Computational Framework Research Articles

MCNN-AAPT: accurate classification and functional prediction of amino acid and peptide transporters in secondary active transporters using protein language models and multi-window deep learning

Secondary active transporters play a crucial role in cellular physiology by facilitating the movement of molecules across cell membranes. Identifying the functional classes of these transporters, particularly amino acid and peptide transporters, is essential for understanding their involvement in various physiological processes and disease pathways, including cancer. This study aims to develop a robust computational framework that integrates pre-trained protein language models and deep learning techniques to classify amino acid and peptide transporters within the secondary active transporter (SAT) family and predict their functional association with solute carrier (SLC) proteins. The study leverages a comprehensive dataset of 448 secondary active transporters, including 36 solute carrier proteins, obtained from UniProt and the Transporter Classification Database (TCDB). Three state-of-the-art protein language models, ProtTrans, ESM-1b, and ESM-2, are evaluated within a deep learning neural network architecture that employs a multi-window scanning technique to capture local and global sequence patterns. The ProtTrans-based feature set demonstrates exceptional performance, achieving a classification accuracy of 98.21% with 87.32% sensitivity and 99.76% specificity for distinguishing amino acid and peptide transporters from other SATs. Furthermore, the model maintains strong predictive ability for SLC proteins, with an overall accuracy of 88.89% and a Matthews Correlation Coefficient (MCC) of 0.7750. This study showcases the power of integrating pre-trained protein language models and deep learning techniques for the functional classification of secondary active transporters and the prediction of associated solute carrier proteins. The findings have significant implications for drug development, disease research, and the broader understanding of cellular transport mechanisms.

Predictive plasticity unveiled: XFEM modeling of cyclic failure in pressurized straight pipelines under three-point bending

This study presents an innovative numerical approach for predicting pressurized welded pipelines’ cyclic behavior and failure mechanisms under complex loading conditions. Focusing on X52 steel pipelines subjected to internal pressure and three-point bending, we investigate the interplay between structural integrity, weld joint positioning, and boundary conditions. Our methodology integrates advanced finite element analysis with a sophisticated material model capturing isotropic and kinematic hardening. The constitutive behavior is calibrated using the Voce model, accurately representing the material’s cyclic response. We employ the von Mises yield criterion to characterize the multiaxial stress state within the pipeline. A key innovation is our application of the eXtended Finite Element Method (XFEM) coupled with the calibrated hardening model, enabling high-fidelity simulation of crack initiation and propagation. Our model’s predictive capabilities are rigorously validated against experimental data, demonstrating excellent agreement across various loading scenarios. Through comprehensive parametric studies, we elucidate the critical influences of internal pressure, end-fixation conditions, and weld joint locations on the pipeline’s structural response. Force-displacement hysteresis curves reveal complex, nonlinear behaviors significantly impacting fatigue life and failure modes. This research advances the understanding of cyclic plasticity and fatigue in welded pressurized pipelines, providing a robust computational framework for predicting long-term performance. The insights gained have far-reaching implications for enhancing critical energy-transportation infrastructure’s safety, reliability, and longevity, potentially informing future design criteria and regulatory guidelines.

Insights of BDAPbI4-Based Flexible Memristor for Artificial Synapses and In-Memory Computing.

Inspired by brain-like spiking computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence promises to realize artificial intelligence (AI) while reducing the energy requirements of computing platforms. In this work, we show the potential of advanced learnings of butane-1,4-diammonium based low-dimensional Dion-Jacobson hybrid perovskite (BDAPbI4) memristor devices in the realm of artificial synapses and neuromorphic computing. Memristors validate Hebbian learning rules with various spike-dependent plasticity within a 10 ± 2 ms time frame, reminiscent of the human brains under flat and bending conditions (∼5 mm radium). A high recognition accuracy of ∼94% of handwritten images from the MNIST database via an artificial neural network (ANN) is achieved with only 50 epochs. An efficient demonstration of second-order memristors and the Pavlovian dog experiment exhibit significant promise in expediting learning and memory consolidation. To showcase the in-memory computing potential, a flexible 4 × 4 crossbar array is designed with measured data retention up to ∼103 s along with 26 multilevel resistance states. The crossbar array is successfully programmed for the facile configurability of image "Z". In conclusion, the integration of supervised, unsupervised, and associative learning holds great promise across a spectrum of future technologies, ranging from the realm of spiking neural networks to neuromorphic computing, brain-machine interfaces, and adaptive control systems.

A Robust-Based Home Energy Management Model for Optimal Participation of Prosumers in Competitive P2P Platforms

Nowadays, advanced metering and communication infrastructures make it possible to enable decentralized control and market schemes. In this context, prosumers can interact with their neighbors in an active manner, thus sharing resources. This practice, known as peer-to-peer (P2P), can be put into practice under cooperative or competitive premises. This paper focuses on the second case, where the peers partaking in the P2P platform compete among themselves to improve their monetary balances. In such contexts, the domestic assets, such as on-site generators and storage systems, should be optimally scheduled to maximize participation in the P2P platform and thus enable the possibility of obtaining monetary incomes or exploiting surplus renewable energy from adjacent prosumers. This paper addresses this issue by developing a home energy management model for optimal participation of prosumers in competitive P2P platforms. The new proposal is cast in a three-stage procedure, in which the first and last stages are focused on domestic asset scheduling, while the second step decides the optimal offering/bidding strategy for the concerned prosumer. Moreover, uncertainties are introduced using interval notation and equivalent scenarios, resulting in an amicable computational framework that can be efficiently solved by average machines and off-the-shelf solvers. The new methodology is tested on a benchmark four-prosumer community. Results prove that the proposed procedure effectively maximizes the participation of prosumers in the P2P platform, thus increasing their monetary benefits. The role of storage systems is also discussed, in particular their capability of increasing exportable energy. Finally, the influence of uncertainties on the final results is illustrated.

Self-Rectifying Memristor-Based Reservoir Computing for Real-Time Intrusion Detection in Cybersecurity.

The increasing sophistication of cybersecurity threats, driven by the proliferation of big data and the Internet of Things (IoT), necessitates the development of advanced real-time intrusion detection systems (IDSs). In this study, we present a novel approach that integrates NiO-doped WO3-x/ZnO bilayer self-rectifying memristors (SRMs) within a reservoir computing (RC) framework for IDS applications. The proposed crossbar array architecture exploits the exceptional dynamic properties of SRMs, achieving a classification accuracy of 93.07% on the CSE-CIC-IDS2018 data set, while demonstrating ultrahigh information-processing efficiency. Our approach not only leverages the tunable characteristics of memristors but also addresses the challenge of sneak path currents in large-scale integration, offering a robust and scalable solution for next-generation IDS. This work exemplifies the power of emerging electronics in enhancing cybersecurity through innovative hardware implementations.

Evaluating the Efficacy of LLMs to Emulate Realistic Human Personalities

To enhance immersion and engagement in video games, the design of Affective Non-Player Characters (NPCs) is a key focus for researchers and practitioners. Affective Computing frameworks improve Non-player characters (NPC) by providing personalities, emotions, and social relations. Large Language Models (LLMs) bring the promise to dynamically enhance character design when coupled with these frameworks, but further research is needed to validate the models truly represent human qualities. In this research, a comprehensive analysis investigates the capabilities of LLMs to generate content that aligns with human personality, using the Big Five and human responses from the International Personality Item Pool (IPIP) questionnaire. Our goal is to benchmark the performance of various LLMs, including frontier models and local models, against an extensive dataset comprising over 50,000 human surveys of self-reported personality tests to determine whether LLMs can replicate human-like decision-making with personality-driven prompts. A range of personality profiles were used to cluster the test results from the human survey dataset. Our methodology involved prompting LLMs with self-evaluated test items for each personality profile, comparing their outputs to human baseline responses, and evaluating the accuracy and consistency. Our findings show that some local models had 0% alignment of any personality profiles when compared to the human dataset, while the frontier models, in some cases, had 100% alignment. The results indicate that NPCs can successfully emulate human-like personality traits using LLMs, as demonstrated by benchmarking the LLM's output against human data. This foundational work serves as a methodology for game developers and researchers to test and evaluate LLMs, ensuring they accurately represent the desired human personalities and can be expanded for further validation.

Quantitative Approach to Explore Regulatory T Cell Activity in Immuno-Oncology.

The failure of immunotherapies in cancer patients is being widely studied due to the complexities present in the tumor microenvironment (TME), where regulatory T cells (Treg) appear to actively participate in providing an immune escape mechanism for tumors. Therefore, therapies to specifically inhibit tumor-infiltrating Treg represent a challenge, because Treg are distributed throughout the body and provide physiological immune homeostasis to prevent autoimmune diseases. Characterization of immunological and functional profiles could help to identify the mechanisms that need to be inhibited or activated to ensure Treg modulation in the tumor. To address this, quantitative in silico approaches based on mechanistic mathematical models integrating multi-scale information from immune and tumor cells and the effect of different therapies have allowed the building of computational frameworks to simulate different hypotheses, some of which have subsequently been experimentally validated. Therefore, this review presents a list of diverse computational mathematical models that examine the role of Treg as a crucial immune resistance mechanism contributing to the failure of immunotherapy. In addition, this review highlights the relevance of certain molecules expressed in Treg that are associated with the TME immunosuppression, which could be incorporated into the mathematical model for a better understanding of the contribution of Treg modulation. Finally, different preclinical and clinical combinations of molecules are also included to show the trend of new therapies targeting Treg.

From Molecules to Devices: A Multiscale Approach to Evaluating Organic Photovoltaics.

Due to their efficient molecular design, nonfullerene acceptors (NFAs) have significantly advanced organic photovoltaics (OPVs). However, the lack of models to screen and evaluate candidate NFAs based on the resulting device performance has impeded the rapid development of high-performance molecules. This work introduces a computational framework utilizing a kinetic Monte Carlo (kMC) model to derive device parameters from molecular properties computed through first principles. By analyzing the quantum chemical properties of diverse dimeric conformers, we estimate the relative probabilities of microscopic processes such as charge separation, recombination, and transport along with charge transfer state formation in the active layer of OPVs. These probabilities set up a random walk of charge carriers in a grid with disordered molecular sites, allowing us to track their average behavior and calculate key device parameters. Our model consistently predicts measured device parameters, including the short-circuit current and open-circuit voltage, for OPVs with diverse NFAs with high accuracy. Additionally, we applied the model to evaluate donor-acceptor combinations of known compounds and newly designed NFA molecules, identifying high-performing pairs. This model offers a computationally efficient approach for designing novel NFA molecules and optimizing the OPV performance.

Simple Recurrent Networks are Interactive.

There is disagreement among cognitive scientists as to whether a key computational framework - the Simple Recurrent Network (SRN; Elman, Machine Learning, 7(2), 195-225, 1991; Elman, Cognitive Science, 14(2), 179-211, 1990) - is a feedforward system. SRNs have been essential tools in advancing theories of learning, development, and processing in cognitive science for more than three decades. If SRNs were feedforward systems, there would be pervasive theoretical implications: Anything an SRN can do would therefore be explainable without interaction (feedback). However, despite claims that SRNs (and by extension recurrent neural networks more generally) are feedforward (Norris, 1993), this is not the case. Feedforward networks by definition are acyclic graphs - they contain no loops. SRNs contain loops - from hidden units back to hidden units with a time delay - and are therefore cyclic graphs. As we demonstrate, they are interactive in the sense normally implied for networks with feedback connections between layers: In an SRN, bottom-up inputs are inextricably mixed with previous model-internal computations. Inputs are transmitted to hidden units by multiplying them by input-to-hidden weights. However, hidden units simultaneously receive their own previous activations as input via hidden-to-hidden connections with a one-step time delay (typically via context units). These are added to the input-to-hidden values, and the sums are transformed by an activation function. Thus, bottom-up inputs are mixed with the products of potentially many preceding transformations of inputs and model-internal states. We discuss theoretical implications through a key example from psycholinguistics where the status of SRNs as feedforward or interactive has crucial ramifications.

Dynamic metabolic modeling of ATP allocation during viral infection.

Viral pathogens, like SARS-CoV-2, hijack the host's macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the host's survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic Flux Balance Analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modeling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modeling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

On exploiting the architecture of annual ring growth for developing a new class of bio-inspired composites

Inspired by several biological structures available in nature, bio-inspired composite structures are evidenced to exhibit a noteworthy enhancement in various mechanical and multi-physical performances as compared to conventional structures. This article proposes to exploit the architecture of annual ring growth of the stems of trees for developing a new class of bio-inspired composites with enhanced static and dynamic performances, including deflections, stresses, strain energy, and vibration. Concentric circular annual-ring geometries are considered where each layer of concentric circular fibers is analogous to the growth per annum of trees. The annual rings are modeled in a finite element-based computational framework by idealizing each layer as a composite of graphite fibers and epoxy matrix under different boundary conditions. The ratio of deflection to weight and frequency to weight of bio-inspired and traditional composites are compared by considering different parameters such as the number of annual rings, layers, and supporting stiffeners. The numerical results reveal that the proposed bio-inspired composites can enhance and modulate the static and dynamic properties to a significant extent, opening new design pathways for developing high-performance fiber network composites.

Data-driven homogenisation of viscoelastic porous elastomers: Feedforward versus knowledge-based neural networks

A computational framework is established to implement time-dependent data-driven surrogate constitutive models for the homogenised mechanical response of porous elastomers at large strains. The aim is to enhance the computational efficiency of multiscale analyses through the use of these surrogate models. To achieve this, explicit finite element (FE) simulations are conducted to predict the homogenised response of a cubic unit cell of a porous elastomer, using two different viscoelastic descriptions of the parent material, subject to pseudo-random, multiaxial, non-proportional histories of macroscopic strains. The histories of homogenised variables extracted from each set of FE predictions form a training dataset, which is used to train two different surrogate models, both relying on artificial neural networks (NNs). The first model predicts the increment in macroscopic stress over a simulation step, as a function of the macroscopic stress and strain at the beginning of the step, of the prescribed macroscopic strain increment, and of the corresponding time increment. The second model uses the same inputs and outputs but tests a knowledge-based modelling approach: it relies on the aid of an additional nonlinear elastic constitutive model, which is time- and path-independent and known a priori. This model represents an existing base of knowledge which is augmented and corrected by a NN after training on viscoelastic data. The data-driven surrogate model, therefore, learns the viscoelastic behaviour of the unit cell starting from knowledge of its elastic response. The two surrogate models are found to have comparable and very high accuracies, capturing the response of the homogenised unit cell to complex loading histories. Hyperparameter optimisation shows that the second, knowledge-based model requires a simpler NN and therefore incurs a smaller computational cost.

FDA and EMA Oversight of Disruptive Science on Application of Finite Absorption Time (F.A.T.) Concept in Oral Drug Absorption: Time for Scientific and Regulatory Changes.

Background: It has been demonstrated that the concept of infinite absorption time, associated with the absorption rate constant, which drives a drug's gastrointestinal absorption rate, is not physiologically sound. The recent analysis of oral drug absorption data based on the finite absorption time (F.A.T.) concept and the relevant physiologically based finite-time pharmacokinetic (PBFTPK) models developed provided a better physiologically sound description of oral drug absorption. Methods: In this study, we re-analyzed, using PBFTPK models, seven data sets of ketoprofen, amplodipine, theophylline (three formulations), and two formulations (reference, test) from a levonorgestrel bioequivalence study. Equations for one-compartment-model drugs, for the estimation of fraction of dose absorbed or the bioavailable fraction exclusively from oral data, were developed. Results: In all cases, meaningful estimates for (i) the number of absorption stages, namely, one for ketoprofen and the levonorgestrel formulations, two for amlodipine, the immediate-release theophylline formulation, and the extended-release Theotrim formulation, and three for the extended-release Theodur formulation, (ii) the duration of each absorption stage and the corresponding drug input rate, and (iii) the total duration of drug absorption, which ranged from 0.75 h (ketoprofen) to 11.6 h for Theodur were derived. Estimates for the bioavailable fraction of ketoprofen and two theophylline formulations exhibiting one-compartment-model kinetics were derived. Conclusions: This study provides insights into the detailed characteristics of oral drug absorption. The use of PBFTPK models in drug absorption analysis can be leveraged as a computational framework to discontinue the perpetuation of the mathematical fallacy of classical pharmacokinetic analysis based on the absorption rate constant as well as in the physiologically based pharmacokinetic (PBPK) studies and pharmacometrics. The present study is an additional piece of evidence for the scientific and regulatory changes required to be implemented by the regulatory agencies in the not-too-distant future.

Diagnostics and Prognostics with High Dimensional Spatial-Temporal Data: From Structures to Human Brains

Diagnostics and prognostics with high-dimensional spatial-temporal data require innovative methodologies due to the inherent complexity of such datasets. This thesis explores the challenges of diagnostics and prognostics in high-dimensional spatial-temporal data, extending from physical structures to complex human brain analyses through resting-state functional magnetic resonance imaging (rs-fMRI). Drawing an analogy to engineering structural health monitoring using spatial-temporal vibration data, the approach leverages techniques from engineering diagnostics and prognostics data analytics to handle clinical problems with similar characteristics. A pioneering approach is developed to analyze multimodal datasets that not only include advanced rs-fMRI features—Amplitude of Low-frequency Fluctuations (ALFF), Regional Homogeneity (ReHo), Euler Characteristics (EC), and Fractal Analysis—but also encompass a wide array of clinical data. This integration includes infant developmental metrics such as birth weight and gestational age, maternal health factors like BMI and fat mass, and environmental influences including dietary intake and mental health during pregnancy. The study establishes a robust computational framework that uses advanced machine learning algorithms to analyze the interplay of these diverse data types, enhancing the precision and predictive power of our models for early childhood development. Initial validations have demonstrated the effectiveness of this comprehensive approach in identifying ADHD, with ongoing efforts aimed at expanding the methodology to address a broader range of developmental disorders. This work not only advances the diagnostic and prognostic capabilities in medical imaging but also significantly contributes to the field of Prognostics and Health Management (PHM). By providing a solid foundation for managing and understanding high-dimensional and multimodal spatial-temporal data across various disciplines, it bridges the gap between engineering and clinical diagnostics, demonstrating the potential for cross-disciplinary innovation.

DDDR-33. TRANSCRIPTOMICS-BASED NOVEL DRUG DISCOVERY IN DIFFUSE MIDLINE GLIOMA

Abstract Introduction. Pediatric high-grade gliomas are the most common cause of cancer-related death in children, of which the most malignant and devastating tumors include diffuse midline glioma (DMG). Recent availability of tumor samples and advances in next-generation sequencing enable the profiling of thousands of molecular features in DMG. We employ a system-based approach that uses gene expression as a representation of the molecular state, providing an avenue for therapeutic discovery. Previously, we used this approach to identify repurposed candidates for DMG; however, the final candidate had poor brain penetration, prompting us to discover novel compounds with better brain penetration. To apply it to novel compound discovery, we need the expression profiles of all library compounds, which is impractical for millions of compounds. To overcome this, we have developed a platform, Gene expression profiles Predictor on chemical Structures (GPS), that incorporates machine learning algorithms which leverage existing drug-induced gene expression profiles to predict gene expression based on compound structure. methods. DMG RNA-Seq samples were acquired from St. Jude Children’s Research Hospital and Children’s Brain Tumor Network (CBTN). We developed deep learning autoencoder to search reference normal tissues from Genotype-Tissue Expression (GTEx). A DMG meta-signature was created and fed into GPS to predict drug candidates from the Enamine CNS library, seven of which were experimentally tested in vitro and the most promising one subsequently validated in vivo. Results. Cell proliferation analyzed by MTS assay showed three compounds with IC50< 50 µM in K27M-mutant DMG cell lines (SF8628, DIPG007, SU-DIPG36). Toxicity study showed no adverse effects in the mice treated with 100-200 mg/kg of the most promising compound (IC50 = 1.9 µM in DMG cells). Conclusion. Our novel computational framework leverages deep learning to fill the gap in drug discovery in DMG. Work is underway to determine antitumor activity, brain penetration (via HPLC) and survival benefit in the mice bearing DMG PDX treated with the lead compound.

Uncovering functional lncRNAs by scRNA-seq with ELATUS

Long non-coding RNAs (lncRNAs) play fundamental roles in cellular processes and pathologies, regulating gene expression at multiple levels. Despite being highly cell type-specific, their study at single-cell (sc) level is challenging due to their less accurate annotation and low expression compared to protein-coding genes. Here, we systematically benchmark different preprocessing methods and develop a computational framework, named ELATUS, based on the combination of the pseudoaligner Kallisto with selective functional filtering. ELATUS enhances the detection of functional lncRNAs from scRNA-seq data, detecting their expression with higher concordance than standard methods with the ATAC-seq profiles in single-cell multiome data. Interestingly, the better results of ELATUS are due to its advanced performance with an inaccurate reference annotation such as that of lncRNAs. We independently confirm the expression patterns of cell type-specific lncRNAs exclusively detected with ELATUS and unveil biologically important lncRNAs, such as AL121895.1, a previously undocumented cis-repressor lncRNA, whose role in breast cancer progression is unnoticed by traditional methodologies. Our results emphasize the necessity for an alternative scRNA-seq workflow tailored to lncRNAs that sheds light on the multifaceted roles of lncRNAs.

Exploring tissue permeability of brain tumours in different grades: Insights from pore-scale fluid dynamics analysis

Interstitial fluid (ISF) flow is identified as an essential physiological process that plays an important role in the development and progression of brain tumours. However, the relationship between the permeability of the tumour tissue, a complex porous medium, and the interstitial fluid flow around the tumour cells at the microscale is not well understood. To shed light on this issue, and in the absence of experimental techniques that can provide direct measurements, we develop a computational model to predict the tissue permeability of brain tumours in different grades by analysing the ISF flow at the pore scale. The 3-D geometrical models of tissue extracellular spaces are digitally reconstructed for each grade tumour based on their morphological properties measured from microscopic images. The predictive accuracy of the framework is validated by experimental results reported in the literature. Our results indicate that high-grade brain tumours are less permeable despite their higher porosity, whereas necrotic areas of glioblastoma are more permeable than the viable tumour areas. This implies that tissue permeability is primarily governed by both tissue porosity and the deposition of hyaluronic acid (HA), a key component of the extracellular matrix, while the HA deposition can have a greater effect than macro-level porosity. Parametric studies show that tissue permeability falls exponentially with increasing HA concentration in all grades of brain tumours, and this can be captured using an empirically derived relationship in a quantitative manner. These findings provide an improved understanding of the hydraulic properties of brain tumours and their intrinsic links to tumour microstructure. This work can be used to reveal the intratumoural physiochemical processes that rely on fluid flow and offer a powerful tool to tune textured and porous biomaterials for desired transport properties. Statement of SignificanceInterstitial fluid flow in the extracellular space of brain tumours plays a crucial role in their progression, development, and response to drug treatments. However, the mechanisms of interstitial fluid transport around tumour cells and the characterization of these microscale transports at the tissue scale to meet clinical requirements are largely unknown. In the absence of advanced experimental techniques to capture these pore-scale transport phenomena, we have developed and validated a computational framework to successfully reveal these phenomena across all grades of brain tumours. For the first time, we have quantitatively determined the tissue permeability of all grades of brain tumours as a function of the concentration of hyaluronic acid, a key component of the extracellular matrix. This framework will enhance our ability to capture the intratumoural physicochemical processes in brain tumours and correlate them with tumour tissue-scale behaviours.

Development of Software for Hydrometallurgical Calculation of Metal Extraction

Hydrometallurgy plays a critical role in the metallurgical industry by providing an efficient method for extracting metals from ores and secondary materials using aqueous solutions. This approach is particularly advantageous for processing low-grade and complex ores, as well as secondary resources that cannot be effectively processed by traditional pyrometallurgical methods. The objective of this study is to develop specialized software to automate and optimize the calculations necessary for metal extraction in hydrometallurgical processes. The software integrates a complete computational framework for automating the various stages of the hydrometallurgical process, including ore composition initialization, total element mass calculations, and the determination of metal concentrations in both metal products (matte) and by-products (slag). The methodology involves the design and implementation of a web-based application using Django for the user interface and MySQL for robust data storage. The computational module, written in Python, automates the mathematical operations required to simulate complex chemical reactions and metal extraction processes. This module supports real-time processing and ensures accurate calculations for each stage of metal extraction, including leaching, extraction, re-extraction, sorption, and electrolysis. The software also features detailed tables and dynamic graphs that allow users to analyze the distribution of valuable metals and assess the influence of key operational parameters on extraction efficiency. The results show that the developed software successfully manages large datasets, enhances the precision of hydrometallurgical calculations, and minimizes the risk of human error. The implementation of the software leads to significant improvements in the economic efficiency of production by optimizing metal recovery rates and reducing operational costs. Additionally, it supports comprehensive process control by providing actionable insights into each stage of extraction, thus improving the quality of the final metal product. Overall, the software represents a significant technological advancement in the field, offering a scalable solution for industries aiming to streamline hydrometallurgical processes.

Personalizing computational models to construct medical digital twins.

To realize the promise of personalized medicine, we need to be able to integrate different types of data collected from a given patient into a computational framework that enables decision making about optimal interventions to help this patient to either maintain or regain health. Digital twins represent such a framework, once the technology is sufficiently developed. A fundamental problem that currently does not have a widely applicable solution is how to calibrate a generic computational model of human biology to a given patient at a given time. This paper presents a solution to this problem for the agent-based model framework, commonly used to capture stochastic and spatially heterogeneous biological processes, such as tumor growth or immune system dynamics.

Convolution tensor decomposition for efficient high-resolution solutions to the Allen–Cahn equation

This paper presents a convolution tensor decomposition based model reduction method for solving the Allen–Cahn equation. The Allen–Cahn equation is usually used to characterize phase separation or the motion of anti-phase boundaries in materials. Its solution is time-consuming when high-resolution meshes and large time scale integration are involved. To resolve these issues, the convolution tensor decomposition method is developed, in conjunction with a stabilized semi-implicit scheme for time integration. The development enables a powerful computational framework for high-resolution solutions of Allen–Cahn problems, and allows the use of relatively large time increments for time integration without violating the discrete energy law. To further improve the efficiency and robustness of the method, an adaptive algorithm is also proposed. Numerical examples have confirmed the efficiency of the method in both 2D and 3D problems. Orders-of-magnitude speedups were obtained with the method for high-resolution problems, compared to the finite element method. The proposed computational framework opens numerous opportunities for simulating complex microstructure formation in materials on large-volume high-resolution meshes at a deeply reduced computational cost.