk of being taken over by cheating mutants that do not contribute to the pool of public goods. Here we present theoretical findings that address how the social evolution of microbes can be manipulated by external perturbations, to inhibit or promote the fixation of cheaters. To control social evolution, we determine the effects of fluid-dynamical properties such as flow rate or boundary geometry. We also study the social evolutionary consequences of introducing beneficial or harmful chemicals at steady state and in a time dependent fashion. We show that by modulating the flow rate and by applying pulsed chemical signals, we can modulate the spatial structure and dynamics of the population, in a way that can select for more or less cooperative microbial populations.
Inferring the dynamics of ionic currents from recursive piecewise data assimilation of approximate neuron models
Authors: Stephen A. Wells, Joseph D. Taylor, Paul G. Morris, Alain Nogaret
Date: 2023-12-20 00:00:00

We construct neuron models from data by transferring information from an observed time series to the state variables and parameters of Hodgkin-Huxley models. When the learning period completes, the model will predict additional observations and its parameters uniquely characterise the complement of ion channels. However, the assimilation of biological data, as opposed to model data, is complicated by the lack of knowledge of the true neuron equations. Reliance on guessed conductance models is plagued with multi-valued parameter solutions. Here, we report on the distributions of parameters and currents predicted with intentionally erroneous models, over-specified models, and an approximate model fitting hippocampal neuron data. We introduce a recursive piecewise data assimilation (RPDA) algorithm that converges with near-perfect reliability when the model is known. When the model is unknown, we show model error introduces correlations between certain parameters. The ionic currents reconstructed from these parameters are excellent predictors of true currents and carry a higher degree of confidence, >95.5%, than underlying parameters, >53%. Unexpressed ionic currents are correctly filtered out even in the presence of mild model error. When the model is unknown, the covariance eigenvalues of parameter estimates are found to be a good gauge of model error. Our results suggest that biological information may be retrieved from data by focussing on current estimates rather than parameters.
FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation
Authors: Yi Xiao, Lei Bai, Wei Xue, Kang Chen, Tao Han, Wanli Ouyang
Date: 2023-12-16 00:00:00
Tasks: Weather Forecasting

Weather forecasting is a crucial yet highly challenging task. With the maturity of Artificial Intelligence (AI), the emergence of data-driven weather forecasting models has opened up a new paradigm for the development of weather forecasting systems. Despite the significant successes that have been achieved (e.g., surpassing advanced traditional physical models for global medium-range forecasting), existing data-driven weather forecasting models still rely on the analysis fields generated by the traditional assimilation and forecasting system, which hampers the significance of data-driven weather forecasting models regarding both computational cost and forecasting accuracy. In this work, we explore the possibility of coupling the data-driven weather forecasting model with data assimilation by integrating the global AI weather forecasting model, FengWu, with one of the most popular assimilation algorithms, Four-Dimensional Variational (4DVar) assimilation, and develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model and consider the temporal evolution of atmospheric dynamics to obtain accurate analysis fields for making predictions in a cycling manner without the help of physical models. Owning to the auto-differentiation ability of deep learning models, FengWu-4DVar eliminates the need of developing the cumbersome adjoint model, which is usually required in the traditional implementation of the 4DVar algorithm. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields for making accurate and efficient iterative predictions.
Why a Mayor cannot Change the Course of a Pandemic -- An agent-based Study on the Covid Spread on Local Level in Germany
Authors: Lucas Heger, Kerem Akdogan, Matthias Schott
Date: 2023-12-18 00:00:00

During the COVID-19 pandemic, a large variance of incidence rates on local level, e.g. cities and districts, within one country has been observed, while the same non-pharmaceutical measures have been taken to control the spread of the virus. This variance in incidence rates triggered the question, if the spread of incidence rates can be explained only by statistical processes and the local population statistics or if indeed other factors, e.g. local information campaigns, have to be considered. Within this paper we study the expected spread of incidence rates in the German State of Rhineland Palatinate during the second COVID-19 wave using an agent based simulation and find that the spread of incidence rates can be solely explained by population statistics and further statical effects.
Energy-saving technologies and energy efficiency in the post-pandemic world
Authors: Wadim Strielkowski, Larisa Gorina, Elena Korneeva, Olga Kovaleva
Date: 2023-12-18 00:00:00

This paper explores the role of energy-saving technologies and energy efficiency in the post-COVID era. The pandemic meant major rethinking of the entrenched patterns in energy saving and efficiency. It also provided opportunities for reevaluating energy consumption for households and industries. In addition, it highlighted the importance of employing digital tools and technologies in energy networks and smart grids (e.g. Internet of Energy (IoE), peer-to-peer (P2P) prosumer networks, or the AI-powered autonomous power systems (APS)). In addition, the pandemic added novel legal aspects to the energy efficiency and energy saving and enhanced inter-national collaborations and partnerships. The paper highlights the importance of energy efficiency measures and examines various technologies that can contribute to a sustainable and resilient energy future. Using the bibliometric network analysis of 12960 publications indexed in Web of Science databases, it demonstrates the potential benefits and challenges associated with implementing energy-saving technologies and autonomic power systems in a post-COVID world. Our findings emphasize the need for robust policies, technological advancements, and public engagement to foster energy efficiency and mitigate the environmental impacts of energy consumption.
Managing Demographic Transitions: A Comprehensive Analysis of China's Path to Economic Sustainability
Authors: Yuxin Hu
Date: 2023-12-19 00:00:00

This article presents an analysis of China's economic evolution amidst demographic changes from 1990 to 2050, offering valuable insights for academia and policymakers. It uniquely intertwines various economic theories with empirical data, examining the impact of an aging population, urbanization, and family dynamics on labor, demand, and productivity. The study's novelty lies in its integration of Classical, Neoclassical, and Endogenous Growth theories, alongside models like Barro and Sala-i-Martin, to contextualize China's economic trajectory. It provides a forward-looking perspective, utilizing econometric methods to predict future trends, and suggests practical policy implications. This comprehensive approach sheds light on managing demographic transitions in a global context, making it a significant contribution to the field of demographic economics.
Least-cost diets to teach optimization and consumer behavior, with applications to health equity, poverty measurement and international development
Authors: Jessica K. Wallingford, William A. Masters
Date: 2023-12-19 00:00:00

The least-cost diet problem introduces students to optimization and linear programming, using the health consequences of food choice. We provide a graphical example, Excel workbook and Word template using actual data on item prices, food composition and nutrient requirements for a brief exercise in which students guess at and then solve for nutrient adequacy at lowest cost, before comparing modeled diets to actual consumption which has varying degrees of nutrient adequacy. The graphical example is a 'three sisters' diet of corn, beans and squash, and the full multidimensional model is compared to current food consumption in Ethiopia. This updated Stigler diet shows how cost minimization relates to utility maximization, and links to ongoing research and policy debates about the affordability of healthy diets worldwide.
Endogenous preference for non-market goods in carbon abatement decision
Authors: Fangzhi Wang, Hua Liao, Richard S. J. Tol, Changjing Ji
Date: 2023-12-18 00:00:00

Carbon abatement decisions are usually based on the implausible assumption of constant social preference. This paper focuses on a specific case of market and non-market goods, and investigates the optimal climate policy when social preference for them is also changed by climate policy in the DICE model. The relative price of non-market goods grows over time due to increases in both relative scarcity and appreciation of it. Therefore, climbing relative price brings upward the social cost of carbon denominated in terms of market goods. Because abatement decision affects the valuation of non-market goods in the utility function, unlike previous climate-economy models, we solve the model iteratively by taking the obtained abatement rates from the last run as inputs in the current run. The results in baseline calibration advocate a more stringent climate policy, where endogenous social preference to climate policy raises the social cost of carbon further by roughly 12%-18% this century. Moreover, neglecting changing social preference leads to an underestimate of non-market goods damages by 15%. Our results support that climate policy is self-reinforced if it favors more expensive consumption type.
Effects of Daily Exercise Time on the Academic Performance of Students: An Empirical Analysis Based on CEPS Data
Authors: Ningyi Li
Date: 2023-11-30 00:00:00

This paper examines the effects of daily exercise time on the academic performance of junior high school students in China, with an attempt to figure out the most appropriate daily exercise time for students from the perspective of improving students' test scores. By dividing the daily exercise time into five sections to construct a categorical variable in a linear regression model as well as using another model to draw intuitive figures, I find that spending both too little and too much time on physical activity every day would have adverse impacts on students' academic performance, with differences existing in the impacts by gender, grade, city scale, and location type of the school. The findings of this paper carry implications for research, school health and education policy and physical and general education practice. The results also provide recommendations for students, parents and teachers.
Nudging Nutrition: Lessons from the Danish "Fat Tax"
Authors: Christian Møller Dahl, Nadja van 't Hoff, Giovanni Mellace, Sinne Smed
Date: 2023-11-29 00:00:00
Tasks: Nutrition

In October 2011, Denmark introduced the world's first and, to date, only tax targeting saturated fat. However, this tax was subsequently abolished in January 2013. Leveraging exogenous variation from untaxed Northern-German consumers, we employ a difference-in-differences approach to estimate the causal effects of both the implementation and repeal of the tax on consumption and expenditure behavior across eight product categories targeted by the tax. Our findings reveal significant heterogeneity in the tax's impact across these products. During the taxed period, there was a notable decline in consumption of bacon, liver sausage, and cheese, particularly among low-income households. In contrast, expenditure on butter, cream, and margarine increased as prices rose. Interestingly, we do not observe any difference in expenditure increases between high and low-income households, suggesting that the latter were disproportionately affected by the tax. After the repeal of the tax, we do not observe any significant decline in consumption. On the contrary, there was an overall increase in consumption for certain products, prompting concerns about unintended consequences resulting from the brief implementation of the tax. Finally, we find strong evidence on an overall increase purchases of butter abroad for households living less than 50 km from the German boarder but we do not find strong evidence of spatial heterogeneous effects of the tax.
The Market for Lemons and the Regulator's Signalling Problem
Authors: Roy Long
Date: 2023-12-18 00:00:00

The Market for Lemons is a classic model of asymmetric information first studied by Nobel Prize economist George Akerlof. It shows that information asymmetry between the seller and buyer may result in market collapse or some sellers leaving the market. "Lemons" in the used car market are cars of poor quality. The information asymmetry present is that the buyer is uncertain of the cars' true quality. I first offer a simple baseline model that illustrates the market collapse, and then examine what happens when regulation, ie. a DMV is introduced to reveal (signal) the true car quality to the buyer. The effect on the market varies based on the assumptions about the regulator. The central focus is on the DMV's signal structure, which can have interesting effects on the market and the information asymmetry. I show that surprisingly, when the DMV actually decreases the quality of their signal in a well constructed way, it can substantially increase their profit. On the other hand, this negatively effects overall welfare.