pwc-archive/papers-with-abstracts huggingface dataset:
Molecular Communication-Inspired Particle Collector-Transmitter (PaCoT) for Heavy Metal Removal from Human Circulatory System
Authors: Hilal Esra Yaldiz, Ozgur B. Akan
Date: 2025-01-10 00:00:00

This study proposes a novel molecular communication (MC)-inspired nanomachine, PArticle COllector-Transmitter (PaCoT), to remove toxic heavy metals from the human circulatory system. PaCoT collects these toxic metals and transmits them to release nodes, such as lymph capillaries, before they reach critical organs. The design incorporates key physical parameters and operates through particle reception and release mechanisms. In the reception process, described as ligand-receptor binding reactions, modeled as a continuous-time Markov process (CTMP), PaCoT uses metallothionein proteins as receptors and heavy metals (e.g., Zn, Pb, Cd) as ligands. We assume that the toxicity condition (toxic (bit-1), non-toxic (bit-0)) is encoded into the concentration of heavy metal molecules. Thus, we consider that heavy metal concentration within the MC channel (e.g., human circulatory system) employs binary concentration shift keying (binary CSK). The concentration ratio of specific heavy metals is estimated to infer toxicity, i.e., a high ratio indicates toxicity and a low ratio suggests non-toxicity. Toxicity detection is achieved by monitoring the receptor bound duration in the presence of interferers and various types of heavy metals. After detecting and collecting toxic heavy metals, PaCoT securely retains them in a liquid medium (e.g., water) until release, employing two mechanisms: (1) a single-disc viscous micropump to regulate flow rate, and (2) Brownian motion to facilitate diffusion. PaCoT's performance is evaluated through MATLAB simulations, focusing on bit error probability (BEP) of the toxicity detection method, release time of molecules from PaCoT and energy consumption.
Resiliency metrics quantifying emergency response in a distribution system
Authors: Shikhar Pandey, Gowtham Kandaperumal, Arslan Ahmad, Ian Dobson
Date: 2025-01-10 00:00:00

The electric distribution system is a cornerstone of modern life, playing a critical role in the daily activities and well-being of individuals. As the world transitions toward a decarbonized future, where even mobility relies on electricity, ensuring the resilience of the grid becomes paramount. This paper introduces novel resilience metrics designed to equip utilities and stakeholders with actionable tools to assess performance during storm events. The metrics focus on emergency storm response and the resources required to improve customer service. The practical calculation of the metrics from historical utility data is demonstrated for multiple storm events. Additionally, the metrics' improvement with added crews is estimated by "rerunning history" with faster restoration. By applying this resilience framework, utilities can enhance their restoration strategies and unlock potential cost savings, benefiting both providers and customers in an era of heightened energy dependency.
On the Interaction in Transient Stability of Two-Inverter Power Systems containing GFL inverter Using Manifold Method
Authors: Yifan Zhang, Yunjie Gu, Yue Zhu, Timothy C. Green, Hsiao-Dong Chiang
Date: 2025-01-10 00:00:00

Many renewable energy resources are integrated into power systems via grid-following (GFL) inverters which rely on a phase-locked loop (PLL) for grid synchronization. During severe grid faults, GFL inverters are vulnerable to transient instability, often leading to disconnection from the grid. This paper aims to elucidate the interaction mechanisms and define the stability boundaries of systems of two inverters, including GFL, grid-forming (GFM), or grid-supporting (GSP) inverters. First, the generalized large-signal expression for the two-inverter system under various inverter combinations is derived, revealing that no energy function exists for systems containing GFL inverters. This implies that the traditional direct method cannot be applied to such systems. To overcome these challenges, a manifold method is employed to precisely determine the domain of attraction (DOA) of the system, and the transient stability margin is assessed by a new metric termed the critical clearing radius (CCR). A case study of the two-inverter system under various inverter combinations is conducted to explore large-signal interactions across different scenarios. Manifold analysis and simulation results reveal that GSP inverters using PLL for grid synchronization exhibit behavior similar to GFM inverters when the droop coefficients in the terminal voltage control loop (TVC) are sufficiently large. Compared to GFL inverters, GSP inverters incorporating a TVC significantly enhances the transient stability of other inverters. In the STATCOM case, the optimal placement of the STATCOM, realized by GSP or GFM inverters, is identified to be at the midpoint of a transmission line. All findings in this paper are validated through electromagnetic transient (EMT) simulations
The Safe Trusted Autonomy for Responsible Space Program
Authors: Kerianne L. Hobbs, Sean Phillips, Michelle Simon, Joseph B. Lyons, Jared Culbertson, Hamilton Scott Clouse, Nathaniel Hamilton, Kyle Dunlap, Zachary S. Lippay, Joshua Aurand, Zachary I. Bell, Taleri Hammack, Dorothy Ayres, Rizza Lim
Date: 2025-01-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

The Safe Trusted Autonomy for Responsible Space (STARS) program aims to advance autonomy technologies for space by leveraging machine learning technologies while mitigating barriers to trust, such as uncertainty, opaqueness, brittleness, and inflexibility. This paper presents the achievements and lessons learned from the STARS program in integrating reinforcement learning-based multi-satellite control, run time assurance approaches, and flexible human-autonomy teaming interfaces, into a new integrated testing environment for collaborative autonomous satellite systems. The primary results describe analysis of the reinforcement learning multi-satellite control and run time assurance algorithms. These algorithms are integrated into a prototype human-autonomy interface using best practices from human-autonomy trust literature, however detailed analysis of the effectiveness is left to future work. References are provided with additional detailed results of individual experiments.
Enhanced sampled-data model predictive control via nonlinear lifting
Authors: Nuthasith Gerdpratoom, Fumiya Matsuzaki, Yutaka Yamamoto, Kaoru Yamamoto
Date: 2025-01-10 00:00:00
Tasks: Model Predictive Control

This paper introduces a novel nonlinear model predictive control (NMPC) framework that incorporates a lifting technique to enhance control performance for nonlinear systems. While the lifting technique has been widely employed in linear systems to capture intersample behaviour, their application to nonlinear systems remains unexplored. We address this gap by formulating an NMPC scheme that combines fast-sample fast-hold (FSFH) approximations and numerical methods to approximate system dynamics and cost functions. The proposed approach is validated through two case studies: the Van der Pol oscillator and the inverted pendulum on a cart. Simulation results demonstrate that the lifted NMPC outperforms conventional NMPC in terms of reduced settling time and improved control accuracy. These findings underscore the potential of the lifting-based NMPC for efficient control of nonlinear systems, offering a practical solution for real-time applications.
Formally Verified Neural Lyapunov Function for Incremental Input-to-State Stability of Unknown Systems
Authors: Ahan Basu, Bhabani Shankar Dey, Pushpak Jagtap
Date: 2025-01-10 00:00:00

This work presents an approach to synthesize a Lyapunov-like function to ensure incrementally input-to-state stability ($\delta$-ISS) property for an unknown discrete-time system. To deal with challenges posed by unknown system dynamics, we parameterize the Lyapunov-like function as a neural network, which we train using the data samples collected from the unknown system along with appropriately designed loss functions. We propose a validity condition to test the obtained function and incorporate it into the training framework to ensure provable correctness at the end of the training. Finally, the usefulness of the proposed technique is proved using two case studies: a scalar non-linear dynamical system and a permanent magnet DC motor.
Non-intrusive Data-driven ADI-based Low-rank Balanced Truncation
Authors: Umair Zulfiqar
Date: 2025-01-10 00:00:00

In this short note, a non-intrusive data-driven formulation of ADI-based low-rank balanced truncation is provided. The proposed algorithm only requires transfer function samples at the mirror images of ADI shifts. If some shifts are used in both approximating the controllability Gramian and the observability Gramian, then samples of the transfer function's derivative at these shifts are also needed to enforce Hermite interpolation in the Loewner framework. It is noted that ADI-based low-rank balanced truncation can be viewed as a two-step process. The first step involves constructing an interpolant of the original model at the mirror images of the ADI shifts, which can be done non-intrusively within the Loewner framework. The second step involves reducing this interpolant using low-rank factors of Gramians associated with the interpolation data through the balanced square-root algorithm. This second step does not require any system information, making the overall process non-intrusive with the only required information being samples of the transfer function and/or its derivative at the mirror images of ADI shifts. Furthermore, it is shown that when the order of the reduced model in ADI-based low-rank balanced truncation is selected to match the numerical rank of the low-rank factors of the Gramians, it effectively reduces to standard interpolation at the mirror images of the ADI shift. An illustrative example is provided to explain the proposed approach.
Dissecting a Small Artificial Neural Network
Authors: Xiguang Yang, Krish Arora, Michael Bachmann
Date: 2025-01-03 00:00:00

We investigate the loss landscape and backpropagation dynamics of convergence for the simplest possible artificial neural network representing the logical exclusive-OR (XOR) gate. Cross-sections of the loss landscape in the nine-dimensional parameter space are found to exhibit distinct features, which help understand why backpropagation efficiently achieves convergence toward zero loss, whereas values of weights and biases keep drifting. Differences in shapes of cross-sections obtained by nonrandomized and randomized batches are discussed. In reference to statistical physics we introduce the microcanonical entropy as a unique quantity that allows to characterize the phase behavior of the network. Learning in neural networks can thus be thought of as an annealing process that experiences the analogue of phase transitions known from thermodynamic systems. It also reveals how the loss landscape simplifies as more hidden neurons are added to the network, eliminating entropic barriers caused by finite-size effects.
Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired Materials
Authors: Yingbin Chen, Milad Arzani, Xuan Mu, Sophia Jin, Shaoping Xiao
Date: 2025-01-08 00:00:00
Tasks: Active Learning

Metastructured auxetic patches, characterized by negative Poisson's ratios, offer unique mechanical properties that closely resemble the behavior of human tissues and organs. As a result, these patches have gained significant attention for their potential applications in organ repair and tissue regeneration. This study focuses on neural networks-based computational modeling of auxetic patches with a sinusoidal metastructure fabricated from silk fibroin, a bio-inspired material known for its biocompatibility and strength. The primary objective of this research is to introduce a novel, data-driven framework for patch design. To achieve this, we conducted experimental fabrication and mechanical testing to determine material properties and validate the corresponding finite element models. Finite element simulations were then employed to generate the necessary data, while greedy sampling, an active learning technique, was utilized to reduce the computational cost associated with data labeling. Two neural networks were trained to accurately predict Poisson's ratios and stresses for strains up to 15\%, respectively. Both models achieved $R^2$ scores exceeding 0.995, which indicates highly reliable predictions. Building on this, we developed a neural network-based design model capable of tailoring patch designs to achieve specific mechanical properties. This model demonstrated superior performance when compared to traditional optimization methods, such as genetic algorithms, by providing more efficient and precise design solutions. The proposed framework represents a significant advancement in the design of bio-inspired metastructures for medical applications, paving the way for future innovations in tissue engineering and regenerative medicine.
An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand
Authors: Biao Li, Qing-Kai Song, Wen-Gang Qi, Fu-Ping Gao
Date: 2025-01-08 00:00:00
Tasks: Sand

Predicting the lateral pile response is challenging due to the complexity of pile-soil interactions. Machine learning (ML) techniques have gained considerable attention for their effectiveness in non-linear analysis and prediction. This study develops an interpretable ML-based model for predicting p-y curves of monopile foundations. An XGBoost model was trained using a database compiled from existing research. The results demonstrate that the model achieves superior predictive accuracy. Shapley Additive Explanations (SHAP) was employed to enhance interpretability. The SHAP value distributions for each variable demonstrate strong alignment with established theoretical knowledge on factors affecting the lateral response of pile foundations.
Annealed mean-field epidemiological model on scale-free networks with a mitigating factor
Authors: K. M. Kim, M. O. Hase
Date: 2025-01-13 00:00:00

An annealed version of the quenched mean-field model for epidemic spread is introduced and investigated analytically and assisted by numerical calculations. The interaction between individuals follows a prescription that is used to generate a scale-free network, and we have adjusted the number of connections to produce a sparse network. Specifically, the model's behavior near the infection threshold is examined, as well as the behavior of the stationary prevalence and the probability that a connection between individuals encounters an infected one. We found that these functions display a monotonically increasing dependence on the infection rate. Subsequently, a modification that mimics the mitigation in the probability of encountering an infected individual is introduced, following an old idea rooted in the Malthus-Verhulst model. We found that this modification drastically changes the probability that a connection meets an infected individual. However, despite this change, it does not alter the monotonically increasing behavior of the stationary prevalence.
Bacterial proliferation pattern formation
Authors: John S. Chuang, Riccardo Rao, Stanislas Leibler
Date: 2025-01-16 00:00:00

Bacteria can form a great variety of spatially heterogeneous cell density patterns, ranging from simple concentric rings to dynamical spiral waves appearing in growing colonies. These pattern formation phenomena are important as they reflect how cellular processes such as metabolism operate in heterogeneous chemical environments. In the laboratory, they can be studied in simplified set-ups, where spatial gradients of oxygen and nutrients are externally imposed, and cells are immobilized in a gel matrix. An intriguing example, observed in such set-ups over 80 years ago, is the sequential formation of narrow bands of high cell density, taking place even for a clonal population. However, key aspects of the dynamics of band formation remained obscure. Using time-lapse imaging of replicate transparent columns in simplified growth media, we first quantify the precision of the positioning and timing of band formation. We also show that the appearance and position of different bands can be modulated independently. This "modularity" is suggested by the observation that different bands differ in their gene expression, and it is reproduced by a theoretical model based on the existence of internal metabolic states and the induction of a pH gradient. Finally, we can also modify the observed pattern formation by introducing genetic modifications that impair selected metabolic pathways. In our opinion, the possibility of precise measurements and controls, together with the simplicity and richness of the "proliferation pattern formation" phenomenon, can make it a model system to study the response of cellular processes to heterogeneous environments.
Applied Probability Insights into Nonlinear Epidemic Dynamics with Independent Jumps
Authors: Brahim Boukanjime, Mohamed El Fatini, Mohamed Maama
Date: 2025-01-13 00:00:00

This paper focuses on the analysis of a stochastic SAIRS-type epidemic model that explicitly incorporates the roles of asymptomatic and symptomatic infectious individuals in disease transmission dynamics. Asymptomatic carriers, often undetected due to the lack of symptoms, play a crucial role in the spread of many communicable diseases, including COVID-19. Our model also accounts for vaccination and considers the stochastic effects of environmental and population-level randomness using L\'evy processes. We begin by demonstrating the existence and uniqueness of a global positive solution to the proposed stochastic system, ensuring the model's mathematical validity. Subsequently, we derive sufficient conditions under which the disease either becomes extinct or persists over time, depending on the parameters and initial conditions. The analysis highlights the influence of random perturbations, asymptomatic transmission, and vaccination strategies on disease dynamics. Finally, we conduct comprehensive numerical simulations to validate the theoretical findings and illustrate the behavior of the model under various scenarios of randomness and parameter settings. These results provide valuable insights into the stochastic dynamics of epidemic outbreaks and inform strategies for disease management and control.
Female and Combined Male-Female Injury Risk Functions for the Anterior Pelvis Under Frontal Lap Belt Loading Conditions
Authors: Connor Hanggi, Joon Seok Kong, James Caldwell, Bronislaw Gepner, Martin Östling, Jason R. Kerrigan
Date: 2025-01-15 00:00:00

Purpose: Iliac wing fractures due to lap belt loading have been observed in laboratory settings for 50 years and recent data suggest they are also occurring in the field. Automated driving systems (ADS) and other occupant compartment advancements are expected to offer enhanced flexibility in seating orientation, which could place a greater reliance on the seatbelt to restrain occupants. Such changes may increase seatbelt loads and create new challenges in successfully restraining occupants and mitigating injury to areas such as the pelvis. Injury criteria exist for component-level male iliac wing fractures resulting from frontal lap belt loading, but not for females. Methods: This study explored female iliac wing fracture tolerance in the same loading environment as a previous study that explored the fracture tolerance of isolated male iliac wings. Male and female fracture data were combined to evaluate the effect of sex. Injury risk functions were created by fitting Weibull survival models to data that integrated censored and exact failure observations. Results: Twenty female iliac wings were tested; fourteen of them sustained fracture with known failure forces (exact), but the remaining six wings either (1) did not fracture, or (2) fractured after an event that changed the boundary conditions (right censored). The fracture tolerance of the tested specimens ranged widely (1134 - 8759 N) and averaged 4240 N (SD 2516 N). Conclusion: Female data and combined male-female data were analyzed. Age was the only covariate investigated in this study that had a statistically significant effect and improved the predictive performance of the models.
An Elementary Microscopic Model of Sympatric Speciation
Authors: Franco Bagnoli, Tommaso Matteuzzi
Date: 2025-01-14 00:00:00
Tasks: model

Using as a narrative theme the example of Darwin's finches, a microscopic agent-based model is introduces to study sympatric speciation as a result of competition for resources in the same ecological niche. Varying competition among individuals and resource distribution, the model exhibits some of the main features of evolutionary branching processing. The model can be extended to include spatial effects, different genetic loci, sexual mating and recombination, etc.
Physics-Informed Machine Learning for Microscale Drying of Plant-Based Foods: A Systematic Review of Computational Models and Experimental Insights
Authors: C. P. Batuwatta-Gamage, H. Jeong, HCP Karunasena, M. A. Karim, C. M. Rathnayaka, Y. T. Gu
Date: 2025-01-14 00:00:00
Tasks: Physics-informed machine learning

This review examines the current state of research on microscale cellular changes during the drying of plant-based food materials (PBFM), with particular emphasis on computational modelling approaches. The review addresses the critical need for advanced computational methods in microscale investigations. We systematically analyse experimental studies in PBFM drying, highlighting their contributions and limitations in capturing cellular-level phenomena, including challenges in data acquisition and measurement accuracy under varying drying conditions. The evolution of computational models for microstructural investigations is thoroughly examined, from traditional numerical methods to contemporary state-of-the-art approaches, with specific focus on their ability to handle the complex, nonlinear properties of plant cellular materials. Special attention is given to the emergence of data-driven models and their limitations in predicting microscale cellular behaviour during PBFM drying, particularly addressing challenges in dataset acquisition and model generalization. The review provides an in-depth analysis of Physics-Informed Machine Learning (PIML) frameworks, examining their theoretical foundations, current applications in related fields, and unique advantages in combining physical principles with neural network architectures. Through this comprehensive assessment, we identify critical gaps in existing methodologies, evaluate the trade-offs between different modelling approaches, and provide insights into future research directions for improving our understanding of cellular-level transformations during PBFM drying processes. The review concludes with recommendations for integrating experimental and computational approaches to advance the field of food preservation technology.