The effect of adsorbent textural and functional properties on model naphthenic acid adsorption.

Naphthenic acids, NAs, are a major contaminant of concern and a focus of much research around remediation of oil sand process affected waters, OSPW. Using activated carbon adsorbents are an attractive option given their low cost of fabrication and implementation. A deeper evaluation of the effect NA structural differences have on uptake affinity is warranted. Here we provide an in-depth exploration of NA adsorption including many more model NA species than have been assessed previously with evaluation of adsorption kinetics and isotherms at the relevant alkaline pH of OSPW using several different carbon adsorbents with pH buffering to simulate the behaviour of real OSPW. Uptake for the NA varied considerably regardless of the activated carbon used, ranging from 350 mg/g to near zero highlighting recalcitrant NAs. The equilibrium data was explored to identify structural features of these species and key physiochemical properties that influence adsorption. We found that certain NA will be resistant to adsorption when hydrophobic adsorbents are used. Adsorption isotherm modelling helped explore interactions occurring at the interface between NA and adsorbent surfaces. We identified the importance of NA hydrophobicity for activated carbon uptake. Evidence is also presented that indicates favorable hydrogen bonding between certain NA and surface site hydroxyl groups, demonstrating the importance of adsorbent surface functionality for NA uptake. This research highlights the challenges associated with removing NAs from OSPW through adsorption and also identifies how adsorbent surface chemistry modification can be used to increase the removal efficiency of recalcitrant NA species.

Nedosiran population pharmacokinetic and pharmacodynamic modelling and simulation to guide clinical development and dose selection in patients with primary hyperoxaluria type 1.

AIM: To characterize pharmacokinetic and pharmacodynamic profiles of nedosiran in patients with primary hyperoxaluria type 1 (PH1), identify influential covariates and confirm therapeutic doses. METHODS: A population pharmacokinetic (PK)/pharmacodynamic (PD) (POP-PKPD) model was developed to characterize the concentration-time course of nedosiran and the corresponding effect on 24-h urinary oxalate (Uox). Simulations of dosing to achieve clinically meaningful reduction in Uox in children, adolescents and adults with PH1 were performed. RESULTS: Analyses included PK data from 143 healthy participants and PH1/PH2 patients, and PD data from 46 PH1 patients. Nedosiran PK was described by a two-compartment model with dual n-transit absorption and parallel linear and nonlinear elimination. The relationship between nedosiran exposure and Uox was described by an indirect response model. Body weight, estimated glomerular filtration rate (eGFR) and disease status were identified as influential covariates for the POP-PK model. The simulation results supported a weight-banded dosing regimen of nedosiran sodium in adolescents and adults (≥12 years) with PH1 of 170 mg (weight ≥50 kg) and 136 mg (weight <50 kg), in children (6-11 years) with PH1 of 3.5 mg/kg, and no dose adjustments for PH1 patients with relatively preserved kidney function (eGFR ≥ 30 mL/min/1.73m2). Following the proposed dosing regimens, the simulated median fold-changes in PK AUC0-τ,ss were acceptable (≤1.51 fold-change) and ~71% of PH1 patients across all age groups achieved near-normal Uox (<0.6 mmol) by week 52. CONCLUSIONS: The final POP-PKPD model characterizes observed nedosiran PK and Uox data. Simulations support nedosiran dosing regimens in PH1 patients aged ≥6 years with relatively preserved kidney function.

Metabolic modelling identifies mitochondrial Pi uptake and pyruvate efflux as key aspects of daytime metabolism and proton homeostasis in crassulacean acid metabolism leaves.

Crassulacean acid metabolism (CAM) leaves are characterized by nocturnal acidification and diurnal deacidification processes related with the timed actions of phosphoenolpyruvate carboxylase and Rubisco, respectively. How CAM leaves manage cytosolic proton homeostasis, particularly when facing massive diurnal proton effluxes from the vacuole, remains unclear. A 12-phase flux balance analysis (FBA) model was constructed for a mature malic enzyme-type CAM mesophyll cell in order to predict diel kinetics of intracellular proton fluxes. The charge- and proton-balanced FBA model identified the mitochondrial phosphate carrier (PiC, Pi/H+ symport), which provides Pi to the matrix to sustain ATP biosynthesis, as a major consumer of cytosolic protons during daytime (> 50%). The delivery of Pi to the mitochondrion, co-transported with protons, is required for oxidative phosphorylation and allows sufficient ATP to be synthesized to meet the high energy demand during CAM Phase III. Additionally, the model predicts that mitochondrial pyruvate originating from decarboxylation of malate is exclusively exported to the cytosol, probably via a pyruvate channel mechanism, to fuel gluconeogenesis. In this biochemical cycle, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) acts as another important cytosolic proton consumer. Overall, our findings emphasize the importance of mitochondria in CAM and uncover a hitherto unappreciated role in metabolic proton homeostasis.

The use of predictive modelling to determine the likelihood of donor return during the COVID-19 pandemic.

Artificial intelligence (AI) uses sophisticated algorithms to "learn" from large volumes of data. This could be used to optimise recruitment of blood donors through predictive modelling of future blood supply, based on previous donation and transfusion demand. We sought to assess utilisation of predictive modelling and AI blood establishments (BE) and conducted predictive modelling to illustrate its use. A BE survey of data modelling and AI was disseminated to the International Society of Blood transfusion members. Additional anonymzed data were obtained from Italy, Singapore and the United States (US) to build predictive models for each region, using January 2018 through August 2019 data to determine likelihood of donation within a prescribed number of months. Donations were from March 2020 to June 2021. Ninety ISBT members responded to the survey. Predictive modelling was used by 33 (36.7%) respondents and 12 (13.3%) reported AI use. Forty-four (48.9%) indicated their institutions do not utilise predictive modelling nor AI to predict transfusion demand or optimise donor recruitment. In the predictive modelling case study involving three sites, the most important variable for predicting donor return was number of previous donations for Italy and the US, and donation frequency for Singapore. Donation rates declined in each region during COVID-19. Throughout the observation period the predictive model was able to consistently identify those individuals who were most likely to return to donate blood. The majority of BE do not use predictive modelling and AI. The effectiveness of predictive model in determining likelihood of donor return was validated; implementation of this method could prove useful for BE operations.

Radiocarbon dataset for the TRB central-place at Kaldus, Poland.

This dataset compiles radiocarbon dates received for botanical macroremains and animal bones from domestic and ritual pits and human graves unearthed during excavations at the archaeological site of Kaldus (Poland) that can be related to the Funnel Beaker culture (TRB). Prior to radiocarbon dating by accelerator mass spectrometry (AMS), plant macroremains were checked against diagnostic attributes of species identification by standard paleobotanical analysis. The dataset contains already published (n = 4) and new (n = 10) radiocarbon dates that were used to establish the absolute chronology of the TRB habitus at Kaldus and its diachronic spatial organization. This dataset serves as an archive for future studies focusing on the TRB settlement pattern and organization in the region of modern Poland. It also has a utility to be reused in archaeological and chronological research on the movement of copper metalwork and the gradual spread of human cremation rite in the region.

Treating gaps and biases in biodiversity data as a missing data problem.

Big biodiversity data sets have great potential for monitoring and research because of their large taxonomic, geographic and temporal scope. Such data sets have become especially important for assessing temporal changes in species' populations and distributions. Gaps in the available data, especially spatial and temporal gaps, often mean that the data are not representative of the target population. This hinders drawing large-scale inferences, such as about species' trends, and may lead to misplaced conservation action. Here, we conceptualise gaps in biodiversity monitoring data as a missing data problem, which provides a unifying framework for the challenges and potential solutions across different types of biodiversity data sets. We characterise the typical types of data gaps as different classes of missing data and then use missing data theory to explore the implications for questions about species' trends and factors affecting occurrences/abundances. By using this framework, we show that bias due to data gaps can arise when the factors affecting sampling and/or data availability overlap with those affecting species. But a data set per se is not biased. The outcome depends on the ecological question and statistical approach, which determine choices around which sources of variation are taken into account. We argue that typical approaches to long-term species trend modelling using monitoring data are especially susceptible to data gaps since such models do not tend to account for the factors driving missingness. To identify general solutions to this problem, we review empirical studies and use simulation studies to compare some of the most frequently employed approaches to deal with data gaps, including subsampling, weighting and imputation. All these methods have the potential to reduce bias but may come at the cost of increased uncertainty of parameter estimates. Weighting techniques are arguably the least used so far in ecology and have the potential to reduce both the bias and variance of parameter estimates. Regardless of the method, the ability to reduce bias critically depends on knowledge of, and the availability of data on, the factors creating data gaps. We use this review to outline the necessary considerations when dealing with data gaps at different stages of the data collection and analysis workflow.

Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy.

Public health responses to the COVID-19 pandemic varied across the world. Some countries (e.g. mainland China, New Zealand and Taiwan) implemented elimination strategies involving strict travel measures and periods of rigorous non-pharmaceutical interventions (NPIs) in the community, aiming to achieve periods with no disease spread; while others (e.g. many European countries and the USA) implemented mitigation strategies involving less strict NPIs for prolonged periods, aiming to limit community spread. Travel measures and community NPIs have high economic and social costs, and there is a need for guidelines that evaluate the appropriateness of an elimination or mitigation strategy in regional contexts. To guide decisions, we identify key criteria and provide indicators and visualizations to help answer each question. Considerations include determining whether disease elimination is: (1) necessary to ensure healthcare provision; (2) feasible from an epidemiological point of view and (3) cost-effective when considering, in particular, the economic costs of travel measures and treating infections. We discuss our recommendations by considering the regional and economic variability of Canadian provinces and territories, and the epidemiological characteristics of different SARS-CoV-2 variants. While elimination may be a preferable strategy for regions with limited healthcare capacity, low travel volumes, and few ports of entry, mitigation may be more feasible in large urban areas with dense infrastructure, strong economies, and with high connectivity to other regions.

The population-level impact of introducing rapid diagnostic tests on syphilis transmission in Canadian arctic communities - a mathematical modeling study.

Background: Canadian Arctic communities have experienced sustained syphilis transmission, with diagnoses rates 18-times higher than the national average. Remoteness from laboratory facilities leads to delays between syphilis screening and treatment, contributing to onward transmission. Rapid diagnostic tests can eliminate treatment delays via testing at the point-of-care. This study aims to describe syphilis diagnostic gaps and to estimate the impact of introducing rapid diagnostic tests at the point-of-care on syphilis transmission. Methods: To assess the population-level impact of deploying rapid diagnostic tests, an individual-based model was developed using detailed surveillance data, population surveys, and a prospective diagnostic accuracy field study. The model was calibrated to syphilis diagnoses (2017-2022) from a community of approximately 1,050 sexually active individuals. The impacts of implementing rapid diagnostic tests using whole blood (sensitivity: 92% for infectious and 81% for non-infectious syphilis; specificity: 99%) from 2023 onward was calculated using the annual median fraction of cumulative new syphilis infections averted over 2023-2032. Findings: The median modeled syphilis incidence among sexually active individuals was 44 per 1,000 in 2023. Males aged 16-30 years exhibited a 51% lower testing rate than that of their female counterparts. Maintaining all interventions constant at their 2022 levels, implementing rapid diagnostic tests could avert a cumulative 33% (90% credible intervals: 18-43%) and 37% (21-46%) of new syphilis infections over 5 and 10 years, respectively. Increasing testing rates and contact tracing may enhance the effect of rapid diagnostic tests. Interpretation: Implementing rapid diagnostic tests for syphilis in Arctic communities could reduce infections and enhance control of epidemics. Such effective diagnostic tools could enable rapid outbreak responses by providing same-day testing and treatment at the point-of-care. Funding: Canadian Institutes of Health Research.

Integrated environmental management and GPS-X modelling for current and future sustainable wastewater treatment: A case study from the Middle East.

In the context of today's rapidly changing environmental challenges, accurately predicting the performance and efficiency of environmental management strategies is crucial. Particularly in the Middle East, where research on wastewater treatment plants (WWTPs) is notably lacking, addressing this need is imperative. This study investigates the treatment efficiency of a wastewater treatment plant and proposes various techniques to enhance its performance. Employing a case study method, we utilise the GPS-X model to forecast the plant's performance under diverse scenarios, offering solutions for future challenges. The results reveal that the current plant layout operates efficiently, with removal efficiencies for Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) at 98.3 %, 95.1 %, and 96.1 %, respectively. The outlet Dissolved Oxygen (DO) of 1.9 mg/L meets local wastewater reuse standards. Furthermore, the GPS-X model forecasts the plant's performance under different scenarios, suggesting the feasibility of a new layout within 20-25 years and the need for additional units after 40 years. As inflow approaches maximum design capacity, simulation results underscore the importance of utilising the full plant design and expanding it for optimal operation over 60 years. This research provides critical insights for improving WWTP performance and emphasizes the significance of strategic planning in addressing long-term environmental management challenges. Moreover, this study represents a pioneering effort in addressing critical water scarcity challenges in Jordan by exploring the potential of treated wastewater (TWW) as a sustainable solution, thus contributing to the advancement of environmental management practices in the region.

STEP: extraction of underlying physics with robust machine learning.

A prevalent class of challenges in modern physics are inverse problems, where physical quantities must be extracted from experimental measurements. End-to-end machine learning approaches to inverse problems typically require constructing sophisticated estimators to achieve the desired accuracy, largely because they need to learn the complex underlying physical model. Here, we discuss an alternative paradigm: by making the physical model auto-differentiable we can construct a neural surrogate to represent the unknown physical quantity sought, while avoiding having to relearn the known physics entirely. We dub this process surrogate training embedded in physics (STEP) and illustrate that it generalizes well and is robust against overfitting and significant noise in the data. We demonstrate how STEP can be applied to perform dynamic kernel deconvolution to analyse resonant inelastic X-ray scattering spectra and show that surprisingly simple estimator architectures suffice to extract the relevant physical information.

Modelling hepatitis C infection acquired from blood transfusions in the UK between 1970 and 1991 for the Infected Blood Inquiry.

The Statistics Expert Group was convened at the request of the Infected Blood Inquiry to provide estimates of the number of infections and deaths from blood-borne infections including hepatitis B virus, human immunodeficiency virus, hepatitis C virus (HCV) and variant Creutzfeldt Jakob disease, as a direct result of contaminated blood and blood products administered in the United Kingdom of Great Britain and Northern Ireland (UK). In the absence of databases of HCV infections and related deaths for all nations of the UK, a statistical model was required to estimate the number of infections and subsequent deaths from HCV acquired from blood transfusions from January 1970 to August 1991. We present this statistical model in detail alongside the results of its application to each of the four nations in the UK. We estimated that 26 800 people (95% uncertainty interval 21 300-38 800) throughout the UK were chronically infected with HCV because of contaminated blood transfusions between January 1970 and August 1991. The number of deaths up to the end of 2019 that occurred as a result of this chronic infection is estimated to be 1820 (95% uncertainty interval 650-3320).

Understanding driving forces of food waste separation intention to enhance regional and local solid waste planning: application of PLS-SEM and multi-group analysis.

This study extended the theory of planned behaviour (TPB) and applied it to analyse influencing factors of food waste separation intention (FSI) among households in central Vietnam. Partial least squares structural equation modelling (PLS-SEM) was employed to analyse the data. The heterogeneity of factors contributing to FSI in cities of three levels was examined using multi-group analysis (MGA). The results indicate that attitudes, subjective norms (SN), perceived behavioural control (PBC), awareness of benefit (AB), information publicity (IP), facility availability (FA), and trust significantly affected FSI of households. MGA results indicated the heterogeneity of impacts of PBC and attitude on FSI of households among three municipal levels. The results will serve as basic data for waste officers, solid-waste management project leaders, non-governmental organisations (NGOs), and other related stakeholders to lay the foundation of food waste management planning in terms of regional scale and local scale. This study will also aid the creation of a circular economy by providing a scientific base for enhancing food waste separation at source in central Vietnam.

3D printed microneedles: revamping transdermal drug delivery systems.

One of the advancements of the transdermal drug delivery system (TDDS) is the development of microneedles (MNs). These micron-sized needles are used for delivering various types of drugs to address the disadvantage of other transdermal techniques as well as oral drug delivery systems. MNs have high patient acceptance due to self-administration with minimally invasive and pain compared to the parenteral drug delivery. Over the years, various methods have been adopted to evolve the MNs and make them more cost-effective, accurate, and suitable for multiple applications. One such method is the 3D printing of MNs. The development of MN platforms using 3D printing has been made possible by improved features like precision, printing resolution, and the feasibility of using low-cost raw materials. In this review, we have tried to explain various types of MNs, fabrication methods, materials used in the formulation of MNs, and the recent applications that utilize 3D-printed MNs.

Climate change could fuel urinary schistosomiasis transmission in Africa and Europe.

The freshwater snail Bulinus truncatus is an important intermediate host for trematode parasites causing urogenital schistosomiasis, a tropical disease affecting over 150 million people. Despite its medical importance, uncertainty remains about its global distribution and the potential impacts of climate change on its future spread. Here, we investigate the distribution of B. truncatus, combining the outputs of correlative and mechanistic modelling methods to fully capitalize on both experimental and occurrence data of the species and to create a more reliable distribution forecast than ever constructed. We constructed ensemble correlative species distribution models using 273 occurrence points collected from different sources and a combination of climatic and (bio)physical environmental variables. Additionally, a mechanistic thermal suitability model was constructed, parameterized by recent life-history data obtained through extensive lab-based snail-temperature experiments and supplemented with an extensive literature review. Our findings reveal that the current suitable habitat for B. truncatus encompasses the Sahel region, the Middle East, and the Mediterranean segment of Africa, stretching from Southern Europe to Mozambique. Regions identified as suitable by both methods generally coincide with areas exhibiting high urogenital schistosomiasis prevalence. Model projections into the future suggest an overall net increase in suitable area of up to 17%. New suitable habitat is in Southern Europe, the Middle East, and large parts of Central Africa, while suitable habitat will be lost in the Sahel region. The change in snail habitat suitability may substantially increase the risk of urogenital schistosomiasis transmission in parts of Africa and Southern Europe while reducing it in the Sahel region.

Surgical parameters influence paediatric knee kinematics and cartilage stresses in anterior cruciate ligament reconstruction: Navigating subject-specific variability using neuromusculoskeletal-finite element modelling analysis.

PURPOSE: Anterior cruciate ligament (ACL) rupture is increasingly common in paediatric and adolescent populations, typically requiring surgical ACL reconstruction (ACLR) to restore knee stability. However, ACLR substantially alters knee biomechanics (e.g., motion and tissue mechanics) placing the patient at elevated risk of early-onset knee osteoarthritis. METHODS: This study employed a linked neuromusculoskeletal (NMSK)-finite element (FE) model to determine effects of four critical ACLR surgical parameters (graft type, size, location and pre-tension) on tibial articular cartilage stresses in three paediatric knees of different sizes during walking. Optimal surgical combinations were defined by minimal kinematic and tibial cartilage stress deviations in comparison to a corresponding intact healthy knee, with substantial deviations defined by normalized root mean square error (nRMSE) > 10%. RESULTS: Results showed unique trends of principal stress deviations across knee sizes with small knee showing least deviation from intact knee, followed by large- and medium-sized knees. The nRMSE values for cartilage stresses displayed notable variability across different knees. Surgical combination yielding the highest nRMSE in comparison to the one with lowest nRMSE resulted in an increase of maximum principal stress on the medial tibial cartilage by 18.0%, 6.0% and 1.2% for small, medium and large knees, respectively. Similarly, there was an increase of maximum principal stress on lateral tibial cartilage by 11.2%, 4.1% and 12.7% for small, medium and large knees, respectively. Knee phenotype and NMSK factors contributed to deviations in knee kinematics and tibial cartilage stresses. Although optimal surgical configurations were found for each knee size, no generalizable trends emerged emphasizing the subject-specific nature of the knee and neuromuscular system. CONCLUSION: Study findings underscore subject-specific complexities in ACLR biomechanics, necessitating personalized surgical planning for effective restoration of native motion and tissue mechanics. Future research should expand investigations to include a broader spectrum of subject-specific factors to advance personalized surgical planning. LEVEL OF EVIDENCE: Level III.

Disease progression in chronic heart failure is linear: Insights from multistate modelling.

AIMS: Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF. METHODS AND RESULTS: Consecutive patients (n = 4918) were enrolled (median age 75 [67-81] years, 61.3% men, 44% with HF and reduced ejection fraction). We generated a model by observing events during the first 2 years of follow-up. The model yielded surprisingly accurate predictions of how a population with HF will behave during subsequent years. As examples, the predicted transition probability from hospitalization to death was 0.11; the observed probabilities were 0.13, 0.14, and 0.16 at 3, 4, and 5 years, respectively. Similarly, the predicted transition intensity for rehospitalization was 0.35; the observed probabilities were 0.38, 0.34, and 0.35 at 3, 4, and 5 years, respectively. A multivariable model including covariates thought to influence outcome did not improve accuracy. Predicted average life expectancy was approximately 10 years for the unadjusted model and 13 years for the multivariable model, consistent with the observed mortality of 41% at 5 years. CONCLUSIONS: A multistate Markov chain model for patients with chronic HF suggests that the proportion of patients transitioning each year from a given state to another remains constant. This finding suggests that the course of HF at a population level is more linear than is commonly supposed and predictable based on current patient status.

Elicitation of security threats and vulnerabilities in Insurance chatbots using STRIDE.

Although chatbots are used a lot for customer relationship management (CRM), there needs to be more data security and privacy control strategies in chatbots, which has become a security concern for financial services institutions. Chatbots gain access to large amounts of vital company information and clients' personal information, which makes them a target of security attacks. The loss of data stored in chatbots can cause major harm to companies and customers. In this study, STRIDE (viz. Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege) modelling was applied to identify the data security vulnerabilities and threats that pertain to chatbots used in the insurance industry. To do this, we conducted a case study of a South African insurance organisation. The adopted methodology involved data collection from stakeholders in the insurance organisation to identify chatbot use cases and understand chatbot operations. After that, we conducted a STRIDE-based analysis of the chatbot use cases to elicit security threats and vulnerabilities in the insurance chatbots in the organisation. The results reveal that security vulnerabilities associated with Spoofing, Denial of Service, and Elevation of privilege are more relevant to insurance chatbots. The most security threats stem from Tampering, Elevation of privilege, and Spoofing. The study extends the discussion on chatbot security. It fosters an understanding of security threats and vulnerabilities that pertain to insurance chatbots, which is beneficial for security researchers and practitioners working on the security of chatbots and the insurance industry.

Modelling approach integrating climate projections for coastal groundwater management.

Climate change and excessive groundwater extraction are major contributors to rising groundwater salinization due to seawater intrusion in coastal aquifers. This study aims to define a wide-applicable approach in which hydrological balance, boundary conditions, and irrigation water demand, defined over time considering climate change predictions, can integrated into a numerical model of the groundwater system. The approach was tested in a selected coastal aquifer. The approach spans from the past, used to define steady or almost natural conditions for calibration purposes (1950-2000 in the test), to the future (2100), divided in decade steps. The water balance analysis is based on an inverse hydrogeological water balance approach. The future climate change predictions are used to assess variations in boundary conditions of the groundwater model concerning salinity and sea level, recharge, and inflow from upstream aquifers. The approach considers changes in agricultural activities, groundwater demand, and river stage. The regional model is generated using the MODFLOW code for the groundwater flow model and the SEAWAT code for the salt transport model. The test concerns the Metaponto coastal plain, in which a porous aquifer is at salinization risk due to seawater intrusion. In this way, different influences of climate change and human activities are combined to define a 3d view of groundwater depletion and salinization effects. Quantifying these potential effects or risks, adaptation scenarios with numerical assessments are outlined in this study.

Making waves: Knowledge and data fusion in urban water modelling.

Mathematical modeling plays a crucial role in understanding and managing urban water systems (UWS), with mechanistic models often serving as the foundation for their design and operations. Despite the wide adoptions, mechanistic models are challenged by the complexity of dynamic processes and high computational demands. Data-driven models bring opportunities to capture system complexities and reduce computational cost, by leveraging the abundant data made available by recent advance in sensor technologies. However, the interpretability and data availability hinder their wider adoption. This paper advocates for a paradigm shift in the application of data-driven models within the context of UWS. Integrating existing mechanistic knowledge into data-driven modeling offers a unique solution that reduces data requirements and enhances model interpretability. The knowledge-informed approach balances model complexity with dataset size, enabling more efficient and interpretable modeling in UWS. Furthermore, the integration of mechanistic and data-driven models offers a more accurate representation of UWS dynamics, addressing lingering uncertainties and advancing modelling capabilities. This paper presents perspectives and conceptual framework on developing and implementing knowledge-informed data-driven modeling, highlighting their potential to improve UWS management in the digital era.

RMCProfile7: reverse Monte Carlo for multiphase systems.

This work introduces a completely rewritten version of the program RMCProfile (version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature of RMCProfile7 is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available. RMCProfile7 is freely available for download at https://rmcprofile.ornl.gov/.