Search

The application and development of methods using hydrogen as a gas for testing active surface sites is of great interest in the study of oxide catalysts. In this work a systematic study of Pt/CeO 2 catalysts, depending on the platinum loading and calcination temperature was carried out using the method of temperature-programmed reduction by hydrogen (TPR-H 2). Experimental TPR-H 2 curves demonstrated a complex structure over a wide temperature range, including the presence of abnormally narrow consumption peaks (ANCP-H 2) at low temperatures. To describe the kinetics, mathematical modeling of H 2 consumption by various platinum active centers has been used, including isolated platinum ions (single atoms) and PtO x clusters of 2D and 3D structures on the surface of ceria nanoparticles. We proposed a criterion that makes it possible to extend the analysis of the TPR-H 2 curves for reducible metal-supported oxide systems and unambiguously establish the action of the autocatalytic mechanism in hydrogen consumption. The kinetics of ANCP-H 2 by cluster PtO x centers with a half-width of several degrees on the base of the autocatalysis mechanism was described on the base of the proposed model. The simulations show that ANCP-H 2 are described by synchronous fast formation of metallic centers that provide a sharp acceleration of H 2 consumption. In general, the obtained results establish the fundamental aspects of the H 2 interaction with heterogeneous catalysts and are of particular interest in the light of various practical applications of hydrogen energy. [Display omitted] • Pt/CeO 2 catalysts were studied depending on Pt loading using TPR-H 2 method. • TPR-H 2 curves are capable to distinguish Pt2+-SA sites and PtOx cluster. • Abnormally narrow peaks (ANCP-H 2) were observed due to 2D- and 3D-PtO x clusters. • ANCP-H 2 are well simulated on the base of the autocatalytic stages of PtO x reduction. • ANCP-H 2 is due to strong difference in the oxygen reactivity of Pt–O –Pt and Pt–O–Ce. [ABSTRACT FROM AUTHOR]

• Critical releasable charges (CRCs) are modeled by mechanism analysis. • Predicted CRCs are generally within 16 % compared with measured and simulated values. • Effect of air speed on concentration field under CRCs is studied by CFD. • Transition value of air speed is 0.1 and 0.2 m/s for 8 g and 14.2 g CRC respectively. Propane (R290) is a promising refrigerant for split-type household air conditioners with the advantages of energy saving, environmentally friendliness and low charge. Nevertheless, the large scale use of R290 has been hindered by its high flammability. The potential fire hazards can be relieved by reducing R290 releasable charge, and numerous methods have been put forward. In the authors' previous papers, the method of refrigerant pump-down was proposed and a mathematical model of critical releasable charge (CRC, ignition threshold of leakage) was developed by dimensional analysis, but it is difficult to reflect the actual physical process of R290 dispersion. To reveal the leakage and dispersion mechanism of R290, a new numerical model of CRC is developed based on mechanism analysis in this work, in which the turbulent jet model, Coanda effect and dimensional analysis are involved. The predicted CRCs agree well with experimental and simulated data. Besides, the influence of indoor air speed with its direction parallel to the IDU panel on R290 concentration field is investigated by computational fluid dynamics (CFD) under different releasable charges. It is found that there is a transition value of air speed, below which the fire hazards decrease greatly with the increase of air speed, on the contrary, when the air speed is above the transition value, raising air speed has little impact on fire hazards. [ABSTRACT FROM AUTHOR]

This paper's goal is to give an overview to tolerance analysis. The cost and quality of a product can be significantly impacted by decisions about tolerating. Increased focus on tolerance design is required if high-precision assemblies are to be produced at cheaper costs. After a component's tolerance has been determined, tolerance analysis looks to propagate the manufacturing imprecisions (geometrical defects) and to validate the value of the functional requirements. The mathematical modelling is absolutely necessary for this validation. Models for tolerance analysis are discussed in a lot of literature. There are summaries of the current state of the art, the most recent advancements, and the upcoming trends in tolerancing research. From a mathematical perspective, this paper provides a taxonomy of mathematical concepts for tolerance analysis. [ABSTRACT FROM AUTHOR]

In South Korea, the National Immunization Program has included one-dose varicella vaccination for 1-year-olds since 2005. This study examines the potential impact of introducing a two-dose varicella vaccination for children, along with zoster vaccination for adults, using either the zoster vaccine live (ZVL) or recombinant zoster vaccine (RZV). The investigation considered four strategies in a base case scenario. The first involved introducing zoster vaccination for 60-year-olds, with a 60 % coverage. The second strategy combined zoster vaccination with a second-dose varicella vaccination for 4-year-olds, with a 90 % coverage. An age-structured model spanning 50 years was employed, assuming a zoster vaccine catch-up campaign over the initial 5 years. Cost-effectiveness analyses were conducted, assessing incremental cost-effectiveness ratios (ICERs), incremental net monetary benefits (INMBs), and net loss under different ages at zoster vaccination (50, 60, 65, and 70 years) and varying willingness-to-pay (WTP) levels from ₩40 million ($34,998) to ₩84 million ($74,000). All strategies were cost-effective and significantly reduced herpes zoster (HZ) incidence, preventing approximately 3,077,000 to 7,609,000 cases, depending on the chosen strategy. The combined strategy prevented around 4,950,000 varicella and 653,000 HZ cases additionally. RZV outperformed ZVL by preventing twice as many HZ cases and offering greater QALY gains. However, ZVL was more cost-effective due to its lower cost. Probabilistic sensitivity analyses revealed that RZV became more cost-effective at higher WTP thresholds, exceeding ₩60.9 million ($53,193) in terms of ICER and ₩62.5 million ($54,591) for INMBs and net loss. The optimal age for zoster vaccination was 60 years concerning ICER but 50 years regarding INMB. Combining RZV with a two-dose varicella vaccination strategy reduced the disease burden and improved QALY more effectively, though ZVL remained more cost-effective at lower WTP levels. Decisions regarding vaccination policies should be balanced between the public health needs and WTP levels. [ABSTRACT FROM AUTHOR]

The present study investigates the viability of employing Solar parabolic trough collectors (PTC) and parabolic dish collectors (PDC) integrated with thermal energy storage (TES) as the primary heat source for a steam-powered Rankine cycle, aimed to produce 5500 kW power for green hydrogen generation. A techno-economic analysis finds the system's overall efficiency (solar input to generator output) as 15.06% with PTC and 18.80% with PDC. Besides, incorporating PTC with binary and ternary salts in the proposed system yields approximately 22.7 % lower capital costs and a 7.8 % shorter payback period than solar PDC. Likewise, the levelized hydrogen cost with these options is around 12.3 % lower than those of PDC across all zones. However, to sustain a maximum annual hydrogen production of 238.38 tonne, equivalent to an annual electricity generation of 11180.07 MWh in each zone, the required PTC area is approximately 24.85% larger than PDC area. • Concentrated Solar Power (CSP) technologies with TES systems are utilized for hydrogen production. • System's overall efficiency (solar to electric) is found to be 14.68 % with PTC and 18.33% with PDC. • The economic analysis compares the system's capital cost, payback period, and the levelized cost hydrogen production. • Month-wise hydrogen production is presented in the different Indian climatic zones. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder, characterized by the accumulation of excess fat in the liver, and is a driving factor for various severe liver diseases. These multi-factorial and multi-timescale changes are observed in different clinical studies, but these studies have not been integrated into a unified framework. In this study, we aim to present such a unified framework in the form of a dynamic mathematical model. For model training and validation, we collected data for dietary or drug-induced interventions aimed at reducing or increasing liver fat. The model was formulated using ordinary differential equations (ODEs) and the mathematical analysis, model simulation, model formulation and the model parameter estimation were all performed in MATLAB. Our mathematical model describes accumulation of fat in the liver and predicts changes in lipid fluxes induced by both dietary and drug interventions. The model is validated using data from a wide range of drug and dietary intervention studies and can predict both short-term (days) and long-term (weeks) changes in liver fat. Importantly, the model computes the contribution of each individual lipid flux to the total liver fat dynamics. Furthermore, the model can be combined with an established bodyweight model, to simulate even longer scenarios (years), also including the effects of insulin resistance and body weight. To help prepare for corresponding eHealth applications, we also present a way to visualize the simulated changes, using dynamically changing lipid droplets, seen in images of liver biopsies. In conclusion, we believe that the minimal model presented herein might be a useful tool for future applications, and to further integrate and understand data regarding changes in dietary and drug induced changes in ectopic TAG in the liver. With further development and validation, the minimal model could be used as a disease progression model for steatosis. [ABSTRACT FROM AUTHOR]

Kefir is a fermented dairy product based on the fermentation of milk by bacteria and yeasts. It is produced by adding kefir grains, consisting of a consortium of microorganisms, to milk in order to start a natural fermentation (Garofalo et al., 2020). Kefir is well recognized for its potential health value as a source of probiotics, however, there have been concerns about the potential growth of pathogenic microorganisms in kefir and the potential health hazards associated (Leite et al., 2013) A mathematical model was developed to describe the evolution of microbes present during kefir fermentation and the potential growth of Salmonella spp. as one example of a potential food safety hazard. For this, equations previously described in the scientific literature were combined and adapted to the milk kefir matrix. To assess the safety of the product, the growth of Salmonella was predicted considering its interaction with the medium and the other species. The drop in pH; generation of yeasts metabolites such as ethanol; and buffer capacity was described and considered when modelling Salmonellas' kinetics. Interaction between the pathogenic species and the background microflora was included in the model. Parameters of some well-described systems were taken from literature. Alongside, some other parameters describing specific assets from the system were estimated using experimental data of microbial population kinetics during kefir fermentation. The growth of yeasts, lactic acid bacteria, Salmonella together with the pH were experimentally collected at critical processing times and fitted to the mathematical model by minimizing the residual sum of squares (RSS). Confidence intervals of 95 % were calculated. For further validation, the output of the model was contrasted with an independent data set. It was concluded that Salmonella is able to increase its population during the first hours of the kefir fermentation process. Inactivation can be apportioned to the drop of pH as a consequence of the LAB metabolism, once the pH reaches values below 4. After this no growth of Salmonella seems to be found in milk kefir as confirmed by the model. No residual population of the pathogen is observed. This suggests that by controlling the growth and metabolism of the background microflora, safety can be assured in milk kefir. This process was successfully described by a novel mathematical model and the estimation of its'parameters. [ABSTRACT FROM AUTHOR]

The reliable incorporation of hydrogen as a clean energy source necessitates an in-depth comprehension of the dynamics of flammable clouds resulting from potential leakage incidents. This research introduces an innovative model rooted in the principles of the kinetic theory of gases to determine the dimensions of flammable hydrogen clouds during leaks. The model integrates critical variables such as leak rate, leak direction, wind speed, and wind direction, in addition to surrounding atmospheric conditions, offering a precise forecast of hydrogen dispersion and behaviour during leakage scenarios. This modelling approach relies on two dimensionless groups derived through dimensional analysis, and the correlation between these groups has been established. Furthermore, the model incorporates three constants accounting for hydrogen buoyancy, gas dilution, and mixing, which are fine-tuned through a minimal number of Computational Fluid Dynamics (CFD) simulations. This indispensable information is crucial for the formulation of effective safety protocols, evacuation plans, and risk assessment strategies. Validation of the model against simulated data attests to its accuracy and reliability, underscoring its potential as a valuable tool for steering the secure integration of hydrogen technologies. This research significantly contributes to the advancement of safety measures in the utilisation of hydrogen as a clean energy source, providing a robust foundation for the design and implementation of precautionary measures in real-world applications. • A method for predicting hydrogen flammable cloud size based on dimensional analysis and CFD is proposed. • This method allows an improvement in the calculation efficiency while ensuring its accuracy. • A detailed comparative analysis of three prediction methods was presented in this paper. • This method is suitable for explosion risk analysis during the preliminary design phase. [ABSTRACT FROM AUTHOR]

Understanding and resolving the complexity of diseases such as dengue need the use of mathematical modelling, which enables us to forecast outbreaks, create practical solutions, and allocate resources effectively to lessen the disease's effects. This research proposes a mathematical model that examines the dynamics of dengue illness transmission. Te model accounts for a number of important factors that affect the disease's spread, including the increase of the mosquito population, how long it takes an illness to develop and the extent to which infected humans also infect mosquitoes. Further, basic reproduction number has been calculated using the next generation matrix method for the single strain dengue model and SEIR-SEI model has been analyzed via mathematical theorems showing the positivity of the solutions and boundedness of the equations governing the proposed model. Sensitivity analysis has been done for basic reproduction number R0. Te findings indicate that the basic reproduction number primarily depends on factors such as the transmission rate of disease and the rate at which mosquitoes become infected by infectious human beings and depends inversely on factors such as the recovery rate of humans and the rate at which mosquitoes transmit the virus to humans. Numerical simulations have been done on a small population to show the number of people and number of mosquitoes in different compartments at any given time t and it shows the progress of disease transmission in a population. [ABSTRACT FROM AUTHOR]

The study focuses on the flow characteristics with geometrical considerations utilizing electrical-conduct Non-Newtonian Fluid. The tubular concentric annulus is the geometry selected for the investigation. The electric-conductive Non-Newtonian Fluid is a character-wise two-constant Bingham model used in this study. The study also utilized geometry, the magnetohydrodynamic (MHD) effect, and the hall current. The employed system had the inner tube spinning and the outer tube stationary. Prandtl's assumptions of boundary layer principles are applied to solve the governing equations using the finite difference method for various fluid flow parameters, the Gauss-Elimination method to compute the results, and the MHD field and geometrical factors with hall current. This paper discusses fluid flow properties such as axial, radial, tangential velocities, and pressure distribution. Further, the mentioned above flow properties impact the heat transfer effect. This research details how flow parameters in the concentric tube flow process affect the development of heat transfer effects in the MHD field and hall current. [Display omitted] • The development of MHD flow and heat transport have been studied computationally. • The impact of Hall current and Magnetic effect on the flow field has been considered. • This work refers to the non-Newtonian fluid as a two-constant yield stress fluid model. • The heat transfer impact has been calculated using the fluid flow properties. • This work has been done using a variety of fluid and geometric factors. [ABSTRACT FROM AUTHOR]

We present the first computational model of the pathophysiological consequences of phosgene-induced lung injury in porcine subjects. Data from experiments previously performed in several cohorts of large healthy juvenile female pigs (111 data points from 37 subjects), including individual arterial blood gas readings, respiratory rate and heart rate, were used to develop the computational model. Close matches are observed between model outputs (PaO 2 and PaCO 2) and the experimental data, for both terminally anaesthetised and conscious subjects. The model was applied to investigate the effectiveness of continuous positive airway pressure (CPAP) as a pre-hospital treatment method when treatment is initiated at different time points post exposure. The model predicts that clinically relevant benefits are obtained when 10 cmH 2 O CPAP is initiated within approximately 8 h after exposure. Supplying low-flow oxygen (40%) rather than medical air produced larger clinical benefits than applying higher CPAP pressure levels. This new model can be used as a tool for conducting investigations into ventilation strategies and pharmaceutical treatments for chemical lung injury of diverse aetiology, and for helping to refine and reduce the use of animals in future experimental studies. • Presents the first computational model of phosgene lung injury in porcine subjects. • Model validated against data from multiple experiments. • Model used to investigate the effectiveness of continuous positive airway pressure. • Predicts clinically relevant benefits when initiated within 8 h after exposure. • Low-flow oxygen produced larger clinical benefits than higher CPAP pressure levels. [ABSTRACT FROM AUTHOR]

• A generic energy delivery model is presented for mirrors in circular orbits. • Model includes Earth's oblateness, rotation, shadow and orbit around the Sun. • Detailed analytical insights are provided for pass duration over a solar farm. • Earlier results for simple polar orbits are extended to a range of different orbits. • The model can in principle be extended to other planetary bodies. Orbiting solar reflectors may be a useful assets to illuminate solar power farms to enhance their utility when direct sunlight is not available. The assessment of their feasibility for a variety of applications requires accurate calculations of how much solar energy can be delivered from a variety of orbits. This paper presents a generic, three-dimensional semi-analytical model that outputs the quantity of solar energy for a given circular orbit and solar power farm position at the beginning of a pass. The model extends previous studies by including new phenomena such as the Earth's oblateness, rotation, shadow on the reflector and orbit around the Sun, in addition to time-dependent geometric and atmospheric losses. These additions provide new analytical insights into the delivery of reflected solar energy delivery and demonstrate the importance of high-fidelity modelling. The strengths of the model are illustrated for a 1000 km altitude Sun-synchronous orbit throughout, as well as a range of other orbits and solar power farms located at different latitudes and longitudes. [ABSTRACT FROM AUTHOR]

The amount of SARS-CoV-2 detected in the upper respiratory tract (URT viral load) is a key driver of transmission of infection. Current evidence suggests that mechanisms constraining URT viral load are different from those controlling lower respiratory tract viral load and disease severity. Understanding such mechanisms may help to develop treatments and vaccine strategies to reduce transmission. Combining mathematical modelling of URT viral load dynamics with transcriptome analyses we aimed to identify mechanisms controlling URT viral load. COVID-19 patients were recruited in Spain during the first wave of the pandemic. RNA sequencing of peripheral blood and targeted NanoString n Counter transcriptome analysis of nasal epithelium were performed and gene expression analysed in relation to paired URT viral load samples collected within 15 days of symptom onset. Proportions of major immune cells in blood were estimated from transcriptional data using computational differential estimation. Weighted correlation network analysis (adjusted for cell proportions) and fixed transcriptional repertoire analysis were used to identify associations with URT viral load, quantified as standard deviations (z-scores) from an expected trajectory over time. Eighty-two subjects (50% female, median age 54 years (range 3–73)) with COVID-19 were recruited. Paired URT viral load samples were available for 16 blood transcriptome samples, and 17 respiratory epithelial transcriptome samples. Natural Killer (NK) cells were the only blood cell type significantly correlated with URT viral load z-scores (r = −0.62, P = 0.010). Twenty-four blood gene expression modules were significantly correlated with URT viral load z-score, the most significant being a module of genes connected around IFNA14 (Interferon Alpha-14) expression (r = −0.60, P = 1e-10). In fixed repertoire analysis, prostanoid-related gene expression was significantly associated with higher viral load. In nasal epithelium, only GNLY (granulysin) gene expression showed significant negative correlation with viral load. Correlations between the transcriptional host response and inter-individual variations in SARS-CoV-2 URT viral load, revealed many molecular mechanisms plausibly favouring or constraining viral replication. Existing evidence corroborates many of these mechanisms, including likely roles for NK cells, granulysin, prostanoids and interferon alpha-14. Inhibition of prostanoid production and administration of interferon alpha-14 may be attractive transmission-blocking interventions. [Display omitted] • SARS-CoV-2 upper respiratory viral z-scores were correlated with host response. • Negative correlation between viral load and blood NK cells and IFN-α14 module. • Positive correlation between blood prostanoid module and viral load. • Nasal granulysin expression negatively correlated with viral load z-score. • Identifies putative mechanisms favouring and restricting SARS-CoV-2 replication. [ABSTRACT FROM AUTHOR]

Next generation influenza vaccines are in development and have the potential for widespread health and economic benefits. Determining the potential health and economic impact for these vaccines is needed to drive investment in bringing these vaccines to the market, and to inform which groups public health policies on influenza vaccination should target. We used a mathematical modelling approach to estimate the epidemiological impact and cost-effectiveness of next generation influenza vaccines in England and Wales. We used data from an existing fitted model, and evaluated new vaccines with different characteristics ranging from improved vaccines with increased efficacy duration and breadth of protection, to universal vaccines, defined in line with the World Health Organisation (WHO) Preferred Product Characteristics (PPC). We calculated the cost effectiveness of new vaccines in comparison to the current seasonal vaccination programme. We calculated and compared the Incremental Cost-Effectiveness Ratio and Incremental Net Monetary Benefit for each new vaccine type. All analysis was conducted in R. We show that next generation influenza vaccines may result in a 21% to 77% reduction in influenza infections, dependent on vaccine characteristics. Our economic modelling shows that using any of these next generation vaccines at 2019 coverage levels would be highly cost-effective at a willingness to pay threshold of £20,000 for a range of vaccine prices. The vaccine threshold price for the best next generation vaccines in £-2019 is £230 (95%CrI £192 - £269) per dose, but even minimally-improved influenza vaccines could be priced at £18 (95%CrI £16 - £21) per dose and still remain cost-effective. This evaluation demonstrates the promise of next generation influenza vaccines for impact on influenza epidemics, and likely cost-effectiveness profiles. We have provided evidence towards a full value of vaccines assessment which bolsters the investment case for development and roll-out of next-generation influenza vaccines. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model in the biofilm phase and in the gas phase that has been successfully applied to investigate various aspects of the biofiltration process. The mixtures of volatile organic compounds (VOCs) are emitted from a wide range of industries, such as chemical, petrochemical, pharmaceutical, pulp paper mills, printing and paint workshops, etc. The objective of the present study is, presence of high n-propanol loading negatively affected the toluene removal; however, n-propanol removal was not affected by the presence of toluene and was effectively removed in the biofilter despite high toluene loadings. A model for toluene and n-propanol biofiltration could predict the cross-inhibition effect of n-propanol on toluene removal. Further, we studied the effects of many physical and biological parameters on model prediction. The mathematical equations (2.12)-(2.16) which are non-linear partial differential equations are solved by using the homotopy perturbation technique. Also, we derived the semi-analytical results for toluene and n -propanol concentrations for saturated and unsaturated kinetics. It is verified that the proposed solution is validated by comparing it with numerical solutions. • Here mathematical modelling is developed for bio-filtration in the biofilm phase. • The adopted model investigate various aspects of the bio-filtration process. • Analytical results is obtained via Homotopy Perturbation Technique. • The obtained solutions is verified for accuracy with existing literature. [ABSTRACT FROM AUTHOR]

• A new model-based approach was presented for the evaluation of the heat transfer coefficient in large-scale freeze-dryers using a small-scale freeze-dryer. • Results in a small-scale freeze-dryer (MicroFD®) and in a large-scale one (REVO®) were compared, pointing out the robustness and the accuracy of the developed model. • The resistance to vapor flow was also calculated and the two model parameters were used for in-silico simulations to calculate the drying time and the product temperature profiles in a different unit. This paper presents a model-based approach for the design of the primary drying stage of a freeze-drying process using a small-scale freeze-dryer (MicroFD® by Millrock Technology Inc.). Gravimetric tests, coupled with a model of the heat transfer to the product in the vials that account also for the heat exchange between the edge vials and the central vials, are used to infer the heat transfer coefficient from the shelf to the product in the vial (K v), that is expected to be (almost) the same in different freeze-dryers. Differently from other approaches previously proposed, the operating conditions in MicroFD® are not chosen to mimic the dynamics of another freeze-dryer: this allows saving time and resources as no experiments are needed in the large-scale unit, and no additional tests in the small-scale unit, apart from the three gravimetric tests usually needed to assess the effect of chamber pressure on K v. With respect to the other model parameter, R p , the resistance of the dried cake to mass transfer, it is not influenced by the equipment and, thus values obtained in a freeze-dryer may be used to simulate the drying in a different unit, provided the same filling conditions are used, as well as the same operating conditions in the freezing stage, and cake collapse (or shrinkage) is avoided. The method was validated considering ice sublimation in two types of vials (2R and 6R) and at different operating conditions (6.7, 13.3 and 26.7 Pa), with the freeze-drying of a 5% w/w sucrose solution as a test case. An accurate estimate for both K v and R p was obtained with respect to the values obtained in a pilot-scale equipment, determined through independent tests for validation purposes. Simulation of the product temperature and drying time in a different unit was then possible, and results were validated experimentally. [Display omitted] [ABSTRACT FROM AUTHOR]

• The majority of RTS,S cost-effective estimates fall below the published $9.30 USD per dose price point. • RTS,S is more cost-effective in high transmission areas which have maximized bed net coverage. • Country-level costs and limitations will be influential in subsequent sub-national analyses. The World Health Organization has recommended a 4-dose schedule of the RTS,S/AS01 (RTS,S) vaccine for children in regions of moderate to high P. falciparum transmission. Faced with limited supply and finite resources, global funders and domestic malaria control programs will need to examine the relative cost-effectiveness of RTS,S and identify target areas for vaccine implementation relative to scale-up of existing interventions. Using an individual-based mathematical model of P. falciparum , we modelled the cost-effectiveness of RTS,S across a range of settings in sub-Saharan Africa, incorporating various rainfall patterns, insecticide-treated net (ITN) use, treatment coverage, and parasite prevalence bands. We compare age-based and seasonal RTS,S administration to increasing ITN usage, switching to next generation ITNs in settings experiencing insecticide-resistance, and introduction of seasonal malaria chemoprevention (SMC) in areas of seasonal transmission. For RTS,S to be the most cost-effective intervention option considered, the maximum cost per dose was less than $9.30 USD in 90.9% of scenarios. Nearly all (89.8%) values at or above $9.30 USD per dose were in settings with 60% established bed net use and / or with established SMC, and 76.3% were in the highest Pf PR 2-10 band modelled (40%). Addition of RTS,S to strategies involving 60% ITN use, increased ITN usage or a switch to PBO nets, and SMC, if eligible, still led to significant marginal case reductions, with a median of 2,653 (IQR: 1,741 to 3,966) cases averted per 100,000 people annually, and 82,270 (IQR: 54,034 to 123,105) cases averted per 100,000 fully vaccinated children (receiving at least three doses). Use of RTS,S results in reductions in malaria cases and deaths even when layered upon existing interventions. When comparing relative cost-effectiveness, scale up of ITNs, introduction of SMC, and switching to new technology nets should be prioritized in eligible settings. [ABSTRACT FROM AUTHOR]

Phase-transition microneedles (PTMNs)-based transdermal drug delivery (TDD) is gaining popularity due to its non-invasiveness and ability to deliver a wide range of drugs. PTMNs absorb interstitial skin fluid (ISF) and transport drugs from microneedle (MNs) domain to the skin without polymer dissolution. To establish PTMNs for practical use, one needs to understand and optimise the key parameters governing drug transport mechanisms to achieve controlled drug delivery. In addressing this point, we have developed a coupled diffusion-binding-deformation model to understand the effect of physicochemical parameters (e.g., swelling capacity, drug binding) of MN and skin mechanical properties on overall drug transport behaviour. The contact mechanics at the MN and skin interface is introduced to account for the resistive force exerted by the deformed skin to MN swelling. The model is validated with the reported data of in vitro insulin delivery using polyvinyl alcohol (PVA) MN. The drug binding parameters are estimated from the fitting of the cumulative release of insulin within 6 hours of MN insertion. To predict the in vivo data of insulin delivery using the PVA MN, one-compartment model of drug pharmacokinetics is incorporated. It is shown in the paper that the model is able to predict the final insulin concentration in blood and in good agreement with the reported experimental data. The proposed model is concluded to be a tool for the predictive design and development of PTMNs-based TDD systems. [Display omitted] • The solution of transport equations is dependent on moving boundary conditions along with the contact modelling at MN/skin interface. • Penalty formulation used to model non-bonded frictional contact between MN and skin interface. • Skin viscoelasticity tends to push MN outside skin at higher swelling ratios, thus reducing drug transport rate. • Drug binding rate parameters estimated using in vitro data and used to predict in vivo drug release. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression. [ABSTRACT FROM AUTHOR]

Utilizing renewable energy sources for hydrogen production represents a key step forward in achieving a climate-neutral society by 2050. Water electrolysis using a polymer electrolyte membrane (PEM) electrolyzer is one of the most efficient ways of hydrogen production. For the mentioned electrolyzer, a mathematical model is derived whose purpose is to better describe and predict the behavior of the system and its output parameters with a brief overview of the assumptions introduced to simplify the model and their overall error. A simulation is made using MATLAB/Simulink software to calculate and plot the desired output parameters and compare these results with the data obtained from several manufacturers. To make the simulation as flexible as possible, the possibility of changing the functional simulation parameters is introduced. The option allows more diverse results and diagrams, as well as a closer inside into system performance and the detection of potential calculation errors. • Flexible Simulink model with variable operating conditions. • Water transfer across the membrane is considerable, about 10% of total incoming water. • Mathematical and Simulink model validated using available commercial electrolyzer data. • Balance of system accomplished using simple electronics and coding. [ABSTRACT FROM AUTHOR]

• We consider competition between sensitive and resistant subpopulations as an optimal control problem. • Bespoke objective functional directly penalizing domination of resistant subpopulation is analyzed. • Properties of singular arc appearing in this optimal control problem are studied. • Penalizing chemoresistance gives rise to singular arcs in the optimal control. • Numerical simulations show that non-trivial competition has a major impact on the structure of optimal control. Emergence of pesticide-resistant strains of pests, antibiotic-resistant bacteria or chemotherapy-resistant tumor cells is a major obstacle standing in the way of effective external control of undesirable growth of these populations. Once the resistant subpopulation becomes dominant the means to control the population are effectively lost. Prevention of resistance may be therefore considered one of the optimal control goals to ensure long-term controllability. In this paper we formulate nonlinear optimal control problem motivated by the question of controlling the growth of two coexisting subpopulations representing two phenotypes of the same species. However, the model we consider is a standard competition model with populations following logistic growth, and therefore it is more general and can be used also when two competing populations are taken into account. We consider a situation when the control (e.g. pesticide or drug) has effect only on the specimen belonging to one (sensitive) population while having no effect on the other (resistant) population. We use a non-standard, nonlinear objective functional in which, aside from penalizing the overall size of both populations, we include an additional, nonlinear term to ensure that the control-sensitive population remains the dominant one. It is shown how the resistance penalty gives rise to locally-optimal singular controls which are key to achieving the balance between resistant and sensitive populations whenever the subpopulations are similar in terms of the model parameters. Analytic properties of the singular arcs and the structure of optimal control are investigated. Both theoretical and numerical results strongly suggest that the optimal control contains a singular interval. [ABSTRACT FROM AUTHOR]

Energy management strategy is gaining much popularity due to the involvement of conventional/non-conventional power sources with an efficient energy storing solution to offer un-interruptible power demand. Thus, in this work a simpler logic control circuit based nonisolated bi-directional DC-DC converter with both battery and super capacitor topology is proposed. For the proposed topology, first its mathematical modelling in ideal-case with parasitic in every mode of actions is presented. To control the bidirectional DC-DC converter topology, its small signal model for individual buck and boost operation is obtained by using averaging and linearization technique. Then, a unified logic circuit-based controller is designed for the obtained systems to analyze the performance of the converter in the buck and boost mode operation. The performance of the proposed topology is verified through MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

Nanosecond pulsed lasers are widely used in the surface treatment of metallic materials due to their low cost and simple operation. During pulsed laser processing, many parameters such as laser and material properties are involved. Consequently, the existing mathematical models for pulsed laser ablation of metals are complex and computationally difficult. In this paper, a simple and novel model was proposed for nanosecond pulsed laser processing based on heat transfer and geometrical mathematics. The geometrical parameters functions of the recast layer under pulsed laser single ablation and continuous superimposed ablation are presented, respectively. The feasibility of the models is validated by experiments. Errors in diameter and depth of single-pulse laser ablation craters were 2.56%–7.14% and 6.82%–18.91%. The recast layer depth error is about 3.47%–12.47% for successive pulses of superimposed ablation. It predicts the shape of the recast layer on pulsed laser ablated metal surfaces. The model is a guide to the surface morphology of laser woven materials and the preparation of surface functionalities, particularly the selection of process parameters for microstructure machining. • A surface morphology prediction model of metals ablated by nanosecond pulsed lasers is developed. • A function mentioned about depth and diameter of a single pulse laser ablated metal crater is pointed out. • Morphological functions are given for continuous pulsed laser ablation with depth superimposed. [ABSTRACT FROM AUTHOR]

Over time, papers or reports may come to be taken for granted as evidence for some phenomenon. Researchers cite them without critically re-examining findings in the light of subsequent work. This can give rise to misleading or erroneous results and conclusions. We explore whether this has occurred in the widely reported outbreak of SARS-CoV-2 at a rehearsal of the Skagit Valley Chorale in March 2020, where it was assumed, and subsequently asserted uncritically, that the outbreak was due to a single infected person. Review of original report and subsequent modelling and interpretations. We reviewed and analysed original outbreak data in relation to published data on incubation period, subsequent modelling drawing on the data, and interpretations of transmission characteristics of this incident. We show it is vanishingly unlikely that this was a single point source outbreak as has been widely claimed and on which modelling has been based. An unexamined assumption has led to erroneous policy conclusions about the risks of singing, and indoor spaces more generally, and the benefits of increased levels of ventilation. Although never publicly identified, one individual bears the moral burden of knowing what health outcomes have been attributed to their actions. We call for these claims to be re-examined and for greater ethical responsibility in the assumption of a point source in outbreak investigations. [ABSTRACT FROM AUTHOR]

An extensive review on the numerical modelling of various flow-induced vibration (FIV) phenomenon and energy harvesting is systematically carried out in this paper. Apart from the experimental endeavours by several researchers, various modelling approaches have become crux in the study of flow-induced vibration (FIV) response of the bluff bodies with different geometries. Here, we have exclusively limited the complex FIV studies to vortex-induced vibration (VIV) and galloping. Primarily, the various computational methods adopted in the numerical modelling of FIV, particularly Reynolds averaged Navier-Stokes (RANS) modelling, direct numerical simulation (DNS) etc. are elucidated in detail while other approaches like detached eddy simulation (DES) and large eddy simulation (LES) are briefly discussed, followed by a concise review of experimental studies on this phenomenon. Secondly, the working principle and practical applications of these methodologies in harnessing energy from VIV and galloping are elaborately described. Furthermore, thriving technologies including vortex bladeless turbine and vortex-induced vibration aquatic clean energy (VIVACE) are succinctly summarized in this paper. The paper concludes with a note on the future prospects and research challenges in the area of modelling and energy capture of FIV. [Display omitted] • This paper extensively reviews the modelling techniques and the practical applications of FIV energy harvesters based on galloping and VIV in particular. • The experimental studies on the various flow-induced vibration phenomena is also studied in detail. • The widely used strategy to improve harvesting efficiency is the use of non-linear magnetic force, which changes the natural frequency of the structure to attain resonance. • Multi-directional harvesters overcome the difficulty of fluctuating power output arising due to time-varying wind speed to a certain extent and it is observed that hybrid harvesters achieve improved energy efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Experimental and modelling investigation on one method to reduce parasitic currents; • Proposed design to reduce ionic shortcut currents using manifold reducers; • Pressure losses were quantified with and without the presence of manifold reducers; • The model was able to calculate parasitic currents and how they varied in the stack; • The design was found to improve the performance of Acid-Base Flow Battery systems. The Acid-Base Flow Battery is an innovative and sustainable electrochemical storage system storing energy in the form of salinity and pH gradients. However, parasitic currents via manifolds dramatically affect system by reducing its Round-Trip Efficiency (RTE). This work experimentally studies this phenomenon using a purposely designed methodology involving sticks placed in the manifold ducts. Various figures of merit were calculated to evaluate battery performance in the charge and discharge phases. A mathematical model was validated and applied to investigate the ionic parasitic currents. The results highlighted the importance of studying this phenomenon, as achieving a reduction in the parasitic currents caused a 25% increase in the net power and more than tripled the RTE compared to the reference configuration without sticks. Although reducing manifold diameter increases pumping losses, it was found to be anyway really beneficial for the process performance and paves the way for future, more suitable, battery designs. [ABSTRACT FROM AUTHOR]

Alterations in white matter (WM) microstructure of the central nervous system have been shown to be pathophysiological presentations of various neurodegenerative disorders. Current methods for measuring such WM features require ex vivo tissue samples analyzed using electron microscopy. Magnetic Resonance Imaging (MRI) diffusion-weighted pulse sequences provide a non-invasive tool for estimating such microstructural features in vivo. The current project investigated the use of two methods of analysis, including the ROI-based (Region of Interest, RBA) and voxel-based analysis (VBA), as well as four mathematical models of WM microstructure, including the ActiveAx Frequency-Independent Extra-Axonal Diffusion (AAI), ActiveAx Frequency-Dependent Extra-Axonal Diffusion (AAD), AxCaliber Frequency-Independent Extra-Axonal Diffusion (ACI), and AxCaliber Frequency-Dependent Extra-Axonal Diffusion (ACD) models. Two mice samples imaged at 7 T and 15.2 T were analyzed. Both the AAI and AAD models provide a single value for each of the fit parameters, including mean effective axon diameter AxD ¯ , packing fraction f in , intra-cellular and D in and extra-cellular D ex diffusion coefficients, as well as the frequency dependence of D ex , β ex for the AAD model. The ACI and ACD models provide this, in addition to a distribution of axon diameters for a chosen ROI. VBA extends this, providing a parameter value for each voxel within the selected ROI, at the cost of increased computational load and analysis time. Overall, RBA-ACD and VBA-AAD were found to be optimal for parameter fitting to physically relevant values in a reasonable time frame. A full comparison of each combination of RBA and VBA with AAI, AAD, ACI, and ACD is provided to give the reader sufficient information to make an informed decision of which model is best for their own experiments. [ABSTRACT FROM AUTHOR]

• Equitable global pandemic preparedness requires evidence from all income settings. • We conducted a first-of-a-kind detailed modelling analysis of COVID-19 in a low-income setting. • Infection and excess death rates in Kabwe, Zambia were among the highest in Africa. • Constraints in health care and timely vaccination rollout amplified this burden. • Our methods open up novel avenues to infer pandemic dynamics in low-income settings. Pandemic response in low-income countries (LICs) or settings often suffers from scarce epidemic surveillance and constrained mitigation capacity. The drivers of pandemic burden in such settings, and the impact of limited and delayed interventions remain poorly understood. We analysed COVID-19 seroprevalence and all-cause excess deaths data from the peri-urban district of Kabwe, Zambia between March 2020 and September 2021 with a novel mathematical model. Data encompassed three consecutive waves caused by the wild-type, Beta and Delta variants. Across all three waves, we estimated a high cumulative attack rate, with 78% (95% credible interval [CrI] 71-85) of the population infected, and a high all-cause excess mortality, at 402 (95% CrI 277-473) deaths per 100,000 people. Ambitiously improving health care to a capacity similar to that in high-income settings could have averted up to 46% (95% CrI 41-53) of accrued excess deaths, if implemented from June 2020 onward. An early and accelerated vaccination rollout could have achieved the highest reductions in deaths. Had vaccination started as in some high-income settings in December 2020 and with the same daily capacity (doses per 100 population), up to 68% (95% CrI 64-71) of accrued excess deaths could have been averted. Slower rollouts would have still averted 62% (95% CrI 58-68), 54% (95% CrI 49-61) or 26% (95% CrI 20-38) of excess deaths if matching the average vaccination capacity of upper-middle-, lower-middle- or LICs, respectively. Robust quantitative analyses of pandemic data are of pressing need to inform future global pandemic preparedness commitments. [ABSTRACT FROM AUTHOR]

• A detailed physics-based model of a tunnel kiln is presented. • Model captures gas and solid phase temperatures, fuel usage and product quality. • Optimal temperature set-points obtained for different kiln production rates. • Energy input to the system is minimized without compromising product quality. • Proposed method consistently yields quality products regardless of production rates. This study presents a detailed physics-based model focusing on an industrial tunnel kiln under feedback control. Initially, an empirical sintering model is integrated into the kiln model to accurately predict the product outlet density, a quality-defining parameter reflecting firing process efficacy. Afterwards, the initial steady-state burner valve positions are determined using industrial temperature data. Subsequently, the interactions between gas temperatures and burner valve positions are quantified through the investigation of the Relative Gain Array. A systematic derivation of optimal set-points for different production rates is then carried out, aiming at minimizing energy consumption while meeting end-product quality specifications. The PID controllers are tuned using a dynamic optimization approach, which involves the minimization of integral criteria. Finally, a case study is conducted to evaluate the efficacy of the optimal set-points under varying production rates. The results demonstrate exceptional system response, thus indicating that firing curves should adapt to production rates for consistently producing quality products. [ABSTRACT FROM AUTHOR]

The present study describes a mathematical model of the steady-state photocatalytic degradation of volatile organic compounds (VOCs) on nanotubular TiO 2 layers in a flow-through photoreactor. The experimental setup for gas phase photocatalysis was designed according to the ISO standard (ISO 22197) with a photocatalyst surface area of 50 cm2 and acetaldehyde as a model gaseous pollutant. To the best of our knowledge, a comprehensive model explaining mass transfer inside TiO 2 nanotubes during the VOCs removal has been successfully developed and verified for the first time. The results of the model showed that the photocatalytic degradation of VOCs is not limited by mass transfer inside the TiO 2 nanotubes under the given conditions and the process is controlled only by the kinetics of photocatalytic degradation. This model has the potential to change our approach to designing efficient photocatalytic systems and optimizing the removal of hazardous air pollutants. [Display omitted] [ABSTRACT FROM AUTHOR]

• CEX and AEX were coupled to separate monoclonal antibody (mAb) variants. • In CEX basic variant was isolated, in AEX acidic and main variants were separated. • Mathematical model was used for optimizing the coupled process. • Optimal column coupling and mAb load depended on variant composition. • Optimized loading density was maximum 26 mg ml−1 for CEX, 600 mg ml−1 for AEX. A design procedure for the separation of charge variants of a monoclonal antibody (mAb) was developed, which was based on the coupling of cation-exchange chromatography (CEX) and anion-exchange chromatography (AEX) under high loading conditions. The design of the coupled process was supported by a dynamic model. The model was calibrated on the basis of band profiles of variants determined experimentally for the mAb materials of different variant compositions. The numerical simulations were used to select the coupling configuration and the loading conditions that allowed for efficient separation of the mAb materials into three products enriched with each individual variant: the acidic (av), main (mv) and basic (bv) one. In the CEX section, a two-step pH gradient was used to split the loaded mass of mAb into a weakly bound fraction enriched with av and mv , and a strongly bound fraction containing the bv -rich product. The weakly bound fraction was further processed in the AEX section, where the mv- rich product was eluted in flowthrough, while the av -rich product was collected by a step change in pH. The choice of flow distribution and the number of columns in the CEX and AEX sections depended on the variant composition of the mAb material. For the selected configurations, the optimized mAb loading density in the CEX columns ranged from 10 to 26 mg mL-1, while in the AEX columns it was as high as 300 or 600 mg mL-1, depending on the variant composition of the mAb material. By proper selection of the loading condition, a trade-off between yield and purity of the products could be reached. [ABSTRACT FROM AUTHOR]

Aquaculture policies that decrease the reliance on wild-caught fish and promote integrated multi-trophic aquaculture (IMTA) both aim to lessen the environmental footprint but often overlook economic-environmental trade-offs and neglect social, health, and broad-scale sustainability considerations. Here we present a new bio-physical model (FYNE) that bridges some of these gaps, by linking farm operational choices, such as feed choices and farm siting, with the nutritional value of the farm-gate seafood products and the resulting environmental eutrophication footprint at the farm level. The FYNE model operates both in conditions of monoculture or of IMTA production, and estimates feed composition effects on growth and omega-3 fatty acid content of Atlantic salmon (Salmo salar) and blue mussel (Mytilus edulis) , and nitrogen and carbon concentrations, the latter being a measure of the environmental eutrophication footprint of the farm. The model here developed integrates for the first time (i) an ecosystem model of an IMTA and (ii) a fatty acid dynamics model, with new models for salmon feed digestibility and mussel ingestion. The outputs span (i) production analysis for simulation of biomass; (ii) farm mass-balance analysis on deposition analysis and environmental eutrophication footprint; and (iii) product nutritional quality, in terms of fatty acid content. An application of the model to an IMTA farm in Scotland (UK) demonstrates how fatty acid and environmental changes can be modelled in tandem and could be used to support the industry to optimize the nutritional value of cultured species and manage aquatic environmental eutrophication footprint at farm level, informing feed and siting decisions. The model effectively predicts changes in omega-3 fatty acids, particularly DHA and EPA, in response to variations in feed composition. Notably, our results confirm that salmon waste has limited influence on mussel growth due to the spatial separation within the IMTA system, which emphasizes the need for strategic siting to enhance system productivity through effective waste recycling. Additionally, results show that while increasing fishmeal in diets enhances environmental outcomes, it reduces levels of critical fatty acids like DHA and EPA, underscoring the need for balanced feed formulations. The method developed in this study described a novel integrated model (FYNE) that extend previous modelling work by incorporating the influence of farmed species feed composition on fatty acid dynamics as well as the farming environmental factors. • Salmon and mussel fatty acid dynamics linked to growth models and environment. • Models influence of feed on IMTA species nutritional quality and environment. • Model replicates the changes in salmon PUFA content relative to dietary PUFA content over a growth cycle. • Integration of PUFA models with IMTA ecosystem model. • Models the relationship between diet composition, fatty acid content, and environmental footprint. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two ionic liquids have been investigated for adipic acid extraction. • High adipic acid separation efficiency of 97.55% was obtained within one-step extraction. • The process was modelled and optimized using a neuro-evolutive approach combining ANNs and DE. • A sensibility analysis indicated that IL-type and pH have the highest influence on the process output. Adipic acid (hexanedioic acid, AA) can be utilised as an essential monomer in the production of nylon-6,6 (75 %), but also for urethane foams, fibers, elastomers, tire reinforcements, and synthetic lubricants. Considerable work has been done to develop sustainable biosynthetic processes for its manufacture, especially for adipic acid efficient separation (one of the main challenges for industrialisation). As for scale-up, process optimisation, and thorough engineering mathematical models that describe the system efficiently are required, this study proposes mathematical models for a reactive extraction using ionic liquids as extractants for adipic acid separation to improve the biosynthetic route's sustainability, efficiency, and environmental performance. Heptane as the solvent and 117.8 g/L [P 6,6,6,14 ][Phos] as the extractant at 2.8 pH of the aqueous phase were the optimum mixture for reactive extraction, resulting in an extraction yield of 97.55 % for adipic acid. The process was modeled using an artificial neural network optimised with a differential evolution algorithm. The optimal model structure had one hidden layer with 20 neurons, and its performance in the testing phase was: explained variance score of 0.974, mean absolute error of 1.917, and coefficient of determination of 0.974. [ABSTRACT FROM AUTHOR]

The diamondback moth is one of the world's most destructive pests. Targeting plants such as broccoli and cabbage, they have few natural predators, reproduce effectively, and have been known to establish pesticide resistance over time. In this paper we investigate management of diamondback moth via a bacterial pesticide at a harvesting company. Recognizing evidence of pesticide resistance in the observed data, we quantify this phenomenon mathematically within an age-structured ordinary differential equation model. Significant improvement in the predicted larval population is seen using our model in comparison to an existing model of the same outbreak, in particular in fitting larval growth via incorporation of pesticide resistance. Model predictions of egg and pupal populations are also compared to observed data to gain insight into model accuracy across the life cycle of the diamondback moth. • Mathematical model for response of diamondback moths to biological control. • Age-structured model that incorporates all life stages of the diamond back moth. • Experiment incorporates repeated release of biological control. • Focus on modelling of repeated pesticide release and reduction in efficacy over time. • Establishes a mathematical description of pesticide resistance of diamondback moths. [ABSTRACT FROM AUTHOR]

The perennial fern Pteris vittata is an Arsenic-hyperaccumulator plant able to grow in hydroponic cultures and hence used for phytoremediation of contaminated water. In order to abate the costs linked to the disposal of As contaminated biomass, in this work Pteris vittata waste roots were tested as a new low-cost bio-adsorbent for the removal of Methylene Blue (MB). The latter was selected as a representative cationic dye since its wide usage in industrial applications. The pH of zero charge was evaluated to be 6.2, hence all the adsorption tests were performed at a neutral pH. Isotherms at 20 °C and 40 °C showed a typical Langmuir trend with a maximum adsorption capacity (q max) of 112 mg/g and 154 mg/g respectively. Kinetic tests were also carried out for different solid-liquid ratios and fitted by a mathematical model. The effective diffusivity of MB in the solid was estimated using the maximum likelihood method and the values of 5.99·10−8± 9.6·10−9 cm2/s and 9.56·10−8± 4.5·10−9 cm2/s were obtained at 20 °C and 40 °C respectively. After calculating the Biot number it was found out that both the intra-particle resistance and the external mass transfer resistance are important for the description of the rate of the dye uptake. This paper sets the basis for further investigation of such material in continuous systems in order to verify its feasibility of application in industrial apparatus. • Pteris vittata roots were tested as low-cost adsorbent for Methylene Blue uptake. • Langmuir isotherm was used to describe the system at equilibrium. • The effective diffusivity of Methylene Blue in the roots was estimated. • Intra-particle resistance is the limiting step of adsorption kinetics. [ABSTRACT FROM AUTHOR]

Crimean-Congo haemorrhagic fever (CCHF) is a priority emerging pathogen for which a licensed vaccine is not yet available. We aim to assess the feasibility of conducting phase III vaccine efficacy trials and the role of varying transmission dynamics. We calibrate models of CCHF virus (CCHFV) transmission among livestock and spillover to humans in endemic areas in Afghanistan, Turkey and South Africa. We propose an individual randomised controlled trial targeted to high-risk population, and use the calibrated models to simulate trial cohorts to estimate the minimum necessary number of cases (trial endpoints) to analyse a vaccine with a minimum efficacy of 60%, under different conditions of sample size and follow-up time in the three selected settings. A mean follow-up of 160,000 person-month (75,000–550,000) would be necessary to accrue the required 150 trial endpoints for a target vaccine efficacy of 60 % and clinically defined endpoint, in a setting like Herat, Afghanistan. For Turkey, the same would be achieved with a mean follow-up of 175,000 person-month (50,000–350,000). The results suggest that for South Africa the low endemic transmission levels will not permit achieving the necessary conditions for conducting this trial within a realistic follow-up time. In the scenario of CCHFV vaccine trial designed to capture infection as opposed to clinical case as a trial endpoint, the required person-months is reduced by 70 % to 80 % in Afghanistan and Turkey, and in South Africa, a trial becomes feasible for a large number of person-months of follow-up (>600,000). Increased expected vaccine efficacy > 60 % will reduce the required number of trial endpoints and thus the sample size and follow-time in phase III trials. Underlying endemic transmission levels will play a central role in defining the feasibility of phase III vaccine efficacy trials. Endemic settings in Afghanistan and Turkey offer conditions under which such studies could feasibly be conducted. [ABSTRACT FROM AUTHOR]

• The combined effect of propolis and Origanum compactum essential oil has been evaluated against methicillin resistant S. aureus. • EEP and OCEO showed significant reduction in the growth rate of MRSA, and that as a function of concentration. • The combination of EEP and OCEO showed strong synergistic interaction, noticed by growth rate reduction and confirmed by RSM. • A mathematical model describing the growth of MRSA in presence of EEP and OCEO has been generated. Staphylococcus aureus is a commensal human pathogen responsible of several infectious diseases, especially nosocomial infections. Its rapid spread and ability to resist to environmental conditions make it a serious threat for public health. The aim of this study was to investigate the combined antibacterial effect of propolis extract and Origanum compactum essential (OCEO) against methicillin resistant S. aureus (MRSA), and to build a mathematical model predicting this effect. Propolis and OCEO were collected from the north of Morocco. The minimal inhibitory concentration (MIC) of each product was determined by the microdilution method. The kinetics of MRSA growth was evaluated in absence and in presence of ethanol extract of propolis (EEP) and OCEO, each alone and in combination. The mathematical modelling was made by a polynomial regression. EEP and OCEO showed strong antibacterial activity against MRSA. The MIC of EEP was 0.625 mg/mL and the MIC of OCEO was 1%. The two products showed significant reduction in the growth rate of MRSA, and that as a function of concentration. The combination of EEP and OCEO showed strong synergistic interaction, noticed by growth rate reduction. A mathematical model describing the growth of MRSA in presence of EEP and OCEO was generated. By the negative signs of the coefficients of EEP and OCEO in the linear part, the model confirmed they synergistic interaction. From the obtained results it can be concluded that the combination of propolis extract and O. compactum EO is an interesting approach to control MRSA infections. [ABSTRACT FROM AUTHOR]

[Display omitted] The correlation between in vivo and in vitro data is yet not sufficiently optimized to allow a significant reduction and replacement of animal testing in pharmaceutical development. One of the main reasons for this lies in the poor mechanistic understanding and interpretation of the physical mechanisms enabling formulation rely on for deploying the drug. One mechanism that still lacks a proper interpretation is the kinetics of drug release from nanocarriers. In this work, we investigate two different types of classical enabling formulations – i) cyclodextrin solutions and ii) liposomal dispersions – by a combination of an experimental method (i.e. UV–Vis localized spectroscopy) and mathematical modelling/numerical data fitting. With this approach, we are able to discriminate precisely between the amount of drug bound to nanocarriers or freely dissolved at any time point; in addition, we can precisely estimate the binding and diffusivity constants of all chemical species (free drug/bound drug). The results obtained should serve as the first milestone for the further development of reliable in vitro/in silico models for the prediction of in vivo drug bioavailability when enabling formulations are used. [ABSTRACT FROM AUTHOR]

A vaccine to prevent congenital cytomegalovirus infection (cCMV) is a public health priority. cCMV results from maternal primary or non-primary CMV infection (reinfection, or reactivation of chronic infection) during pregnancy. Young children are a major source of transmission to pregnant women because they shed CMV at high viral loads for prolonged periods. CMV vaccines evaluated in clinical trials so far have demonstrated only approximately 50% efficacy against maternal primary infection. None of these have been approved, as higher levels of vaccine efficacy are assumed to be required to substantially reduce cCMV prevalence. Here, we designed a mathematical model to capture the relationship between viral shedding by young children and maternal CMV infections during pregnancy. Using this model, we were able to quantify the impact of CMV post-infection immunity on protecting against reinfection and viral shedding. There was a 36% reduction in the risk of infection to a seropositive person with post-infection immunity (reinfection) versus a seronegative person without this immunity (primary infection), given the same exposure. Viral shedding following reinfection was only 34% the quantity of that following primary infection. Our model also predicted that a vaccine that confers the equivalent of post-infection immunity, when given to young children, would markedly reduce both CMV transmission to pregnant women and the prevalence of cCMV. Thus, we predict that existing vaccine candidates that have been shown to be only modestly protective may in fact be highly effective at preventing cCMV by interrupting child-to-mother transmission. [ABSTRACT FROM AUTHOR]

[Display omitted] The prediction of drug dissolution profiles is crucial for elucidating the pharmacokinetic behaviour of drugs and the bioavailability of dosage forms. In this work, we develop a mathematical model to describe the dissolution process of irregularly shaped particles. We use a complete dissolution model that accounts for both surface kinetics and convective diffusion. The mechanistic relationship between the mass transfer coefficient and the local curvature is derived from the fundamental physical laws governing these processes. Our model theoretically shows that the dissolution rate depends nonlinearly on the surface curvature. The subsequent recrystallization process in the bulk fluid is also considered. The main result of this work is its simplicity, since only two coupled nonlinear ordinary differential equations are needed to describe the dissolution process. Another remarkable advantage is the possibility to determine the model parameters using common independent techniques, so that the importance of the wettability of solids on the dissolution process can be evaluated. Finally, the proposed model demonstrated the importance of particle shape in describing the experimental dissolution data of theophylline monohydrate. [ABSTRACT FROM AUTHOR]

Over the past two decades, vaccination programmes for vaccine-preventable diseases (VPDs) have expanded across low- and middle-income countries (LMICs). However, the rise of COVID-19 resulted in global disruption to routine immunisation activities. Such disruptions could have a detrimental effect on public health, leading to more deaths from VPDs, particularly without mitigation efforts. Hence, as routine immunisation activities resume, it is important to estimate the effectiveness of different approaches for recovery. We apply an impact extrapolation method developed by the Vaccine Impact Modelling Consortium to estimate the impact of COVID-19-related disruptions with different recovery scenarios for ten VPDs across 112 LMICs. We focus on deaths averted due to routine immunisations occurring in the years 2020–2030 and investigate two recovery scenarios relative to a no-COVID-19 scenario. In the recovery scenarios, we assume a 10% COVID-19-related drop in routine immunisation coverage in the year 2020. We then linearly interpolate coverage to the year 2030 to investigate two routes to recovery, whereby the immunization agenda (IA2030) targets are reached by 2030 or fall short by 10%. We estimate that falling short of the IA2030 targets by 10% leads to 11.26% fewer fully vaccinated persons (FVPs) and 11.34% more deaths over the years 2020–2030 relative to the no-COVID-19 scenario, whereas, reaching the IA2030 targets reduces these proportions to 5% fewer FVPs and 5.22% more deaths. The impact of the disruption varies across the VPDs with diseases where coverage expands drastically in future years facing a smaller detrimental effect. Overall, our results show that drops in routine immunisation coverage could result in more deaths due to VPDs. As the impact of COVID-19-related disruptions is dependent on the vaccination coverage that is achieved over the coming years, the continued efforts of building up coverage and addressing gaps in immunity are vital in the road to recovery. [ABSTRACT FROM AUTHOR]

This paper discussed the relationship between angle and the value PvGRF for the execution of parachute landing. The value of PvGRF has been determined by using mathematical modelling principle of conservation of energy. The mathematical model describes the correlation between peak vertical ground reaction force and drop height. The peak vertical ground reaction force was used to determine the amount of force exerted on the parachutist during landing. A total of 21 parachutists from the Malaysian Army participated in this study comprising of three professional and eighteen amateur parachutists. Two cameras were utilised to capture the movements and thus generate kinematic information throughout the landing phase. The results showed that the estimated maximum peak vertical value of ground reaction force during the foot strike phase for professional and amateur parachutists were 3129 N and 4380 N respectively. Furthermore, the data showed that the professionals bent their hip and knees before their foot strike the ground. From the principle of biomechanics, this action increases the time and drop height during landing and thus reduce the peak vertical ground reaction force. Therefore, the hip and knee movement of the professional parachutist acts as a guideline for amateurs to reduce the impact force during landing. [ABSTRACT FROM AUTHOR]

• A novel hybrid system is proposed with deterministic prediction and interval prediction. • Outlier detection and correction method are used to reduce the complexity of the series. • Data decomposition strategy is applied to decompose the original series to improve the prediction accuracy. • A multi-objective optimization algorithm is selected to determine the original weights and biases of the forecasting model. • Applied interval prediction to quantify the range of concentration change of air pollutants caused by uncertainties. A novel system regarding deterministic and interval predictions of pollutant concentration is constructed in this study, which can not only obtain higher prediction accuracy in deterministic prediction and also provide effective interval prediction of air pollutant concentration. In the deterministic prediction stage, the improved extreme learning machine combines outlier detection and correction algorithm, data decomposition strategy, and a multi-objective optimization algorithm to form a hybrid model for predicting pollutant concentration. Moreover, the applicability of the optimization algorithm was verified from theoretical and experimental analysis. In the interval prediction stage, three distributions are compared to mine, the traits of deterministic prediction errors are analyzed, and interval prediction is designed to quantify the uncertainties associated with pollutant concentration. To investigate the prediction performance of the proposed system, comparison experiments have been executed using the PM 2.5 concentration series from three cities. The results indicate that the system proposed in this paper outperforms comparison models in forecasting accuracy and has advantages for pollution prediction. [ABSTRACT FROM AUTHOR]

Globally, there has been a commitment to produce and distribute a vaccine within 100 days of the next pandemic. This 100-day target will place pressure on countries to make swift decisions on how to optimise vaccine delivery. We used data from the COVID-19 pandemic to inform mathematical modelling of future pandemics in Indonesia for a wide range of pandemic characteristics. We explored the benefits of vaccination programs with different start dates, rollout capacity, and age-specific prioritisation within a year of the detection of a novel pathogen. Early vaccine availability, public uptake of vaccines, and capacity for consistent vaccine delivery were the key factors influencing vaccine benefit. Monitoring age-specific severity will be essential for optimising vaccine benefit. Our study complements existing pathogen-specific pandemic preparedness plans and contributes a tool for the rapid assessment of future threats in Indonesia and similar middle-income countries. • We modelled future pandemics in Indonesia under various pathogen characteristics. • Our model quantified the impact of vaccines available within 100 days. • Early vaccine availability is crucial for determining the impact of vaccines. • Understanding age-specific severity is essential for prioritising vaccines. • Communication to ensure high and consistent uptake of vaccines is important. [ABSTRACT FROM AUTHOR]

Solar energy has experienced a surge in utilization, primarily through the adoption of solar photovoltaic (PV) panels, tapping into its abundant renewable potential. Efficiently cooling the operational surfaces of these PV panels is paramount for optimizing their performance and extending their lifespan. Cooling not only boosts electrical efficiency but also mitigates cell degradation. Temperature plays a crucial role in panel efficiency, particularly when temperatures exceed 25 °C, highlighting the need for effective cooling methods. Previous research has struggled to optimize control without relying on traditional cooling mediums. One promising approach involves attaching thermoelectric coolers (TECs) to the rear of PV panels, effectively reducing surface temperatures. To evaluate the economic viability of this approach, a comparative analysis is conducted between conventional systems and PV-TEC hybrid systems. A mathematical model describing the cooling process of PV modules is formulated, and simulations are carried out using MATLAB and the SCIP (Solving Constrained Integer Programs) in OPTI toolbox. The outcomes illustrate that precise control of TECs can increase the electrical output power efficiency of PV modules by 9.27 % while efficiently regulating surface temperatures. Furthermore, life cycle cost comparisons reveal that the hybrid system may be more cost-effective, boasting a 10.56 % cost saving over a 20-year project lifespan. A break-even point analysis indicates that the proposed system may break even in 6.5 years, demonstrating its potential for long-term economic benefits. This research underscores the significance of thermoelectric cooling in enhancing both performance and economic viability in solar PV systems. • A model of a photovoltaic system connected to thermoelectric cooling device is developed. • Optimal operation control of the thermoelectric is used to cool the Photovoltaic system. • A potential increase of the electrical output power efficiency of photovoltaic modules of 9.27 % is observed. • The proposed system with optimal control would break even in 6.5 years, compared to the baseline without cooling. [ABSTRACT FROM AUTHOR]

• Modelling approaches of GHG emissions from WRRFs are comprehensively summarized. • Mechanistic models fail to capture variability but could benefit from hybridization. • ML can be used for GHG emissions monitoring but needs further investigation. • Different types of hybrid models are compared. • Further monitoring campaigns are required for CH 4 emissions. In light of the historic Paris Agreement at the UN Climate Change Conference aimed at combating global warming, there has been increased momentum to quantify and mitigate greenhouse gas (GHG) emissions from Water Resources Recovery Facilities (WRRFs). However, the current methodologies for estimating GHG emissions from WRRFs are fraught with high degrees of uncertainty. To address this, a range of modelling approaches has been employed to estimate GHG emissions, specifically nitrous oxide (N 2 O) and methane (CH 4), and to optimize and mitigate such emissions through linking operational processes. This article conducts a thorough and critical examination of GHG emissions modelling efforts in WRRFs, covering mechanistic, data-driven, and hybrid models for N 2 O and CH 4 , alongside empirical, steady-state, and dynamic plant-wide models. It emphasizes the applicability and limitations of these methods in full-scale applications, highlighting the calibration complexities of mechanistic models and the limited explainability of data-driven tools. The review also discusses innovative emerging approaches, such as hybrid modelling and knowledge-based AI, and stresses the necessity for novel, model-aided strategies to quantify and monitor fugitive methane emissions effectively. By elucidating knowledge gaps, addressing literature discrepancies, and reviewing diverse modelling methodologies, this article significantly enhances the current understanding of GHG modelling in WRRFs, paving the way for more sustainable and environmentally responsible wastewater management practices. [ABSTRACT FROM AUTHOR]

• In contrast to gas-phase systems, the theory and practice of determining effective diffusion coefficients in porous solid materials immersed in liquids is a challenge. • A method for the termination of effective diffusion coefficients packed beds was proposed. • Experiments were carried out with steps response technique to reveal the role of diffusion, adsorption and dispersion in packed beds. • A mathematical model was proposed and verified with experimental data. Many industrially relevant chemical processes are affected by the liquid-phase diffusion resistance in porous materials, for example in heterogeneous catalysts. Therefore, reliable experimental methods for the determination of effective diffusion coefficients in porous structures are highly desirable. In this work, a method was developed to study the internal diffusion effects in porous media by step response experiments with tracers injected to fixed beds filled with porous particles and inert material. The tracer responses were recorded at the outlet of the bed by UV–vis spectrometry. Ethanol was used as the tracer and water as the solvent. Porous aluminum oxide particles were selected as the model material. The step responses were described with a dynamic axial dispersion model, i.e. partial differential equations for the inert and catalyst sections of the bed. The partial differential equations describing the model were discretized with finite differences and the resulting ordinary differential equations were solved numerically with a solver for stiff ordinary differential equations. From the experimental data, the dispersion coefficients of the bed and the effective diffusion coefficient in the porous aluminum oxide particles were successfully determined by non-linear regression analysis. The values of the effective diffusion coefficents had a good agreement with the liquid-phase diffusion coefficients estimated from correlations. The method is applicable for any porous material. [ABSTRACT FROM AUTHOR]

[Display omitted] • Exploration of UAE parameters impact on longan seeds properties. • Identification of varied optimal conditions for extraction. • Development of predictive models for longan seeds properties. • Thermodynamic analysis reveals process characteristics. • Insights for efficient extraction processes with industrial applications. Investigating the extraction of bioactive compounds represents a hopeful direction for maximizing the value of longan fruit byproducts. This study explored the influence of ultrasonic-assisted extraction (UAE) parameters—specifically ultrasonic power ratios, temperatures, and exposure times—utilizing water as a green solvent on several properties of the longan seeds extract (LSE). These properties encompassed the energy consumption of the UAE process (EC), extraction yield (EY), total phenolic contents (TPC), total flavonoid contents (TFC), and antioxidant activity (DPPH). Additionally, the study sought to optimize the conditions of UAE process and examine its thermodynamic properties. A three-level, three-factor full factorial design was utilized to assess the effects of different factors on LSE properties. Results indicated that EC, EY, TPC, TFC, and DPPH were significantly influenced by power ratios, temperatures, and exposure time. Moreover, the proposed models effectively characterized the variations in different properties during the extraction process. The optimized extraction conditions, aimed at minimizing EC while maximizing EY, TPC, TFC, and DPPH radical scavenging activity, were demonstrated as an ultrasonic power ratio of 44.4 %, a temperature of 60 °C, and an extraction time of 17.7 min. Optimization led to 563 kJ for EC, 7.85 % for EY, 47.21 mg GAE/mL for TPC, 96.8 mg QE/mL for TFC, and 50.15 % for DPPH radical scavenging activity. The results emphasized that the UAE process exhibited characteristics of endothermicity and spontaneity. The results provide valuable insights that could inform the enhancement of extraction processes, potentially benefiting industrial utilization and pharmaceutical formulations. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) is a viral disease that affects domestic and feral pigs. While not currently present in Australia, ASF outbreaks have been reported nearby in Indonesia, Timor-Leste, and Papua New Guinea. Feral pigs are found in all Australian states and territories and are distributed in a variety of habitats. To investigate the impacts of an ASF introduction event in Australia, we used a stochastic network-based metapopulation feral pig model to simulate ASF outbreaks in different regions of Australia. Outbreak intensity and persistence in feral pig populations was governed by local pig recruitment rates, population size, carcass decay period, and, if applicable, metapopulation topology. In Northern Australia, the carcass decay period was too short for prolonged persistence, while endemic transmission could possibly occur in cooler southern areas. Populations in Macquarie Marshes in New South Wales and in Namadgi National Park in the Australian Capital Territory had the highest rates of persistence. The regions had different modes of transmission that led to long-term persistence. Endemic Macquarie Marshes simulations were characterised by rapid transmission caused by high population density that required a fragmented metapopulation to act as a bottleneck to slow transmission. Endemic simulations in Namadgi, with low density and relatively slow transmission, relied on large, well-connected populations coupled with long carcass decay times. Despite the potential for endemic transmission, both settings required potentially unlikely population sizes and dynamics for prolonged disease survival. • Built a stochastic metapopulation ASF model with feral pig social structure. • Investigated ASF introduction in different Australian regions. • Persistence in Northern Australia unlikely due to reduced decay times. • Increased likelihood of endemic transmission in southern regions. • Southern feral pig population structure likely inhibits endemic transmission. [ABSTRACT FROM AUTHOR]