Reactive transport modelling as a toolbox to compare remediation strategies for aquifers impacted by uranium in situ recovery.

More than 60% of worldwide uranium production is based on the In Situ Recovery mining technique. This exploitation method directly falls within the scope of the applications of reactive transport modelling to optimize uranium production and limit its associated environmental impact. We propose a modelling approach which is able to represent the natural evolution of an aquifer impacted by an ISR test performed using sulfuric acid. The model is calibrated on a 12 year-long data series obtained from 12 monitoring wells surrounding an ISR pilot cell. Through this process-based approach, we simulate the impact of several remediation strategies that can be considered in these contexts. In particular, we model the impact of Pump & Treat combined with reverse osmosis, as well as the circulation of non-impacted fluids through the reservoir with different operating strategies. Our approach allows to compare the effectiveness of these strategies. For this small-scale ISR pilot, monitored natural attenuation constitutes an interesting approach due to its faster pH recovery time with respect to Pump & Treat (5-10 years to pH ∼ 6), whose efficiency can be improved by the addition of exchangeable cations. Circulation of unimpacted fluids can reduce pH recovery times if performed for periods longer than the ISR exploitation and/or deployed with a delay. Combined with an economic evaluation of their deployment, this modelling approach can help the mining operator select and design optimal remediation strategies from an environmental and economical standpoint.

Mathematical Assessment of the Role of Intervention Programs for Malaria Control.

Malaria remains a global health problem despite the many attempts to control and eradicate it. There is an urgent need to understand the current transmission dynamics of malaria and to determine the interventions necessary to control malaria. In this paper, we seek to develop a fit-for-purpose mathematical model to assess the interventions needed to control malaria in an endemic setting. To achieve this, we formulate a malaria transmission model to analyse the spread of malaria in the presence of interventions. A sensitivity analysis of the model is performed to determine the relative impact of the model parameters on disease transmission. We explore how existing variations in the recruitment and management of intervention strategies affect malaria transmission. Results obtained from the study imply that the discontinuation of existing interventions has a significant effect on malaria prevalence. Thus, the maintenance of interventions is imperative for malaria elimination and eradication. In a scenario study aimed at assessing the impact of long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), and localized individual measures, our findings indicate that increased LLINs utilization and extended IRS coverage (with longer-lasting insecticides) cause a more pronounced reduction in symptomatic malaria prevalence compared to a reduced LLINs utilization and shorter IRS coverage. Additionally, our study demonstrates the impact of localized preventive measures in mitigating the spread of malaria when compared to the absence of interventions.

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment.

Importance: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective: To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design: Multicohort study. Setting: Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants: Individuals without HF at baseline. Exposures: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures: Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance: Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.

How can agent-based modelling provide new insights into the impact of minimum unit pricing in Scotland?

In recent years we have gained insight into the impact of minimum unit pricing (MUP)-a legal floor price below which a given volume of alcohol cannot be sold-on population-level reductions in alcohol sales, consumption and harm. However, several questions remain unanswered including how individual-level purchasing changes impact the local economy (e.g., balance between on-licence and off-licence outlets), lead to long-term population-level trends (e.g., youth drinking) and social harms (e.g., violence). Agent-based modelling captures heterogeneity, emergence, feedback loops and adaptive and dynamic features, which provides an opportunity to understand the nuanced effects of MUP. Agent-based models (ABM) simulate heterogeneous agents (e.g., individuals, organisations) often situated in space and time that interact with other agents and/or with their environment, allowing us to identify the mechanisms underlying social phenomena. ABMs are particularly useful for theory development, and testing and simulating the impacts of policies and interventions. We illustrate how ABMs could be applied to generate novel insights and provide best estimates of social network effects, and changes in purchasing behaviour and social harms, due to the implementation of MUP. ABMs like other modelling approaches can simulate alternative implementations of MUP (e.g., policy intensity [£0.50, £0.60] or spatial scales [local, national]) but can also provide an understanding of the potential impact of MUP on different population groups (e.g., alcohol exposure of young people who are not yet drinking). Using ABMs to understand the impact of MUP would provide new insights to complement those from traditional epidemiological and other modelling methods.

Economic evaluation of the effect of needle and syringe programs on skin, soft tissue, and vascular infections in people who inject drugs: a microsimulation modelling approach.

BACKGROUND: Needle and syringe programs (NSP) are effective harm-reduction strategies against HIV and hepatitis C. Although skin, soft tissue, and vascular infections (SSTVI) are the most common morbidities in people who inject drugs (PWID), the extent to which NSP are clinically and cost-effective in relation to SSTVI in PWID remains unclear. The objective of this study was to model the clinical- and cost-effectiveness of NSP with respect to treatment of SSTVI in PWID. METHODS: We performed a model-based, economic evaluation comparing a scenario with NSP to a scenario without NSP. We developed a microsimulation model to generate two cohorts of 100,000 individuals corresponding to each NSP scenario and estimated quality-adjusted life-years (QALY) and cost (in 2022 Canadian dollars) over a 5-year time horizon (1.5% per annum for costs and outcomes). To assess the clinical effectiveness of NSP, we conducted survival analysis that accounted for the recurrent use of health care services for treating SSTVI and SSTVI mortality in the presence of competing risks. RESULTS: The incremental cost-effectiveness ratio associated with NSP was $70,278 per QALY, with incremental cost and QALY gains corresponding to $1207 and 0.017 QALY, respectively. Under the scenario with NSP, there were 788 fewer SSTVI deaths per 100,000 PWID, corresponding to 24% lower relative hazard of mortality from SSTVI (hazard ratio [HR] = 0.76; 95% confidence interval [CI] = 0.72-0.80). Health service utilization over the 5-year period remained lower under the scenario with NSP (outpatient: 66,511 vs. 86,879; emergency department: 9920 vs. 12,922; inpatient: 4282 vs. 5596). Relatedly, having NSP was associated with a modest reduction in the relative hazard of recurrent outpatient visits (HR = 0.96; 95% CI = 0.95-0.97) for purulent SSTVI as well as outpatient (HR = 0.88; 95% CI = 0.87-0.88) and emergency department visits (HR = 0.98; 95% CI = 0.97-0.99) for non-purulent SSTVI. CONCLUSIONS: Both the individuals and the healthcare system benefit from NSP through lower risk of SSTVI mortality and prevention of recurrent outpatient and emergency department visits to treat SSTVI. The microsimulation framework provides insights into clinical and economic implications of NSP, which can serve as valuable evidence that can aid decision-making in expansion of NSP services.

A systematic review of the modelling and economic evaluation studies assessing regulatory options for e-cigarette use.

BACKGROUND: Governments around the world are considering regulating access to nicotine e-cigarettes to prevent uptake among youth however people that smoke tobacco may use them to assist with smoking cessation. The health and cost implications of regulating e-cigarette use among populations are unknown but have been explored in modelling studies. We reviewed health economic evaluation and simulation modelling studies that assessed long-term consequences and interpret their potential usefulness for decision-makers. METHODS: A systematic review with a narrative synthesis was undertaken. Six databases were searched for modelling studies evaluating population-level e-cigarette control policies or interventions restricting e-cigarette use versus more liberalized use. Studies were required to report the outcomes of life years, quality-adjusted life years (QALYs) and/or healthcare costs. The quality of the studies was assessed using two quality assessment tools. RESULTS: In total, 15 studies were included with nine for the United States and one each for the United Kingdom, Italy, Australia, Singapore, Canada, and New Zealand. Three studies included cost-utility analyses. Most studies involved health state transition (or Markov) closed cohort models. Many studies had limitations with their model structures, data input quality and transparency, and insufficient analyses handling model uncertainty. Findings were mixed with 11 studies concluding that policies permitting e-cigarette use lead to net benefits and 4 studies concluding net losses in life-years or QALYs and/or healthcare costs.Five studies had industry conflicts of interest. CONCLUSIONS: While authors did conclude net benefit than net harm in more of the studies so far conducted, the significant limitations that we identified with many of the studies in this review, make it uncertain whether or not countries can expect net population harms or benefits of restrictive versus unrestrictive e-cigarette policies. The generalizability of the findings is limited for decision-makers. In light of the deep uncertainty around the health and economic outcomes of e-cigarettes, simulation modelling methods and uncertainty analyses should be strengthened.

Toward Adoption of Health Risk Assessment in Population-Based and Clinical Scenarios: Lessons From JADECARE.

Introduction: Health risk assessment (HRA) strategies are cornerstone for health systems transformation toward value-based patient-centred care. However, steps for HRA adoption are undefined. This article analyses the process of transference of the Adjusted Morbidity Groups (AMG) algorithm from the Catalan Good Practice to the Marche region (IT) and to Viljandi Hospital (EE), within the JADECARE initiative (2020-2023). Description: The implementation research approach involved a twelve-month pre-implementation period to assess feasibility and define the local action plans, followed by a sixteen-month implementation phase. During the two periods, a well-defined combination of experience-based co-design and quality improvement methodologies were applied. Discussion: The evolution of the Catalan HRA strategy (2010-2023) illustrates its potential for health systems transformation, as well as its transferability. The main barriers and facilitators for HRA adoption were identified. The report proposes a set of key steps to facilitate site customized deployment of HRA contributing to define a roadmap to foster large-scale adoption across Europe. Conclusions: Successful adoption of the AMG algorithm was achieved in the two sites confirming transferability. Marche identified the key requirements for a population-based HRA strategy, whereas Viljandi Hospital proved its potential for clinical use paving the way toward value-based healthcare strategies.

JAGS model specification for spatiotemporal epidemiological modelling.

Bayesian inference in modelling infectious diseases using Bayesian inference using Gibbs Sampling (BUGS) is notable in the last two decades in parallel with the advancements in computing and model development. The ability of BUGS to easily implement the Markov chain Monte Carlo (MCMC) method brought Bayesian analysis to the mainstream of infectious disease modelling. However, with the existing software that runs MCMC to make Bayesian inferences, it is challenging, especially in terms of computational complexity, when infectious disease models become more complex with spatial and temporal components, in addition to the increasing number of parameters and large datasets. This study investigates two alternative subscripting strategies for creating models in Just Another Gibbs Sampler (JAGS) environment and their performance in terms of run times. Our results are useful for practitioners to ensure the efficiency and timely implementation of Bayesian spatiotemporal infectious disease modelling.

Assessment of urban underground spaces inundation during extreme rainfall events.

As urbanization progresses and the impacts of climate change become more pronounced, urban flooding has emerged as a critical challenge for resilient cities, particularly concerning urban underground spaces where flooding can lead to significant loss of life and property. Drawing upon a comprehensive review of global research on underground space flood simulation and evacuation, this paper undertakes the modelling of inundation in a substantial underground area during the extraordinary rainfall event on 7 September 2023, in Shenzhen, China. Specifically, it introduces a two-step method to simulate the coupled surface-underground inundation process with high accuracy. The study simulates the inflow processes in three types of underground spaces: parking lots, metro stations, and underpasses. Utilizing the specific force per unit width evaluation, the research examines how varying flood barrier heights influence evacuation time and inundation risk. Subsequently, the paper proposes corresponding evacuation strategies based on the obtained findings. By highlighting the vulnerability of urban underground spaces to flooding, the study underscores the urgent need for further research in this domain.

Forecasting synergistic pathways between rare earth elements, renewable energy, and product and economic complexities in achieving a low-carbon future.

Recent decades have witnessed an unprecedented transformation in the global energy landscape, driven by environmental concerns and the quest for sustainable economic growth. As the world grapples with the urgent need for decarbonization, the utilization of renewable energy technologies with the instrumental role of rare earth elements (REEs) has come to the forefront. However, empirical investigations into their synergistic pathways for product and economic complexities concerning achieving a low-carbon future remain scarce. Therefore, we forecast synergistic pathways between the REE supply, renewable energy, economic and product complexities, and GDP growth using a panel dataset of 11 REE-producing countries from 1990 to 2023. We used Common Correlated Effects and Temporal Causal Models as primary methods to estimate panel long-run elasticities and subsequently forecast mutual causal synergies between the variables. The results indicated that REE supply led to renewable energy and economic growth that further elevated the countries' product and economic complexities rankings. GDP growth increased REE production, economic complexity, and renewable energy directly, and consequently, product complexity and REE production through them. This underscores the positive role of REE production coupled with renewable energy technologies in achieving a low-carbon future based on economic diversification, enhanced industrial capabilities, and technological sophistication.

Assessment of vulnerability to flood risk in the Padma River Basin using hydro-morphometric modeling and flood susceptibility mapping.

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km-2, 0.13 km-2, 0.25 km-2, 0.30 km-2, and 0.28 km-2 and drainage densities of 0.63 km-1, 0.16 km-1, 0.29 km-1, 0.35 km-1, and 0.33 km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

Next generation risk assessment for occupational chemical safety - A real world example with sodium-2-hydroxyethane sulfonate.

Next Generation Risk Assessment (NGRA) is an exposure-led approach to safety assessment that uses New Approach Methodologies (NAMs). Application of NGRA has been largely restricted to assessments of consumer use of cosmetics and is not currently implemented in occupational safety assessments, e.g. under EU REACH. By contrast, a large proportion of regulatory worker safety assessments are underpinned by toxicological studies using experimental animals. Consequently, occupational safety assessment represents an area that would benefit from increasing application of NGRA to safety decision making. Here, a workflow for conducting NGRA under an occupational safety context was developed, which is illustrated with a case study chemical; sodium 2-hydroxyethane sulphonate (sodium isethionate or SI). Exposures were estimated using a standard occupational exposure model following a comprehensive life cycle assessment of SI and considering factory-specific data. Outputs of this model were then used to estimate internal exposures using a Physiologically Based Kinetic (PBK) model, which was constructed with SI specific Absorption, Distribution, Metabolism and Excretion (ADME) data. PBK modelling indicated a worst-case plasma maximum concentration (Cmax) of 0.8 µM across the SI life cycle. SI bioactivity was assessed in a battery of NAMs relevant to systemic, reproductive, and developmental toxicity; a cell stress panel, high throughput transcriptomics in three cell lines (HepG2, HepaRG and MCF-7 cells), pharmacological profiling and specific assays relating to developmental toxicity (Reprotracker and devTOX quickPredict). Points of Departure (PoDs) for SI ranged from 104 to 5044 µM. Cmax values obtained from PBK modelling of occupational exposures to SI were compared with PoDs from the bioactivity assays to derive Bioactivity Exposure Ratios (BERs) which demonstrated the safety for workers exposed to SI under current levels of factory specific risk management. In summary, the tiered and iterative workflow developed here represents an opportunity for integrating non animal approaches for a large subset of substances for which systemic worker safety assessment is required. Such an approach could be followed to ensure that animal testing is only conducted as a "last resort" e.g. under EU REACH.

Impact of Institutional Practices and Surgical Complexity on Sarcoma Surgery Costs: Driving Efficiency in Value-Based Healthcare.

BACKGROUND: Sarcomas present a unique challenge within healthcare systems due to their rarity and complex treatment requirements. This study explores the economic impact of sarcoma surgeries across three Swiss tertiary healthcare institutions, utilizing a consistent surgical approach by a single surgeon to eliminate variability in surgical expertise as a confounding factor. METHODS: By analyzing data from 356 surgeries recorded in a real-world-time data warehouse, this study assesses surgical and hospital costs relative to institutional characteristics and surgical complexity. RESULTS: Our findings reveal significant cost variations driven more by institutional resource management and pricing strategies than by surgical techniques. Surgical and total hospitalization costs were analyzed in relation to tumor dignity and complexity scores, showing that higher complexity and malignancy significantly increase costs. Interestingly, it was found that surgical costs accounted for only one-third of the total hospitalization costs, highlighting the substantial impact of non-surgical factors on the overall cost of care. CONCLUSIONS: The study underscores the need for standardized cost assessment practices and highlights the potential of predictive models in enhancing resource allocation and surgical planning. By advocating for value-based healthcare models and standardized treatment guidelines, this research contributes to more equitable and sustainable healthcare delivery for sarcoma patients. These insights affirm the necessity of including a full spectrum of care costs in value-based models to truly optimize healthcare delivery. These insights prompt a reevaluation of current policies and encourage further research across diverse geographical settings to refine cost management strategies in sarcoma treatment.

Climate change impact and adaptation assessment for road drainage systems.

Climate change exhibits a clear trend of escalating frequency and intensity of extreme weather events, posing heightened risks to drainage systems along the existing road networks. However, very few studies to date have investigated the consequences of projected future changes in rainfall on main road drainage and the resulting risk of road flooding. The work presented in this paper builds on the limited research by introducing a probabilistic model for assessing the impact of climate change on road drainage systems, incorporating climate uncertainty and drainage system variation. The probabilistic scenario-based model and associated framework offer a practical and innovative method for estimating the impact of short-duration storms under future climates for 2071-2100, in the absence of fine-resolution spatio-temporal data. The model also facilitates the assessment of the effectiveness of a climate adaptation strategy. An illustrative case-study of a road drainage system located in the south of Ireland is presented. It was found that the probability of road flooding during intense rainfall is projected to surpass the current acceptable limits set by Irish standards. Assessment of a proactive climate adaptation strategy implemented in 2015 indicated it may need to be adjusted to further reduce climate change impacts and optimise adaptation costs.

Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution.

We developed a high-resolution machine learning based surrogate model to identify a robust land-use future for Australia which meets multiple UN Sustainable Development Goals. We compared machine learning models with different architectures to pick the best performing model considering the data type, accuracy metrics, ability to handle uncertainty and computational overhead requirement. The surrogate model, called ML-LUTO Spatial, was trained on the Land-Use Trade-Offs (version 1.0) model of Australian agricultural land system sustainability. Using the surrogate model, we generated projections of land-use futures at 1.1 km resolution with 95% classification accuracy, and which far surpassed the computational benchmarks of the original model. This efficiency enabled the generation of numerous SDG-compliant (SDGs 2, 6, 7, 13, 15) future land-use maps on a standard laptop, a task previously dependent upon high-performance computing clusters. Combining these projections, we derived a single, robust land-use future and quantified the uncertainty. Our findings indicate that while agricultural land-use remains dominant in all Australian regions, extensive carbon plantings were identified in Queensland and environmental plantings played a role across the study area, reflecting a growing urgency for offsetting greenhouse gas emissions and the restoration of ecosystems to support biodiversity across Australia to meet the 2050 Sustainable Development Goals.

Unpacking the effects of natural gas price transmission on electricity prices in Nordic countries.

Since the Russia-Ukraine war in February 2022, European electricity prices have experienced considerable turbulence, primarily attributed to a shortage in the natural gas supply. We investigate the relationship between natural gas prices in the European continent and electricity prices in Nordic countries before and after the outbreak of war. Despite the low proportion of natural gas electricity generation, the empirical analysis reveals both direct and indirect transmission paths for natural gas prices in Nordic countries. Meanwhile, the theoretical analysis demonstrates how Nordic renewable (wind and solar) and other non-gas generators exercise market power through price bidding in the anticipation of an increase in gas prices or a shortage of gas supply, which results in higher electricity prices. Understanding the underlying factors and dynamics driving substantial price fluctuations in the Nordic electricity market is essential for comprehending the intricate interconnections within the European energy landscape.

The neural implausibility of the diffusion decision model doesn't matter for cognitive psychometrics, but the Ornstein-Uhlenbeck model is better.

In cognitive psychometrics, the parameters of cognitive models are used as measurements of the processes underlying observed behavior. In decision making, the diffusion decision model (DDM) is by far the most commonly used cognitive psychometric tool. One concern when using this model is that more recent theoretical accounts of decision-making place more emphasis on neural plausibility, and thus incorporate many assumptions not found in the DDM. One such model is the Ising Decision Maker (IDM), which builds from the assumption that two pools of neurons with self-excitation and mutual inhibition receive perceptual input from external excitatory fields. In this study, we investigate whether the lack of such mechanisms in the DDM compromises its ability to measure the processes it does purport to measure. We cross-fit the DDM and IDM, and find that the conclusions of DDM would be mostly consistent with those from an analysis using a more neurally plausible model. We also show that the Ornstein-Uhlenbeck Model (OUM) model, a variant of the DDM that includes the potential for leakage (or self-excitation), reaches similar conclusions to the DDM regarding the assumptions they share, while also sharing an interpretation with the IDM in terms of self-excitation (but not leakage). Since the OUM is relatively easy to fit to data, while being able to capture more neurally plausible mechanisms, we propose that it be considered an alternative cognitive psychometric tool to the DDM.

Advancing sustainable materials in a circular economy for decarbonisation.

This research paper delves into the intricate interplay between decarbonisation and sustainability, focusing on adopting chemical looping technologies. Deep decarbonisation scenarios necessitate a profound transformation in various sectors to mitigate climate change, and oil refineries, as pivotal players, must adapt to these changes. Employing the BLUES integrated assessment model, we evaluate the evolution of the refining sector in decarbonisation pathways, emphasising its potential for sustainability through repurposing and emissions mitigation. Additionally, we delve into chemical looping technologies, including Solar Thermal Chemical Looping (STCL), Reverse Water Gas Shift Chemical Looping (RWGS-CL), Chemical Looping Reforming (CLR), and Super Dry Reforming (SDR), elucidating their principles and contributions to carbon dioxide (CO2) conversion. These technologies offer promising routes for CO2 capture and present opportunities for sustainable carbon loop cycles, potentially revolutionising industries' emissions reduction efforts. In a world of climate change, this research illuminates a sustainable path forward by integrating decarbonisation and innovative CO2 management strategies.

Evaluation of enhanced nanofiltration membranes for improving magnesium recovery schemes from seawater/brine: Integrating experimental performing data with a techno-economic assessment.

The global demand for valuable metals and minerals necessitates the exploration of alternative, sustainable approaches to mineral recovery. Seawater mining has emerged as a promising option, offering a vast reserve of minerals and an environmentally friendly alternative to land-based mining. Among the various techniques, Nanofiltration (NF) has gained significant attention as a preliminary treatment step in Minimum Liquid Discharge (MLD) and Zero Liquid Discharge (ZLD) schemes. This study focused on the potential of two underexplored commercial polyamide based NF membranes, Synder NFX and Vontron VNF1, with enhanced divalent over monovalent separation factors, in optimizing the extraction of magnesium hydroxide (Mg(OH)2) from seawater and seawater reverse osmosis (SWRO) brines. The research encompassed a thorough characterization of the membranes utilizing advanced physic-chemical analytical techniques, followed by rigorous experimental assessments using synthetic seawater and SWRO brine in concentration configuration. The findings highlighted the superior selectivity of NFX for magnesium recovery from SWRO brine and the promising concentration factors of VNF1 for seawater treatment. Cross-validation of experimental data with a mathematical model demonstrated the model's reliability as a process design tool in predicting membrane performance. A comprehensive techno-economic evaluation demonstrates the potential of NFX, operating optimally at 23 bar pressure and 70% permeate recovery rate, to yield an estimated annual revenue of 5.683 M€/yr through Mg(OH)2 production from SWRO brine for a plant with a nominal capacity of 0.8 Mm3/y. This research shed light on the promising role of NF membranes in enhancing mineral recovery taking benefit of their separation factors and emphasizes the economic viability of leveraging NF technology for maximizing magnesium recovery from seawater and SWRO brines.

Public health and economic benefits of seasonal influenza vaccination in risk groups in France, Italy, Spain and the UK: state of play and perspectives.

BACKGROUND: Seasonal influenza epidemics have a substantial public health and economic burden, which can be alleviated through vaccination. The World Health Organization (WHO) recommends a 75% vaccination coverage rate (VCR) in: older adults (aged ≥ 65 years), individuals with chronic conditions, pregnant women, children aged 6-24 months and healthcare workers. However, no European country achieves this target in all risk groups. In this study, potential public health and economic benefits achieved by reaching 75% influenza VCR was estimated in risk groups across four European countries: France, Italy, Spain, and the UK. METHODS: A static epidemiological model was used to estimate the averted public health and economic burden of increasing the 2021/2022 season VCR to 75%, using the efficacy data of standard-dose quadrivalent influenza vaccine. For each country and risk group, the most recent data on population size, VCR, pre-pandemic influenza epidemiology, direct medical costs and absenteeism were identified through a systematic literature review, supplemented by manual searching. Outcomes were: averted influenza cases, general practitioner (GP) visits, hospitalisations, case fatalities, number of days of work lost, direct medical costs and absenteeism-related costs. RESULTS: As of the 2021/2022 season, the UK achieved the highest weighted VCR across risk groups (65%), followed by Spain (47%), France (44%) and Italy (44%). Based on modelling, the 2021/2022 VCR prevented an estimated 1.9 million influenza cases, avoiding 375,200 GP visits, 73,200 hospitalisations and 38,400 deaths. To achieve the WHO 75% VCR target, an additional 24 million at-risk individuals would need to be vaccinated, most of which being older adults and patients with chronic conditions. It was estimated that this could avoid a further 918,200 influenza cases, 332,000 GP visits, 16,300 hospitalisations and 6,300 deaths across the four countries, with older adults accounting for 52% of hospitalisations and 80% of deaths. An additional €84 million in direct medical costs and €79 million in absenteeism costs would be saved in total, with most economic benefits delivered in France. CONCLUSIONS: Older adults represent most vaccine-preventable influenza cases and deaths, followed by individuals with chronic conditions. Health authorities should prioritise vaccinating these populations for maximum public health and economic benefits.