Rational protein design.

The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F2 population represented by 500 individuals, obtained from a cross between contrasting homozygous parents, were simulated. Phenotypic traits were simulated based on previously established means and heritability estimates (30%, 50%, and 80%); traits were distributed in a genome with 10 linkage groups, considering two alleles per marker. Four different scenarios were considered. For the principal trait, heritability was 50%, and 40 control loci were distributed in five linkage groups. Another phenotypic control trait with the same complexity as the principal trait but without any genetic relationship with it and without pleiotropy or a factorial link between the control loci for both traits was simulated. These traits shared a large number of control loci with the principal trait, but could be distinguished by the differential action of the environment on them, as reflected in heritability estimates (30%, 50%, and 80%). The coefficient of determination were considered to evaluate the proposed methodologies. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the tested traits. Computational intelligence and machine learning were superior in extracting nonlinear information from model inputs and quantifying the relative contributions of phenotypic traits. The R2 values ranged from 44.0% - 83.0% and 79.0% - 94.0%, for computational intelligence and machine learning, respectively. In conclusion, the relative contributions of auxiliary traits in different scenarios in plant breeding programs can be efficiently predicted using computational intelligence and machine learning.

The hierarchical structure and multifunctional properties of bio-based cellular materials, particularly cellulose, hemicellulose, and lignin, have attracted increasing attention and interest due to their sustainability and versatility. Recent advances in computational modeling and machine learning strategies have provided transformative insights into the molecular, mechanical, thermal, and electronic behaviors of these biopolymers. This review categorizes the conducted studies based on key material properties and discusses the computational methods utilized, including quantum mechanical approaches, atomistic and coarse-grained molecular dynamics, finite element modeling, and machine learning techniques. For each property, such as structural, mechanical, thermal, and electronic, we have analyzed the progress made in understanding inter- and intra-molecular interactions, deformation mechanisms, phase behavior, and functional performance. For instance, atomistic simulations have shown that cellulose nanocrystals exhibit a highly anisotropic elastic response, with axial elastic moduli ranging from approximately 100 to 200 GPa. Similarly, thermal transport studies have shown that the thermal conductivity along the chain axis (≈5.7 W m−1 K−1) is nearly an order of magnitude higher than that in the transverse direction (≈0.7 W m−1 K−1). In recent years, this research area has also experienced rapid advancement in data-driven methodologies, with the number of machine learning applications for biopolymer systems increasing more than fourfold over the past five years. By bridging multiscale modeling and data-driven approaches, this review aims to illustrate how these techniques can be integrated into a unified framework to accelerate the design and discovery of high-performance bioinspired materials. Eventually, we have discussed emerging opportunities in multiscale modeling and data-driven discovery to outline future directions for the design and application of high-performance bioinspired materials. This review aims to bridge the gap between molecular-level understanding and macroscopic functionality, thereby supporting the rational design of next-generation sustainable materials.

This study investigates the intricacies of animal decision-making in T-maze environments through a synergistic approach combining computational modeling and machine learning techniques. Focusing on the binary decision-making process in T-mazes, we examine how animals navigate choices between two paths. Our research employs a mathematical model tailored to the decision-making behavior of fish, offering analytical insights into their complex behavioral patterns. To complement this, we apply advanced machine learning algorithms, specifically Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid approach involving Principal Component Analysis (PCA) for dimensionality reduction followed by SVM for classification to analyze behavioral data from zebrafish and rats. The above techniques result in high predictive accuracies, approximately 98.07% for zebrafish and 98.15% for rats, underscoring the efficacy of computational methods in decoding animal behavior in controlled experiments. This study not only deepens our understanding of animal cognitive processes but also showcases the pivotal role of computational modeling and machine learning in elucidating the dynamics of behavioral science.

This work investigates the utilization of ensemble machine learning methods in forecasting the distribution of chemical concentrations in membrane separation system for removal of an impurity from water. Mass transfer was evaluated using CFD and machine learning performed numerical simulations. A membrane contactor was employed for the separation and mass transfer analysis for the removal of organic molecules from water. The process is simulated via computational fluid dynamics and machine learning. Utilizing a dataset of over 25,000 data points with r(m) and z(m) as inputs, four tree-based learning algorithms were employed: Decision Tree (DT), Extremely Randomized Trees (ET), Random Forest (RF), and Histogram-based Gradient Boosting Regression (HBGB). Hyper-parameter optimization was conducted using Successive Halving, a method aimed at efficiently allocating computational resources to optimize model performance. The ET model emerged as the top performer, with R² of 0.99674. The ET model exhibited a RMSE of 37.0212 mol/m³ and a MAE of 19.6784 mol/m³. The results emphasize the capability of ensemble machine learning techniques to accurately estimate solute concentration profiles in membrane engineering applications.

Summary of Defense Advanced Research Projects Agency's (DARPA) explainable artificial intelligence (XAI) program from the program managers' and evaluator's perspective. Defense Advanced Research Projects Agency (DARPA) formulated the explainable artificial intelligence (XAI) program in 2015 with the goal to enable end users to better understand, trust, and effectively manage artificially intelligent systems. In 2017, the 4-year XAI research program began. Now, as XAI comes to an end in 2021, it is time to reflect on what succeeded, what failed, and what was learned. This article summarizes the goals, organization, and research progress of the XAI program. Dramatic success in machine learning has created an explosion of new AI capabilities. Continued advances promise to produce autonomous systems that perceive, learn, decide, and act on their own. These systems offer tremendous benefits, but their effectiveness will be limited by the machine's inability to explain its decisions and actions to human users. This issue is especially important for the United States Department of Defense (DoD), which faces challenges that require the development of more intelligent, autonomous, and reliable systems. XAI will be essential for users to understand, appropriately trust, and effectively manage this emerging generation of artificially intelligent partners. The problem of explainability is, to some extent, the result of AI's success. In the early days of AI, the predominant reasoning methods were logical and symbolic. These early systems reasoned by performing some form of logical inference on (somewhat) human readable symbols. Early systems could generate a trace of their inference steps, which could then become the basis for explanation. As a result, there was significant work on how to make these systems explainable.1-5 Yet, these early AI systems were ineffective; they proved too expensive to build and too brittle against the complexities of the real world. Success in AI came as researchers developed new machine learning techniques that could construct models of the world using their own internal representations (eg, support vectors, random forests, probabilistic models, and neural networks). These new models were much more effective, but necessarily more opaque and less explainable. The year 2015 was an inflection point in the need for XAI. Data analytics and machine learning had just experienced a decade of rapid progress.6 The deep learning revolution had just begun, following the breakthrough ImageNet demonstration in 2012.6, 7 The popular press was alive with animated speculation about Superintelligence8 and the coming AI Apocalypse.9, 10 Everyone wanted to know how to understand, trust, and manage these mysterious, seemingly inscrutable, AI systems. 2015 also saw the emergence of initial ideas for providing explainability. Some researchers were exploring deep learning techniques, such as the use of deconvolutional networks to visualize the layers of convolutional networks.11 Other researchers were pursuing techniques to learn more interpretable models, such as Bayesian Rule Lists.12 Others were developing model-agnostic techniques that could experiment with a machine learning model—as a black box—to infer an approximate, explainable model, such as LIME.13 Yet, others were evaluating the psychological and human-computer interaction aspects of the explanation interface.13, 14 DARPA spent a year surveying researchers, analyzing possible research strategies, and formulating the goals and structure of the program. In August 2016, DARPA released DARPA-BAA-16-53 to call for proposals. The stated goal of explainable artificial intelligence (XAI) was to create a suite of new or modified machine learning techniques that produce explainable models that, when combined with effective explanation techniques, enable end users to understand, appropriately trust, and effectively manage the emerging generation of AI systems. The target of XAI was an end user who depends on decisions or recommendations produced by an AI system, or actions taken by it, and therefore needs to understand the system's rationale. For example, an intelligence analyst who receives recommendations from a big data analytics system needs to understand why it recommended certain activity for further investigation. Similarly, an operator who tasks an autonomous system needs to understand the system's decision-making model to appropriately use it in future missions. The XAI concept was to provide users with explanations that enable them to understand the system's overall strengths and weaknesses; convey an understanding of how it will behave in future/different situations; and perhaps permit users to correct the system's mistakes. The XAI program assumed an inherent tension between machine learning performance (eg, predictive accuracy) and explainability, a concern that was consistent with the research results at the time. Often the highest performing methods (eg, deep learning) were the least explainable and the most explainable (eg, decision trees) were the least accurate. The program hoped to create a portfolio of new machine learning and explanation techniques to provide future practitioners with a wider range of design options covering the performance-explainability trade space. If an application required higher performance, the XAI portfolio would include more explainable, high-performing, deep learning techniques. If an application required more explainability, XAI would include higher performing, interpretable models. The program was organized into three major technical areas (TAs), as illustrated in Figure 1: (a) the development of new XAI machine learning and explanation techniques for generating effective explanations; (b) understanding the psychology of explanation by summarizing, extending and applying psychological theories of explanation; and (c) evaluation of the new XAI techniques in two challenge problem areas: data analytics and autonomy. The original program schedule consisted of two phases: phase 1, Technology Demonstrations (18 months); and phase 2, Comparative Evaluations (30 months). During phase 1, developers were asked to demonstrate their technology against their own test problems. During phase 2, the original plan was to have developers test their technology against one of two common problems (Figure 2) defined by the government evaluator. At the end of phase 2, the developers were expected to contribute prototype software to an open source XAI toolkit. In May 2017, XAI development began. Eleven research teams were selected to develop the Explainable Learners (TA1) and one team was selected to develop the Psychological Models of Explanation. Evaluation was provided by the Naval Research Lab. The following summarizes those developments and the final state of this work at the end of the program. An interim summary of the XAI developments at the end of 2018 is given in Gunning and Aha.15 The program anticipated that researchers would examine the training process, model representations, and, importantly, explanation interfaces. Three general approaches were envisioned for model representations. Interpretable model approaches would seek to develop ML models that were inherently more explainable and more introspectable for machine learning experts. Deep explanation approaches would leverage deep learning or hybrid deep learning approaches to produce explanations in addition to predictions. Finally, model induction techniques would create approximate explainable models from more opaque, black-box models. Explanation interfaces were expected to be a critical element of XAI, connecting a user to the model to enable them to understand and interact with the decision making process. As the research progressed, 11 XAI teams explored a number of machine learning approaches, such as tractable probabilistic models16 and causal models and explanation techniques such as state machines generated by reinforcement learning algorithms,17 Bayesian teaching,18 visual saliency maps,19-24 and network and GAN dissection.24-26 Perhaps the most challenging and most unique contributions came from the combination of machine learning and explanation techniques27 to conduct well-designed psychological experiments to evaluate explanation effectiveness.28-31 As the program progressed, we also gained a more refined understanding of the spectrum of users and development timeline (Figure 3). The program structure anticipated the need for a grounded psychological understanding of explanation. One team was selected to summarize current psychological theories of explanation to assist the XAI developers and the evaluation team. This work began with an extensive literature survey on the psychology of explanation and previous work on explainability in AI.32 Originally, this team was asked to (a) produce a summary of current theories of explanation, (b) develop a computational model of explanation from those theories, and (c) validate the computational model against the evaluation results from the XAI developers. Developing computational models proved to be a bridge too far, but the team did gain a deep understanding of the area and successfully produced descriptive models. These descriptive models were critical to supporting the effective evaluation approaches, which involved carefully designed user studies, carried out in accordance with DoD human subject research guidelines. Figure 4 illustrates a top-level descriptive model of the XAI explanation process. Evaluation was originally envisioned to be based on a common set of problems, within the data analytics and autonomy domains. However, it quickly became clear that it would be more valuable to explore a variety of approaches across a breadth of problem domains. In order to evaluate the performance in the final year of the program, the evaluation team, led by Eric Vorm, PhD, of the US Naval Research Laboratory (NRL), developed an explanation scoring system (ESS). This scoring system provided a quantitative mechanism for assessing the designs of XAI user studies in terms of technical and methodological appropriateness and robustness. The ESS enabled the assessments of multiple elements of each user study, including the task, domain, explanations, explanation interface, users, hypothesis, data collection, and analysis to ensure that each study met the high standards of human subject research. XAI evaluation measures are shown in Figure 5, and include functional measures, learning performance measures, and explanation effectiveness measures. The DARPA XAI program demonstrated definitively the importance of carefully designing user studies in order to accurately evaluate the effectiveness of explanations in ways that directly enhance appropriate use and trust by human users, and appropriately support human-machine teaming. Often times, multiple types of measures (ie, performance, functionality, and explanation effectiveness) will be necessary to evaluate the performance of an XAI algorithm. XAI user study design can be tricky and the DARPA XAI program discovered that the most effective research teams were ones that featured diverse teams with cross-disciplinary expertise (ie, computer science combined with human-computer interaction and/or experimental psychology, etc.). The XAI program explored many approaches, as shown in Table 1. Interactive debugger interface for visualizing poisoned training datasets. Work is applied on the IARPA TrojAI dataset.33 Establishing objective/quantitative criteria to assess value of explanations for ML models34 CNN-based one-shot detector, using network dissection to identify the most salient features41 Explanations produced by heat maps and text explanations42 Human-machine common ground modeling Indoor navigation with a robot (in collaboration with GA Tech) Video Q&A Human-assisted one-shot classification system by identifying the most salient features Three major evaluations were conducted during the program: one during phase 1 and two during phase 2. In order to evaluate the effectiveness of XAI techniques, researchers on the program designed and executed user studies. User studies are still the gold standard for assessing explanations. There were approximately 12 700 participants in user studies carried out by XAI researchers, including approximately 1900 supervised participants, where the individual was guided through the experiment by the research team (eg, in person or on Zoom) and 10 800 unsupervised participants, where the individual self-guided through the experiment and was not actively guided by the research team (eg, Amazon Mechanical Turk). In accordance with policy for all US DoD funded human subjects research, each research protocol was reviewed by a local Institutional Review Board (IRB) and then a DoD human research protection office reviewed the protocol and the local IRB findings. As mentioned earlier, there seemed to be a natural tension between learning performance and explainability. However, throughout the course of the program, we found evidence that explainability can improve performance. From an intuitive perspective, training a system to produce explanations provides additional supervision, via additional loss functions, training data, or other mechanisms, that encourages a system to learn more effective representations of the world. While this may not be true in all cases and significant work remains to characterize when explainable techniques will be more performant, it provides hope that future XAI systems can be more performant than current systems while meeting user needs for explanations. There currently is no universal solution to XAI. As discussed earlier, different user types require different types of explanations. This is no different from what we face interacting with other humans. Consider, for example, a doctor needing to explain a diagnosis to a fellow doctor, a patient, or a medical review board. Perhaps future XAI systems will be able to automatically calibrate and communicate explanations to a specific user within a large range of user types, but that is still significantly beyond the current state of the art. One of the challenges in developing XAI is measuring the effectiveness of an explanation. DARPA's XAI effort has helped develop foundational technology in this area, but much more needs to be done, including drawing more from the human factors and psychology communities. Measures of explanation effectiveness need to be well established, well understood, and easily implemented by the developer community in order for effective explanations to become a core capability of ML systems. UC Berkeley's result21 demonstrating that advisability, the ability for an AI system to take advice from a user, improves user trust beyond explanations is intriguing. Certainly, users will likely prefer systems where they can quickly correct the behavior of a system in the same ways that humans can provide feedback to each other. Such advisable AI systems that can both produce and consume explanations will be key to enabling closer collaborations between humans and AI systems. Close collaboration is required across multiple disciplines including computer science, machine learning, artificial intelligence, human factors, and psychology, among others, in order to effectively develop XAI techniques. This can be particularly challenging, as researchers tend to focus on a single domain and often need to be pushed to work across domains. Perhaps in the future a XAI-specific research discipline will be created at the intersection of multiple current disciplines. Toward this end, we have worked to create an XAI Toolkit, which collects the various program artifacts (eg, code, papers, reports, etc.) and lessons learned from the 4-year DARPA XAI program into a central, publicly accessible location (https://xaitk.org/).48 We believe the toolkit will be of broad interest to anyone who deploys AI capabilities in operational settings and needs to validate, characterize, and trust AI performance across a wide range of real-world conditions and application areas. Today, we have a more nuanced, less dramatic, and, perhaps, more accurate understanding of AI than we had in 2015. We certainly have a more accurate understanding of the possibilities and the limitations of deep learning. The AI apocalypse has faded from an imminent danger to a distant curiosity. Similarly, the XAI program has produced a more nuanced, less dramatic, and, perhaps, more accurate understanding of XAI. The program certainly acted as a catalyst to stimulate XAI research (both inside and outside of the program). The results have produced a more nuanced understanding of XAI uses and users, the psychology of XAI, the challenges of measuring explanation effectiveness, as well as producing a new portfolio of XAI ML and HCI techniques. There is certainly more work to be done, especially as new AI techniques are developed that will continue to need explanation. XAI will continue as an active research area for some time. The authors believe that the XAI program has made a significant contribution by providing the foundation to launch that endeavor. David Gunning (now retired) is a three-time DARPA program manager, who created and managed the XAI program from its inception in 2016 to its mid-point in 2019. His portfolio of DARPA research programs made significant contributions to the development of AI over the past 25 years. He led the Personalized Assistant that Learns (PAL) program, which produced the technologies behind Apple's Siri. His Command Post of the Future (CPoF) program was later adopted by the US Army as their Command and Control system for use during the Iraq and Afghanistan conflicts. Between DARPA tours, David served in senior positions at Facebook AI, Palo Alto Research Center, Vulcan Inc, Cycorp and co-founded SET Corp. Eric Vorm, PhD, is a cognitive systems engineer and serves as the Deputy Director for the Laboratory for Autonomous Systems Research at the US Naval Research Laboratory in Washington, DC. Dr Vorm led the evaluation team for the DARPA Explainable AI program, and led the development of the first comprehensive criteria for the evaluation of explanations generated by machine learning. Dr Vorm's research focuses on the design of intelligent systems to achieve ideal human-machine teaming, with special emphasis on the role of transparency and explainability in supporting appropriate trust, safety, and reliability in high-risk, time-sensitive operational domains. Jennifer Yunyan Wang, PhD, is a computational neuroscientist with a special focus on AI. As Systems, Engineering and technical Assistance (SETA) contractor to DARPA, she provided technical support and expertise to several programs including XAI, L2M, GARD, and AIE RED. After finishing postdoctoral fellowships in experimental neuroscience at Johns Hopkins University and the Food and Drug Administration, Jennifer joined Quantitative Scientific Solutions in 2018 as a consultant for government R&D and think tanks including IARPA and Center for Security and Emerging Technology. Matt Turek, PhD, joined DARPA's Information Innovation Office (I2O) as a program manager in July 2018 and took over as program manager of the XAI program in 2019. His portfolio also includes the Media Forensics (MediFor), Semantic Forensics (SemaFor), and Machine Common Sense (MCS) programs, as well as the Reverse Engineering of Deceptions (RED) AI Exploration. His research interests include computer vision, machine learning, artificial intelligence, and their application to problems with significant societal impact. Prior to his position at DARPA, Turek led a team at Kitware Inc developing computer vision technologies including large scale behavior recognition and modeling, object detection and tracking, activity recognition, normalcy modeling, and anomaly detection. Data sharing is not applicable to this article as no new data were created or analyzed in this editorial.

In today’s dynamic financial landscape, the integration of computational finance, machine learning, and risk analytics is revolutionizing financial planning and analysis (FP&A). Computational finance leverages mathematical modeling, numerical simulations, and algorithmic techniques to optimize investment strategies and capital allocation. Meanwhile, machine learning enhances predictive capabilities, enabling data-driven decision-making that improves portfolio performance, market forecasting, and credit risk assessment. Risk analytics complements these advancements by quantifying uncertainties, mitigating financial volatility, and ensuring robust risk management frameworks. The convergence of these technologies offers a more refined and adaptive approach to financial strategy development. Computational finance provides the foundation for quantitative models, while machine learning algorithms refine predictions by identifying patterns in vast financial datasets. Risk analytics further strengthens financial decision-making by assessing potential vulnerabilities, stress testing scenarios, and ensuring compliance with regulatory requirements. This integration is particularly crucial in corporate finance, investment banking, and fintech sectors, where accurate forecasting and risk mitigation directly impact profitability and sustainability. As financial markets become increasingly complex, the adoption of advanced AI-driven risk analytics and machine learning-based forecasting models enhances efficiency, reduces operational risks, and improves financial resilience. However, challenges such as data quality, model interpretability, and ethical considerations must be addressed to fully realize the potential of these technologies. This study explores the synergy between computational finance, machine learning, and risk analytics, emphasizing their role in shaping the future of optimized FP&A strategies. By leveraging these innovations, financial institutions can enhance decision-making accuracy, improve regulatory compliance, and optimize financial performance in a rapidly evolving economic environment.

This Reprint brings together selected contributions from the Special Issue Computational Intelligence and Machine Learning: Models and Applications, showcasing recent advances at the intersection of intelligent algorithms and real-world problem solving. The collected papers reflect the growing maturity of computational intelligence and machine learning as core technologies driving innovation across science, engineering, and society. This Reprint highlights methodological advances in distributed, semi-supervised, and weakly supervised learning, addressing challenges such as data uncertainty, decentralization, and limited labeling. It also presents application-driven studies showing how modern models adapt to domains including agriculture, cybersecurity, sports analytics, and document intelligence. Further emphasis is placed on human-centered AI, examining trust, interpretability, user behavior, and technology acceptance.By combining theoretical insights with domain-specific applications, this Reprint emphasizes a shift toward scalable, context-aware, and interpretable machine learning solutions. The contributions collectively illustrate current trends in computational intelligence, including multimodal learning, edge and distributed computing, and responsible AI design.This Reprint is intended for researchers, practitioners, and graduate students working in machine learning, data science, and applied artificial intelligence, as well as for readers interested in how advanced models translate into impactful applications across domains.

Severe acute respiratory syndrome of coronavirus 2 (SARS-CoV-2) is a respiratory virus that disrupts the functioning of several organ systems. The cardiovascular system represents one of the systems targeted by the novel coronavirus disease (COVID-19). Indeed, a hypercoagulable state was observed in some critically ill COVID-19 patients. The timely prediction of thrombosis risk in COVID-19 patients would help prevent the incidence of thromboembolic events and reduce the disease burden. This work proposes a methodology that identifies COVID-19 patients with a high thromboembolism risk using computational modelling and machine learning. We begin by studying the dynamics of thrombus formation in COVID-19 patients by using a mathematical model fitted to the experimental findings of in vivo clot growth. We use numerical simulations to quantify the upregulation in the size of the formed thrombi in COVID-19 patients. Next, we show that COVID-19 upregulates the peak concentration of thrombin generation (TG) and its endogenous thrombin potential. Finally, we use a simplified 1D version of the clot growth model to generate a dataset containing the hemostatic responses of virtual COVID-19 patients and healthy subjects. We use this dataset to train machine learning algorithms that can be readily deployed to predict the risk of thrombosis in COVID-19 patients.

Electrocatalysis presents novel pathways for advanced water treatment. For example, electrocatalytic reduction is an emerging technology for treating oxyanions of concern in water. However, the identification of highly performant, cost-effective catalysts remains a major barrier to deployment at scale. This article discusses how computational modeling and machine learning (ML) can accelerate the search for new catalyst materials for electrocatalytic water treatment processes, such as the electroreduction of oxyanions and degradation of persistent organics. It describes how traditional computational chemistry workflows, now deployed in their basic form for at least two decades, can be expanded in breadth and depth through newly developed machine-learned force fields that have been trained on millions of data points. It also discusses ways in which the theory and ML pipeline can be effectively integrated with experimental synthesis and characterization platforms to rapidly identify and validate new catalyst chemistries for water purification challenges.

Learning What Makes Catalysts Good

This research presents a framework for modeling and predicting the spatial distribution of particle concentrations in public transportation systems, specifically buses and trains, using a combination of Computational Fluid Dynamics (CFD) simulations and Machine Learning (ML) techniques. By integrating these methodologies, the study provides valuable insights into improving air quality and ensuring passenger safety through enhanced ventilation strategies, emitter configurations, and data-driven risk assessments. The parametric analysis revealed significant differences in particle behavior between buses and trains. Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) were used to identify high-risk scenarios, assess variability, and analyze the impact of ventilation rates (ACH), emitter configurations, and thermal conditions. Buses exhibited broader Relative Concentration distributions with higher particle concentrations, whereas trains demonstrated more consistent particle dispersion. Higher ACH levels were found to stabilize air quality, reducing RC values and variability, while emitters placed at the front of vehicles provided the most efficient particle dispersion. Thermal conditions had a minimal influence on RC; therefore, it can be concluded with certainty that thermal conditions did not significantly impact particle distribution behavior. CFD simulations further highlighted the critical role of ACH in reducing particle concentrations, with mean RC values decreasing by up to 73% in buses and 62% in trains as ACH increased. The number and location of emitters significantly influenced particle accumulation, with back-located emitters producing the highest RC values. Spatial analysis showed that certain seating areas consistently experienced higher RC values, such as seats 14-19 in buses and seats 30-54 in trains, indicating particle "hotspots." 3D mapping revealed that the front zones in buses and the middle zones in trains exhibited the highest probabilities of RC > 1, underscoring ventilation inefficiencies in these areas. Machine learning models were employed to predict RC values, with Neural Networks, Random Forest, and Gradient Boosting models achieving high accuracy (R² = 0.87-0.90) and low error rates. These models outperformed linear regression and support vector regressors, which struggled with the nonlinear nature of particle distribution. However, model accuracy declined for higher RC thresholds, highlighting the challenges posed by data imbalance in extreme cases. This study offers a robust framework for analyzing particle distribution in public transportation, identifying high-risk zones, and developing effective mitigation strategies. By leveraging CFD and ML techniques, the research provides actionable insights to enhance ventilation design and passenger safety, paving the way for safer and healthier public transportation systems. Keywords: Particle Distribution, Computational Fluid Dynamics (CFD), Machine Learning (ML), Transportation System, Relative Concentration (RC), Parametric and Statistical analysis, Air Changes per Hour (ACH), Emitters, 3D Mapping, Exposure Risks

Computer modeling, machine learning, and artificial intelligence are currently considered cutting-edge topics in chemistry and materials science. The application of information technologies in natural sciences can help researchers collect big data and understand patterns that are not obvious to humans. In this perspective, I would like to highlight the recent achievements of our research group and other researchers in relation to computer modeling and machine learning in chemistry and materials science.

Biomechanics plays a significant yet complex role in osteoarthritis (OA) onset and progression. Identifying alterations in biomechanical factors and their complex interactions is critical for gaining new insights into OA pathophysiology and identification of clearly defined and modifiable mechanical treatment targets. This review synthesized biomechanics studies from March 2022 to April 2023, from which three themes relating to human gait emerged: (1) new insights into the pathogenesis of OA using computational modelling and machine learning, (2) technology-enhanced biomechanical interventions for OA, and (3) out-of-lab biomechanical assessments of OA. We further highlighted future-focused areas which may continue to advance the field of biomechanics in OA, with a particular emphasis on exploiting technology to understand and treat biomechanical mechanisms of OA outside the laboratory. The breadth of studies included in this review highlights the complex role of biomechanics in OA and showcase numerous innovative and outstanding contributions to the field. Exciting cross-disciplinary efforts integrating computational modelling, mobile sensors, and machine learning methods show great promise for streamlining in vivo multi-scale biomechanics workflows and are expected to underpin future breakthroughs in the understanding and treatment of biomechanics in OA.

The current handling of data in earth observation, modelling and prediction measures gives cause for critical consideration, since we all too often carelessly ignore data uncertainty. We think that Earth scientists are generally aware of the importance of linking data to quantitative uncertainty measures. But we also think that uncertainty quantification of Earth observation data too often fails at very early stages. We claim that data acquisition without uncertainty quantification is not sustainable and machine learning and computational modelling cannot unfold their potential when analysing complex natural systems like the Earth. Current approaches such as stochastic perturbation of parameters or initial conditions cannot quantify uncertainty or bias arising from the choice of model, limiting scientific progress. We need incentives stimulating the honest treatment of uncertainty starting during data acquisition, continuing through analysis methodology and prediction results. Computational modellers and machine learning experts have a critical role, since they enjoy high esteem from stakeholders and their methodologies and their results critically depend on data uncertainty. If both want to advance their uncertainty assessment of models and predictions of complex systems like the Earth, they have a common problem to solve. Together, computational modellers and machine learners could develop new strategies for bias identification and uncertainty quantification offering a more all‐embracing uncertainty quantification than any known methodology. But since it starts for computational modellers and machine learners with data and their uncertainty, the fundamental first step in such a development would be leveraging shareholder esteem to insistently advocate for reduction of ignorance when it comes to uncertainty quantification of data.

Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computational analysis and models to identify predictive image biomarkers that characterize intra-tumoral dynamics throughout the course of treatment. This is particularly difficult in gliomas, where heterogeneity has been well established at a molecular level as well as visually in conventional imaging. Thus, acquiring clinically useful features remains difficult due to temporal variations in tumor dynamics. Identifying surrogate biomarkers through radiomics may provide a non-invasive means of characterizing biologic activities of gliomas. We present an extensive literature review of radiomics-based analysis, with a particular focus on computational modeling, machine learning, and fractal-based analysis in improving differential diagnosis and predicting clinical outcomes. Novel strategies in extracting quantitative features, segmentation methods, and their clinical applications are producingpromising results. Moreover, we provide a detailed summary of the morphometric parameters that have so far been proposed as a means of quantifying imaging characteristics of gliomas. Newly emerging radiomic techniques via machine learning and fractal-based analyses holds considerable potential for improving diagnostic and prognostic accuracy of gliomas. Key points• Radiomic features can be mined through computational analysis to produce quantitative imaging biomarkers that characterize intra-tumoral dynamics throughout the course of treatment.• Surrogate image biomarkers identified through radiomics could enable a non-invasive means of characterizing biologic activities of gliomas.• With novel analytic algorithms, quantification of morphological or sub-regional tumor features to predict survival outcomes is producing promising results.• Quantifying intra-tumoral heterogeneity may improve grading and molecular sub-classifications of gliomas.• Computational fractal-based analysis of gliomas allows geometrical evaluation of tumor irregularities and complexity, leading to novel techniques for tumor segmentation, grading, and therapeutic monitoring.