Extensive Simulations Research Articles

Fast and scalable ensemble learning method for versatile polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning method for computing PRS using summary statistics from genome-wide association studies (GWAS). ALL-Sum leverages a L0L2 penalized regression and ensemble learning across tuning parameters to flexibly model traits with diverse genetic architectures. In extensive large-scale simulations across a wide range of polygenicity and GWAS sample sizes, ALL-Sum consistently outperformed popular alternative methods in terms of prediction accuracy, runtime, and memory usage by 10%, 20-fold, and threefold, respectively, and demonstrated robustness to diverse genetic architectures. We validated the performance of ALL-Sum in real data analysis of 11 complex traits using GWAS summary statistics from nine data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen Biobank, with validation in the UK Biobank. Our results show that on average, ALL-Sum obtained PRS with 25% higher accuracy on average, with 15 times faster computation and half the memory than the current state-of-the-art methods, and had robust performance across a wide range of traits and diseases. Furthermore, our method demonstrates stable prediction when using linkage disequilibrium computed from different data sources. ALL-Sum is available as a user-friendly R software package with publicly available reference data for streamlined analysis.

A learning-based nearly optimal control framework for trajectory tracking of a flexible-link manipulator system with actuator fault

In this paper, a learning-based nearly optimal control framework with fault-tolerant capability is designed to tackle the tracking control problem of a flexible-link manipulator in the presence of actuator fault and model uncertainties. Initially, the optimal control law is obtained by adopting the dynamic programming and a critic structure as the solution of Hamilton–Jacobi–Bellman equation for the nominal model. Then, by implementing an integral sliding mode control, the robustness against actuator fault and model uncertainty is guaranteed. The adaptive laws are constructed based on radial basis functions neural networks to estimate the upper bound of uncertainty and the actuator bias fault, satisfying both optimal performance and chattering reduction of the sliding surface. Furthermore, the actuator effectiveness loss is handled. The stability of the closed-loop system is analytically proven, and the performance of the proposed framework is investigated against several practical operating conditions. This incorporates the fidelity assessment of tracking precision and trackability of control signal using performance indices such as the integral absolute error and root-mean-square error. The results of extensive simulation studies confirm the effectiveness and robustness of the proposed control framework.

Scalable Estimation for Structured Additive Distributional Regression

Obtaining probabilistic models is of high relevance in many recent applications. However, estimation of such distributional models with very large datasets remains a difficult task. In particular, the use of rather complex models can easily lead to memory-related efficiency problems and thereby make estimation infeasible even on high-performance computers. We address these challenges and propose a novel backfitting algorithm, which is based on the ideas of stochastic gradient descent and can deal virtually with any amount of data on a conventional laptop. The algorithm performs automatic selection of variables and determination of smoothing parameters. Its performance is superior or at least equivalent to other implementations for structured additive distributional regression, such as, gradient boosting, while maintaining lower computation time. Performance is evaluated using an extensive simulation study and an exceptionally challenging example of lightning count prediction across Austria with over 9 million observations and 80 covariates.

Optimization of node deployment in underwater internet of things using novel adaptive long short‐term memory‐based egret swarm optimization algorithm

SummaryOptimizing node deployment in the underwater Internet of Things (UIoT) poses significant challenges due to the complex and dynamic nature of underwater environments. This research introduces the adaptive long short‐term memory‐based egret swarm optimization algorithm (ALSTM‐ESOA), a novel approach designed to enhance network coverage and performance efficiently. Unlike traditional methods, ALSTM‐ESOA incorporates cognitive learning capabilities from long short‐term memory (LSTM) and dynamic adaptation strategies inspired by the hunting behaviors of egrets. The algorithm's effectiveness was tested through extensive simulations in MATLAB, demonstrating notable improvements over existing models: network throughput increased by up to 55.56%, deployment time decreased by 88.89%, and energy efficiency improved significantly. These enhancements are critical for robust, real‐time data collection and monitoring in underwater settings, providing substantial benefits for marine research and resource management. The findings suggest that ALSTM‐ESOA significantly outperforms conventional algorithms, offering a promising new tool for the advancement of UIoT applications. After being implemented in MATLAB, the suggested ALSTM‐ESOA model for the node deployment optimization in UIoT is examined. The proposed ALSTM‐ESOA in terms of network throughput is 55.56%, 38.89%, 36.11%, and 11.11% better than CNN, LSTM, ARO‐RTP, and IGOR‐TSA, respectively. Similarly, the proposed ALSTM‐ESOA with respect to deployment time is 88.89%, 81.82%, 75%, and 50% better than CNN, LSTM, ARO‐RTP, and IGOR‐TSA, respectively. For the purpose of exploring marine resources, monitoring underwater environments, and conducting marine scientific investigation, the research's findings are extremely valuable.

Durotaxis and Antidurotaxis Droplet Motion onto Gradient Gel-Substrates.

The self-sustained motion of fluids on gradient substrates is a spectacular phenomenon, which can be employed and controlled in applications by carefully engineering the substrate properties. Here, we report on a design of a gel substrate with stiffness gradient, which can cause the spontaneous motion of a droplet along (durotaxis) or to the opposite (antidurotaxis) direction of the gradient, depending on the droplet affinity to the substrate. By using extensive molecular dynamics simulations of a coarse-grained model, we find that the mechanisms of the durotaxis and antidurotaxis droplet motion are distinct, require the minimization of the interfacial energy between the droplet and the substrate, and share similarities with those mechanisms previously observed for brush substrates with stiffness gradient. Moreover, durotaxis motion takes place over a wider range of affinities and is generally more efficient (faster motion) than antidurotaxis. Thus, our study points to further possibilities and guidelines for realizing both antidurotaxis and durotaxis motion on the same gradient substrate for applications in microfluidics, energy conservation, and biology.

FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool

Objective. Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently. Approach. FAST-PET simulates PET images by performing precise forward projection, scatter, and random estimation that match the scanner geometry and statistics. Although the same process should be applicable to other scanner models, we focus on the Siemens Biograph Vision-600 in this work. Calibration and validation of FAST-PET were performed through comparison with an experimental scan of a National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Further validation was conducted between FAST-PET and Geant4 Application for Tomographic Emission (GATE) quantitatively in clinical image simulations in terms of intensity-based and texture-based features and task-based tumor segmentation. Main results. According to the NEMA IQ phantom simulation, FAST-PET’s simulated images exhibited partial volume effects and noise levels comparable to experimental images, with a relative bias of the recovery coefficient RC within 10% for all spheres and a coefficient of variation for the background region within 6% across various acquisition times. FAST-PET generated clinical PET images exhibit high quantitative accuracy and texture comparable to GATE (correlation coefficients of all features over 0.95) but with ∼100-fold lower computation time. The tumor segmentation masks comparison between both methods exhibited significant overlap and shape similarity with high concordance CCC > 0.97 across measures. Significance. FAST-PET generated PET images with high quantitative accuracy comparable to GATE, making it ideal for applications requiring extensive PET image simulations such as virtual imaging trials, and the development and validation of image processing pipelines.

Model Mechanism for Lipid Uptake by the Human STARD2/PC-TP Phosphatidylcholine Transfer Protein.

The human StAR-related lipid transfer domain protein 2 (STARD2), also known as phosphatidylcholine (PC) transfer protein, is a single-domain lipid transfer protein thought to transfer PC lipids between intracellular membranes. We performed extensive μs-long molecular dynamics simulations of STARD2 of its apo and holo forms in the presence or absence of complex lipid bilayers. The simulations in water reveal ligand-dependent conformational changes. In the 2 μs-long simulations of apo STARD2 in the presence of a lipid bilayer, we observed spontaneous reproducible PC lipid uptake into the protein hydrophobic cavity. We propose that the lipid extraction mechanism involves one to two metastable states stabilized by choline-tyrosine or choline-tryptophane cation-π interactions. Using free energy perturbation, we evaluate that PC-tyrosine cation-π interactions contribute 1.8 and 2.5 kcal/mol to the affinity of a PC-STARD2 metastable state, thus potentially providing a significant decrease of the energy barrier required for lipid desorption.

SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning

As a crucial nonlinear phenomenon, stimulated Raman scattering (SRS) plays multifaceted roles involved in forward and inverse problems. In fibre-optic systems, these roles range from detrimental interference that impairs optical performance to beneficial effects that enables various devices such as Raman amplifier. To obtain solutions of SRS, various numerical methods customized for different scenarios have been proposed. However, these methods are time-consuming, low-efficiency, and experience-orientated, particularly in combined scenarios consisting of both forward and inverse problems. Inspired by physics-informed neural networks, we propose SRS-Net, which combines the efficient automatic differentiation and powerful representation ability of neural networks with the regularization of SRS physical laws, to obtain universal solutions for SRS of forward, inverse, and combined problems. We showcase the intuitive solving procedure and high-speed performance of SRS-Net through extensive simulations covering different scenarios. Additionally, we validate its capabilities in experiments involving the high-fidelity modelling of a wavelength division multiplexing system spanning the C + L-band with approximately 10 THz. The versatility of the SRS-Net framework extends beyond SRS, indicating its potential as a promising universal solution in other engineering problems with nonlinear dynamics governed by partial differential equations.

Response of Foldable Protein Conformations to Non-physiological Perturbations: Interplay of Thermal Factors and Confinement.

Technological advances frequently interface biomolecules with nanomaterials at non-physiological conditions, necessitating response characterization of key processes. Similar encounters are expected in cellular contexts. We report in silico investigations of the response of diverse protein conformational states to lowering of temperature and imposition of spatial constraints. Conformational states are represented by folded form of the Albumin binding domain (ABD) protein, its compact denatured form, and structurally disordered nascent folding elements. Data from extensive simulations are evaluated to elicit structural, thermodynamic and dynamic responses of the states and their associated environment. Analyses reveal alterations to folding propensity with reduced thermal energy and confinement, with signatures of trend reversal in highly disordered states. Across temperatures, confinement has restrictive effects on volume and energetic fluctuations, leading to narrowing of differences in isothermal compressibility (κ) and heat capacities (Cp). While excess (over ideal gas) entropy of the hydration layer marks dependence on the conformational state at bulk, confinement triggers erasure of differences. These observations are largely consistent with timescales of protein-water hydrogen bonding dynamics. The results implicate multi-factorial associations within a simple bio-nano complex. We expect the current study to motivate investigations of more biologically relevant interfaces towards mechanistic understanding and potential applications.

Degrees of Freedom: Search Cost and Self-consistency

Model degrees of freedom ( df ) is a fundamental concept in statistics because it quantifies the flexibility of a fitting procedure and is indispensable in model selection. To investigate the gap between df and the number of independent variables in the fitting procedure, Tibshirani (2015) introduced the search degrees of freedom ( sdf ) concept to account for the search cost during model selection. However, this definition has two limitations: it does not consider fitting procedures in augmented spaces and does not use the same fitting procedure for sdf and df . We propose a modified search degrees of freedom ( msdf ) to directly account for the cost of searching in either original or augmented spaces. We check this definition for various fitting procedures, including classical linear regressions, spline methods, adaptive regressions (the best subset and the lasso), regression trees, and multivariate adaptive regression splines (MARS). In many scenarios when sdf is applicable, msdf reduces to sdf . However, for certain procedures like the lasso, msdf offers a fresh perspective on search costs. For some complex procedures like MARS, the df has been pre-determined during model fitting, but the df of the final fitted procedure might differ from the pre-determined one. To investigate this discrepancy, we introduce the concepts of nominal df and actual df , and define the property of self-consistency, which occurs when there is no gap between these two df ’s. We propose a correction procedure for MARS to align these two df ’s, demonstrating improved fitting performance through extensive simulations and two real data applications.

SIBS: A sparse encoder utilizing self-inspired bases for efficient image representation

Addressing the limitations of pre-defined dictionaries in image processing, this study introduces Self-Inspired Bases-based Sparse Encoder (SIBS), a novel approach that dynamically generates image-specific atoms by leveraging existing dictionaries and input data. Extensive numerical analysis and simulations demonstrate that SIBS enhances sparse coding performance in three key areas: improved quality resolution via precise image reconstruction, accelerated convergence speed, and competitive compression performance rivaling techniques like JPEG and JP2000. Experiments show that SIBS enhances quality levels and NMSE at the same number of non-zero coefficients and outperforms atoms learned directly from the image at low error levels. SIBS also effectively improves the performance of various learned and structured dictionaries by serving as auxiliary bases, enhancing quality resolution, convergence rate, and dictionary efficiency. Additionally, SIBS has potential as a competitive image compression method. The study’s discussion on SIBS limitations guides future research, including examining its performance with other greedy pursuit algorithms and reevaluating the encoder with other lossless and filter techniques.

An optimal normalization method for high sparse compositional microbiome data.

In many omics data, including microbiome sequencing data, we are only able to measure relative information. Various computational or statistical methods have been proposed to extract absolute (or biologically relevant) information from this relative information; however, these methods are under rather strong assumptions that may not be suitable for multigroup (more than two groups) and/or longitudinal outcome data. In this article, we first introduce the minimal assumption required to extract absolute from relative information. This assumption is less stringent than those imposed in existing methods, thus being applicable to multigroup and/or longitudinal outcome data. We then propose the first normalization method that works under this minimal assumption. The optimality and validity of the proposed method and its beneficial effects on downstream analysis are demonstrated in extensive simulation studies, where existing methods fail to produce consistent performance under the minimal assumption. We also demonstrate its application to real microbiome datasets to determine biologically relevant microbes to a specific disease/condition.

Optimising shapes of multiple pin fins in a microchannel using deep reinforcement learning and mesh deformation techniques

The utilisation of advanced pin fin designs in microchannels is useful for enhancing cooling efficiency. Advancements in machine learning and processing power have sparked interest in shape optimisation techniques. This research employs a novel framework that integrates Deep Artificial Neural Networks and Reinforcement Learning with a Computational Fluid Dynamics (CFD) solver to optimise multiple pin fin shapes within a microchannel. By incorporating Radial Basis Function interpolation and Proximal Policy Optimisation alongside FLUENT, acting as the CFD environment, the reinforcement learning agent adeptly explores the design space to enhance the thermohydraulic performance factor (TPF), aiming to maximise Nusselt number while minimising pressure loss. Unlike previous heat transfer optimisation studies, which typically required mesh regeneration at each step, the proposed framework could bypass the meshing step and alter the geometry directly by relying on the RBF interpolation technique to deform the mesh directly. Three distinct scenarios investigated in this study are uniform deformation of all pin fins, deformation of all pin fins arranged in two rows, and individual deformation of each pin fin. Extensive simulations, exceeding 90,000 different cases, demonstrate that although the optimisation process for the individual deformation of each pin fin requires more iterations compared to others, it surpasses them in terms of TPF improvement. Notably, significant improvements are achieved, such as a 49 % enhancement in Nusselt number and a 33 % reduction in pressure drop, culminating in an impressive 63 % increase in TPF compared to the initial geometry.

A novel FVF-based GHz-range biquad in a 28 nm CMOS FD-SOI technology

Inductor-less CMOS filters with bandwidth exceeding several GHz are required in high-speed data converter applications. This paper introduces two complementary biquad filters, one N-based and the other P-based, utilizing the well-established flipped voltage follower (FVF) stage. These filters exhibit more than 7 GHz cut-off frequency and a low power consumption of 0.54 mW/pole for the N-type biquad, and 0.3 mW/pole for the P-type one, demonstrating impressive figures-of-merit (FOMs) even considering bandwidth and dynamic range. The implementation of these biquads in the STMicroelectronics FD-SOI 28-nm CMOS process, along with extensive simulations, ensures stable performance under process, supply voltage and temperature (PVT) variations and mismatches, as confirmed by post-layout simulations. Notably, the area occupied by each biquad is merely 246 μm2 for N-type biquad and 193 μm2 for P-type, marking one of the smallest footprints in the existing literature. The achieved figures-of-merit are noteworthy, showcasing excellent power efficiency, minimal area occupation, and commendable dynamic range.

Safe Coverage Control of Multi-Agent Systems and Its Verification in ROS/Gazebo Environment

This paper presents safe coverage control algorithms for multi-agent systems, integrating Centroidal Voronoi Tessellation (CVT) and control barrier functions (CBFs). This study aims to ensure safety and spatial optimization by combining CVT and CBFs for obstacle avoidance, testing the controller through simulations, and verifying the results with RT mobile robots. This development of safe coverage control algorithms for multi-agent systems achieves a synergy that addresses both safety and spatial optimization, which are crucial for multi-agent systems. The proposed CVT-CBF-based controller has been validated through extensive simulations in the ROS/Gazebo environment and physical experiments with RT robots, demonstrating its effectiveness in achieving collision-free coverage. This study provides a comprehensive understanding of the integration of CVT and CBFs for safe coverage control with obstacle avoidance in multi-agent systems, highlighting both its potential and the necessary considerations for practical deployment.

Advanced control and energy management algorithm for a multi-source microgrid incorporating renewable energy and electric vehicle integration

This paper presents a comprehensive study on advanced control and energy management in a multi-source/multi-load microgrid. The microgrid integrates solar panels, a wind turbine system, a Li-ion battery-based storage system, an electric vehicle (EV), and a DC load, all inter-connected through power electronic converters to a DC bus and linked to the AC grid via a DC/AC inverter. The study outlines the primary control objectives: DC bus voltage regulation, optimization of photovoltaic and wind energy conversion, and maintaining high-quality energy injection into the grid. Given the intermittent nature of solar and wind energy and the varying energy demands that affect battery life and performance, a novel energy management algorithm is introduced. This algorithm addresses different operating modes, such as Constant Current (CC) and Constant Voltage (CV), to enhance network stability, energy quality, and optimize energy distribution between the various sources and the EV. The proposed fuzzy logic controllers, known for their robustness to uncertainties, flexibility, and ease of implementation, are employed to achieve these objectives. The Particle Swarm Optimization (PSO) is used to fine-tune the fuzzy logic control parameters, ensuring optimal performance. This optimization process included minimizing Integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Square Error (ITSE) as objective functions. Extensive simulations under various operating scenarios demonstrated significant improvements in control accuracy, stability, and system performance.

Optimizing wind-solar hydrogen production through collaborative strategy with ALK/PEM multi-electrolyzer arrays

This paper presents an innovative sustainable hydrogen production system integrating solar and wind energy to provide power to alkaline (ALK) and proton exchange membrane (PEM) electrolyzers. On this basis, a novel source and load collaborative optimization strategy is proposed to optimize the hydrogen production system, aiming to address the challenges of the intermittent and variable nature of renewable energy sources. The proposed strategy consists of a classification prediction module using the XGBoost algorithm, which analyzes historical energy generation data to predict optimal electrolyzer operation. Furthermore, the proposed collaborative optimization strategy is able to balance the loads between the electrolyzer arrays, aiming to minimize cold-start situations, prolong steady-state hydrogen production time, and reduce operational variations due to maintenance. The proposed strategy is proved to significantly reduce the standard deviation of the electrolyzer runtime, enhancing the overall system stability, and contributing to the reduction of maintenance frequency. Theoretical analyzes and extensive simulation results validate the effectiveness of the proposed strategy in improving hydrogen production efficiency and sustainability. This work contributes to the integration of renewable energy sources for clean energy production, and provides a practical and effective solution for the urgently needed green hydrogen production.

A novel framework with automated horizontal pleiotropy adjustment in mendelian randomization

The presence of horizontal pleiotropy in Mendelian randomization (MR) analysis has long been a concern due to its potential to induce substantial bias. In recent years, many robust MR methods have been proposed to address this by relaxing the ‘no horizontal pleiotropy’ assumption. Here, we propose a novel two-stage framework called CMR, which integrates a conditional analysis of multiple genetic variants to remove pleiotropy induced by linkage disequilibrium, followed by the application of robust MR methods to model the conditional genetic effect estimates. We demonstrate how the conditional analysis can reduce horizontal pleiotropy and improve the performance of existing MR methods. Extensive simulation studies covering a wide range of scenarios of horizontal pleiotropy showcased the superior performance of the proposed CMR framework over the standard MR framework in which marginal genetic effects are modeled. Moreover, the application of CMR in a negative control outcome analysis and investigation into the causal role of body mass index across various diseases highlighted its potential to deliver more reliable results in real-world applications.

AoI-aware transmission control in real-time mmwave energy harvesting systems: a risk-sensitive reinforcement learning approach

The evolution of enabling technologies in wireless communications has paved the way for supporting novel applications with more demanding QoS requirements, but at the cost of increasing the complexity of optimizing the digital communication chain. In particular, Millimeter Wave (mmWave) communications provide an abundance of bandwidth, and energy harvesting supplies the network with a continual source of energy to facilitate self-sustainability; however, harnessing these technologies is challenging due to the stochastic dynamics of the mmWave channel as well as the random sporadic nature of the harvested energy. In this paper, we aim at the dynamic optimization of update transmissions in mmWave energy harvesting systems in terms of Age of Information (AoI). AoI has recently been introduced to quantify information freshness and is a more stringent QoS metric compared to conventional delay and throughput. However, most prior art has only addressed average-based AoI metrics, which can be insufficient to capture the occurrence of rare but high-impact freshness violation events in time-critical scenarios. We formulate a control problem that aims to minimize the long-term entropic risk measure of AoI samples by configuring the “sense & transmit” of updates. Due to the high complexity of the exponential cost function, we reformulate the problem with an approximated mean-variance risk measure as the new objective. Under unknown system statistics, we propose a two-timescale model-free risk-sensitive reinforcement learning algorithm to compute a control policy that adapts to the trio of channel, energy, and AoI states. We evaluate the efficiency of the proposed scheme through extensive simulations.

Insight into structural dynamics involved in activation mechanism of full length KRAS wild type and P-loop mutants

KRAS protein is known to be frequently mutated in various cancers. The most common mutations being at position 12, 13 and 61. The positions 12 and 13 form part of the phosphate binding region (P-loop) of KRAS. Owing to mutation, the protein remains in continuous active state and affects the normal cellular process. Understanding the structural changes owing to mutations in GDP-bound (inactive state) and GTP-bound (active state) may help in the design of better therapeutics. To understand the structural flexibility due to the mutations specifically located at P-loop regions (G12D, G12V and G13D), extensive molecular dynamics simulations (24 μs) have been carried for both inactive (GDP-bound) and active (GTP-bound) structures for the wild type and these mutants. The study revealed that the local structural changes at the site of mutations allosterically guide changes in distant regions of the protein through hydrogen bond and hydrophobic signalling network. The dynamic cross correlation analysis and the comparison of the correlated motions among different systems manifested that changes in SW-I, SW-II, α3 and the loop preceding α3 affects the interactions of GDP/GTP with different regions of the protein thereby affecting its hydrolysis. Further, the Markov state modelling analysis confirmed that the mutations, especially G13D imparts rigidity to structure compared to wild type and thus limiting its conformational state in either intermediate state or active state. The study suggests that along with SW-I and SW-II regions, the loop region preceding the α3 helix and α3 helix are also involved in affecting the hydrolysis of nucleotides and may be considered while designing therapeutics against KRAS.