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Objectives: 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. Methods: 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. Results: 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. Conclusions: Robust quantitative analyses of pandemic data are of pressing need to inform future global pandemic preparedness commitments.

In this study, mathematical model of advanced exergoeconomic analysis (ADEXEC) basing on advanced specific exergy cost (ADSPECO) and advanced exergy-cost-energy-mass (EXCEM) (ADEXCEM) methods are applied to the industrial cogeneration system (COGEN) which consists of natural gas fired gas turbine unit (with combustion chamber (CC), turbine (GT) and air compressor (AC)), pipe line (PL), wall (WD) and ground (GD) tile dryers under the dead state conditions of 10 °C, 15 °C, 20 °C, 25 °C and 30 °C. According to ADSPECO analysis; although CC ($6.97/GJ) has the lowest unit fuel exergy cost among the components, it has the highest exergy destruction cost rate ($252,391/hour at 30 °C) due to its high exergy destruction value. Although more than 40 % of the exergy destruction cost rate in the COGEN system is detected in CC, WD (105.541 $/h at 30 °C) and GD (107.499 $/h at 30 °C) which together account for more than 67 % of COGEN's avoidable exergy destruction are understood to be the first components to focus on. The fact that the exergy destruction cost rates of all components except PL is higher than the exogenous part of the endogenous part at all dead state temperatures reveals the weakness of the relationship between the components.

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

This article describes the non-linear reaction-diffusion model, which depicts the behaviour of hydrogen peroxide production and glucose oxidation in the chitosan-alginate microsphere. Analytical solutions of glucose, oxygen, gluconic acid and hydrogen peroxide concentrations in planar coordinates under steady-state circumstances are obtained for all reaction-diffusion parameters. The non-linear boundary value problem's approximate analytical expressions are obtained using asymptotic techniques based on the homotopy perturbation and Akbari-Ganji method. The exact and commonly used MATLAB program provided a numerical simulation. It is demonstrated that the resulting analytical expressions strongly agree with the numerical outcomes reported in the literature. Furthermore, determining hydrogen peroxide flux and pH profiles within the microspheres are also discussed. The theoretical findings make it possible to forecast and enhance the efficiency of enzyme kinetics.

Whilst automated analysis of immunostains in pathology research has focused predominantly on the epithelial compartment, automated analysis of stains in the stromal compartment is challenging and therefore requires time-consuming pathological input and guidance to adjust to tissue morphometry as perceived by pathologists. This study aimed to develop a robust method to automate stromal stain analyses using 2 of the commonest stromal stains (SMA and desmin) employed in clinical pathology practice as examples. An effective computational method capable of automatically assessing and quantifying tumour-associated stromal stains was developed and applied on cores of colorectal cancer tissue microarrays. The methodology combines both mathematical models and deep learning techniques with the former requiring no training data and the latter as many inputs as possible. The novel mathematical model was used to produce a digital double marker overlay allowing for fast automated digital multiplex analysis of stromal stains. The results show that deep learning methodologies in combination with mathematical modelling allow for an accurate means of quantifying stromal stains whilst also opening up new possibilities of digital multiplex analyses.

Muconic acid is a six-carbon dicarboxylic acid with conjugated double bonds that finds extensive use in the food (additive), chemical (production of adipic acid, monomer for functional resins and bio-plastics), and pharmaceutical sectors. The biosynthesis of muconic acid has been the subject of recent industrial and scientific attention. However, because of its low concentration in aqueous solutions and high purity requirement, downstream separation presents a significant problem. Artificial Neural Networks and Differential Evolution were used to optimize process parameters for the recovery of muconic acid from aqueous streams in a system with n-heptane as an organic diluent and ionic liquids as extractants. The system using 120 g/L tri-hexyl-tetra-decyl-phosphonium decanoate dissolved in n-heptane, pH of the aqueous phase 3, 20 min contact time, and 45 °C temperature assured a muconic acid extraction efficiency of 99,24 %. Low stripping efficiency compared to extraction efficiency was observed for the optimum conditions on the extraction step (120 g/L ionic liquids dissolved in heptane). However, re-extraction efficiencies obtained for the recycled organic phase in three consecutive stages were close to the first extraction stage. The mechanism analysis proved that the analysed phosphonium ionic liquids (PILSs) extracts only undissociated molecules of muconic acid through H-bonding.

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.

CO2 photocatalytic reduction is a potential and promising technology to reduce the level of the greenhouse gas in the atmosphere but also as an alternative and renewable fuel resource. However, the products yield of the reaction is still low and the identification of the optimal operating conditions that affect the process are still needed to be determined. This study investigates the impact of key operational parameters, specifically photocatalyst concentration and stirring speed, on the photocatalytic reduction of CO2 in a slurry batch photoreactor utilizing synthesized TiO2. A simplified photocatalytic kinetic model, incorporating the radiation field within the photoreactor, was developed, considering mass transfer from liquid to gas phase for the primary detected reaction products (CO, CH4, and H2). The proposed models elucidate the influence of different operating conditions on product yields. Stirring speed, controlled by a magnetic stirrer, impacts the gas–liquid mass transfer rate. Increased liquid phase stirring speed ensures faster species transport to the gas phase, with a diminishing effect beyond 900 rpm. TiO2 photocatalyst mass concentration influences the available total active surface and irradiation absorbance in the photoreactor volume. Optimal product yields were observed at the lowest tested photocatalyst concentration (0.5 g · L-1), indicating improved irradiation distribution and reduced particle agglomeration, resulting in higher available active surface for the reaction. The calculation model successfully predicted product yields even with lower photocatalyst concentration of 0.25 g · L-1, with marginal increases in predicted yields. These findings provide valuable insights for scaling up and optimizing the CO2 photocatalytic reduction process, offering a foundation for future research.

Background: Vietnam continues to have measles and rubella outbreaks following supplementary immunization activities (SIA) and routine immunization despite both having high reported coverage. To evaluate immunization activities, age-specific immunity against measles and rubella, and the number of averted Congenital Rubella Syndrome (CRS) cases, must be estimated. Methods: Dried blood spots were collected from 2091 randomly selected individuals aged 1-39 years. Measles and rubella virus-specific immunoglobulin G (IgG) were measured by enzyme immunoassay. Results were considered positive at ≥120 mIU/mL for measles and ≥10 IU/mL for rubella. The number of CRS cases averted by immunization since 2014 were estimated using mathematical modelling. Results: Overall IgG seroprevalence was 99.7% (95%CI: 99.2-99.9) for measles and 83.6% (95%CI: 79.3-87.1) for rubella. Rubella IgG seroprevalence was higher among age groups targeted in the SIA than in non-targeted young adults (95.4% [95%CI: 92.9-97.0] vs 72.4% [95%CI: 63.1-80.1]; P < 0.001). The estimated number of CRS cases averted in 2019 by immunization activities since 2014 ranged from 126 (95%CI: 0-460) to 883 (95%CI: 0-2271) depending on the assumed postvaccination reduction in the force of infection. Conclusions: The results suggest the SIA was effective, while young adults born before 1998 who remain unprotected for rubella require further vaccination.

Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019–20 and 2022–23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.

Staphylococcus aureus (SA) colonizes and can damage skin in atopic dermatitis lesions, despite being commonly found with Staphylococcus epidermidis (SE), a commensal that can inhibit SA’s virulence and kill SA. In this study, we developed an in silico model, termed a virtual skin site, describing the dynamic interplay between SA, SE, and the skin barrier in atopic dermatitis lesions to investigate the mechanisms driving skin damage by SA and SE. We generated 106 virtual skin sites by varying model parameters to represent different skin physiologies and bacterial properties. In silico analysis revealed that virtual skin sites with no skin damage in the model were characterized by parameters representing stronger SA and SE growth attenuation than those with skin damage. This inspired an in silico treatment strategy combining SA-killing with an enhanced SA–SE growth attenuation, which was found through simulations to recover many more damaged virtual skin sites to a non-damaged state, compared with SA-killing alone. This study demonstrates that in silico modelling can help elucidate the key factors driving skin damage caused by SA–SE colonization in atopic dermatitis lesions and help propose strategies to control it, which we envision will contribute to the design of promising treatments for clinical studies.

The angiotensin-converting enzyme (ACE) gene (ACE) insertion/deletion (I/D) polymorphism raises the possibility of personalising ACE inhibitor therapy to optimise its efficiency and reduce side effects in genetically distinct subgroups. However, the extent of its influence among these subgroups is unknown. Therefore, we extended our computational model of blood pressure regulation to investigate the effect of the ACE I/D polymorphism on haemodynamic parameters in humans undergoing antihypertensive therapy. The model showed that the dependence of blood pressure on serum ACE activity is a function of saturation and therefore, the lack of association between ACE I/D and blood pressure levels may be due to high ACE activity in specific populations. Additionally, in an extended model simulating the effects of different classes of antihypertensive drugs, we explored the relationship between ACE I/D and the efficacy of inhibitors of the renin–angiotensin–aldosterone system. The model predicted that the response of cardiovascular and renal parameters to treatment directly depends on ACE activity. However, significant differences in parameter changes were observed only between groups with high and low ACE levels, while different ACE I/D genotypes within the same group had similar changes in absolute values. We conclude that a single genetic variant is responsible for only a small fraction of heredity in treatment success and its predictive value is limited.

Summary: Background: Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown. Methods: We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method. Findings: The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms. Interpretation: Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively. Funding: Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.

The production of therapeutic proteins in mammalian cell culture is an essential unit operation in biopharmaceutical manufacture that can benefit from the predictive insights of effective process models, leading to accelerated process development and improved process control. This review outlines and evaluates current approaches to predictive model development for mammalian cell culture and protein production. Classical mechanistic and data driven approaches are analysed, together with potential challenges in model development and application, including the experimental requirements for parameter estimation. Hybrid models, which may offer greater robustness, are then explored along with hybrid model architecture and the steps involved in model development. Successful examples from other cell fermentation processes are also considered, for application to the development, monitoring and control of mammalian processes.

Several studies demonstrated novel and effective design solutions for the FPC thermal performance enchantment by the front side heat loss reduction. However, these design improvements were tested under different climatic and operating conditions. This fact makes it almost impossible to compare these design improvements. The following study quantitatively analyses the current flat-plate solar collector design developments, focusing on reducing front-side heat loss. It also performs a simulation analysis to demonstrate the thermal performance improvement of the chosen design solutions for three specific applications. The literature review was performed to identify the most promising design solutions. Then, the considered design solutions were modelled using the validated detailed theoretical model of a flat-plate solar collector (FPC). Finally, the modelling results were used to perform the thermal performance simulation analysis for Würzburg (Germany) climatic conditions and constant mean fluid operating temperatures of 50, 75, and 100 °C. The simulation results indicated that applying the considered design solutions to decrease the front-side heat losses could significantly increase the FPC thermal performance. Moreover, for high-temperature applications, where the front-side heat loss is a crucial factor determining the FPC thermal performance, there is more than a significant potential for the FPC thermal performance enchantment. To sum up, our findings indicate a significant thermal performance improvement potential that could increase the application range of FPCs and make them more competitive for a broader application area.

The paper presents a theoretical analysis performed for unglazed liquid solar thermal collectors (USTCs) to demonstrate the magnitude of the thermal performance uncertainty caused by the different wind conditions under the same wind speed. The literature currently comprises more than 90 combined convection heat transfer correlations. The difference between them is primarily caused by the difference in wind characteristics during the experiment (wind turbulence level, wind direction, etc.). A detailed validated theoretical model of a USTC and forty applicable combined convection heat transfer correlations were used to perform the analysis to demonstrate the magnitude of the thermal performance uncertainty caused by the different wind conditions. It was found that, for the low operating temperature, the uncertainties caused by the different wind conditions are ± 4 %, ± 6 %, and ± 8.5 % under wind speeds of 0 m/s, 1.5 m/s, and 3 m/s, respectively. These values are relatively small compared to the obtained average thermal performance values of 78 %, 73 %, and 65 %. In contrast, the uncertainty values are significant for relatively high operation temperatures: ± 11.5 %, ± 16.5 %, and ± 22 %, while the modelled efficiency values for these operating conditions are 15 %, 30 %, and 44 %. The analysis demonstrated that the difference in wind characteristics under the same wind speed significantly affects a USTC's thermal performance. From a practical standpoint, the USTC's thermal performance in real wind conditions may exhibit significant disparities compared to its performance tested in controlled laboratory conditions under the same wind speed. Furthermore, notably varied thermal performance can be expected for identical USTCs installed in different locations under the same wind speed due to the different wind characteristics (wind turbulence level, wind direction, etc.).

Summary: Background: The unprecedented global outbreak of mpox in 2022 posed a public health challenge. In addition to the mpox vaccine campaign in the United States (US), community organisations and public health agencies initiated educational efforts to promote sexual risk reduction. This modelling study estimated the impact of the two-dose vaccination campaign and sexual behaviour changes coincident with high-risk group awareness on the mpox epidemic in the US. Methods: We fitted a deterministic, risk-structured SEIARV model to the epidemic curve of reported mpox cases in the US between May 22, 2022 and December 22, 2022. We evaluated the putative effects of the two preventive responses in the US -- vaccination and sexual risk reduction -- at the population-level, by calculating the prevention percentages of cumulative cases compared to the counterfactual scenario without interventions. We performed sensitivity analyses with four parameters: case reporting fidelity, vaccine effectiveness, proportion of asymptomatic cases, and assortative mixing. Findings: Model fitting revealed a basic reproduction number of 3.88 and 0.39 for the high-risk and low-risk populations, respectively, with 71.8% of mpox cases estimated from the high-risk population. A two-dose vaccination campaign, solely, could prevent 21.2% (10.2%–24.1%) of cases, while behaviour changes due to high-risk group awareness alone could prevent 15.4% (14.3%–20.6%). The combination of both measures were synergistic, with the model suggesting that 64.0% (43.8%–69.0%) of US cases were averted that would have otherwise occurred. Interpretation: Our models suggest that the 2022–2023 mpox epidemic in the US was controlled by a combination of two-dose mpox vaccination campaign and high-risk group awareness and sexual risk reduction. Funding: Taiwan Ministry of Education grant #NTU-112L9004, Taiwan National Science and Technology Council grant #MOST-109-2314-B-002-147-MY3 and grant #NSC-112-2314-B-002-216-MY3. SHV was supported, in part, by US National Institutes of Health grant #P30MH062294.

Our experimental investigations show that rate of copper anodes degradation decreases during electrolysis in concentrated NaCl solution in water (5 mol/kg or 23 %, freezing point equals −22°C, pH 6.5–7.5) at electric current density about 3000 A/m2 (or 30 A/dm2 = 3 mA/mm2) with temperature increasing from room temperature to 100 °C. Electric current value also increases with temperature increasing. Really, such result is unexpected. General quantity of the H+ and Cl− ions decreases during electrolysis at all temperatures since the H2 and Cl2 gases are formed near electrodes. General quantity of the Cu+ and Cu2+ ions decreases with temperature increasing too. We guess that one reason only should be for electric current value increasing: average charge of copper ions increases from +1 at room temperature to +1.5 at 100 °C and to +2 at 180°C. Corresponding mathematical model is proposed for the analysis. Literature experimental data are used too.

This research builds a mathematical modelling to assess food waste production when designing sustainable packaging solutions integrated with an agricultural intervention in kale production. The model utilizes experimental data obtained from simulated retail and distribution storage conditions to assess the probability of the product to be found out of technical specification and becoming waste. The packaging design was made using a system of differential equations describing the gas exchanges inside the packaging. The waste was estimated fitting linear mixed effect models to the postharvest experimental data, accounting for the variability between and within groups. A field experiment with kale treated with silicon during growth as a bio stimulant was used with the aim to make the product more resilient to packaging conditions. The Kale was then packaged in polylactic acid and oriented polypropylene for postharvest testing. Technological thresholds that indicate out-of-specification product were used to estimate the percentage of product that would likely end up as food waste. In total 7.2% of the product was found to be out of specification with the PLA film after 7 days. Silicon treatment was able to reduce this value to negligible, demonstrating the ability of agricultural interventions to facilitate sustainable packaging and reducing food waste in horticultural products.

Introduction: Electrical-equivalence mathematical models that integrate vascular and cerebrospinal fluid (CSF) compartments perform well in simulations of dynamic cerebrovascular variations and their transient effects on intracranial pressure (ICP). However, ICP changes due to sustained vascular diameter changes have not been comprehensively examined. We hypothesise that changes in cerebrovascular resistance (CVR) alter the resistance of the bulk flow of interstitial fluid (ISF). Research question: We hypothesise that changes in CVR alter the resistance of the bulk flow of ISF, thus allowing simulations of ICP in response to sustained vascular diameter changes. Material and methods: A lumped parameter model with vascular and CSF compartments was constructed and converted into an electrical analogue. The flow and pressure responses to transient hyperaemic response test (THRT) and CSF infusion test (IT) were observed. Arterial blood pressure (ABP) was manipulated to simulate ICP plateau waves. The experiments were repeated with a modified model that included the ISF compartment. Results: Simulations of the THRT produced identical cerebral blood flow (CBF) responses. ICP generated by the new model reacted in a similar manner as the original model during ITs. Plateau pressure reached during ITs was however higher in the ISF model. Only the latter was successful in simulating the onset of ICP plateau waves in response to selective blood pressure manipulations. Discussion and conclusion: Our simulations highlighted the importance of including the ISF compartment, which provides mechanism explaining sustained haemodynamic influences on ICP. Consideration of such interactions enables accurate simulations of the cerebrovascular effects on ICP.

Background and purpose: Interfraction motion during cervical cancer radiotherapy is substantial in some patients, minimal in others. Non-adaptive plans may miss the target and/or unnecessarily irradiate normal tissue. Adaptive radiotherapy leads to superior dose-volume metrics but is resource-intensive. The aim of this study was to predict target motion, enabling patient selection and efficient resource allocation. Materials and methods: Forty cervical cancer patients had CT with full-bladder (CT-FB) and empty-bladder (CT-EB) at planning, and daily cone-beam CTs (CBCTs). The low-risk clinical target volume (CTVLR) was contoured. Mean coverage of the daily CTVLR by the CT-FB CTVLR was calculated for each patient. Eighty-three investigated variables included measures of organ geometry, patient, tumour and treatment characteristics. Models were trained on 29 patients (171 fractions). The Two-CT multivariate model could use all available data. The Single-CT multivariate model excluded data from the CT-EB. A univariate model was trained using the distance moved by the uterine fundus tip between CTs, the only method of patient selection found in published cervix plan-of-the-day studies. Models were tested on 11 patients (68 fractions). Accuracy in predicting mean coverage was reported as mean absolute error (MAE), mean squared error (MSE) and R2. Results: The Two-CT model was based upon rectal volume, dice similarity coefficient between CT-FB and CT-EB CTVLR, and uterine thickness. The Single-CT model was based upon rectal volume, uterine thickness and tumour size. Both performed better than the univariate model in predicting mean coverage (MAE 7 %, 7 % and 8 %; MSE 82 %2, 65 %2, 110 %2; R2 0.2, 0.4, −0.1). Conclusion: Uterocervix motion is complex and multifactorial. We present two multivariate models which predicted motion with reasonable accuracy using pre-treatment information, and outperformed the only published method.

This study is based on an investigation of the transport phenomenon, specifically the quantification of arsenic diffusion in carrots within a temperature range of 89 °C–99 °C using a thin plate model. Studying the diffusion of arsenic in carrots is important due to its toxicity, as it can concentrate during cooking. The World Health Organization considers arsenic as one of the ten chemical substances of public health concern. In this study, biennial hybrid carrots of the Nantesa variety were cooked whole with their epidermis in an aqueous solution containing diarsenic trioxide with an As concentration of 5 mgL−1 at 89 °C, 94 °C, and 99 °C. The cooking times of the carrots at different temperatures were based on a specific degree of tenderness, with a value of ≤3 kg m−2. The evaluated data showed consistency with increasing temperature. The calculated effective diffusion coefficients at temperatures of 89 °C, 94 °C, and 99 °C were 5.84E-09 m2s-1, 1.08E-08 m2s-1, and 2.51E-08 m2s-1 for the flesh (DL), and 1.601E-11 m2s-1, 2.15E-11 m2s-1, and 4.39E-11 m2s-1 for the epidermis (DE), respectively. The activation energy for diffusion was determined to be 159.54 kJmol−1 for the and 110.68 kJmol−1 for the epidermis. Similar behaviours were observed in different radial positions of the carrot, where the arsenic content decreased from the periphery to the centre, consistent with studies on diffusion phenomena with other solutes in food. The novelty was the detailed quantification of arsenic diffusion in the Nantes-type hybrid carrot matrix. This study is limited to a specific concentration of 5 mgL−1 of arsenic solution. The findings of this study may have significant implications for public health and food safety.

The integration of microalgae cultivation in anaerobic digestion (AD) plants can take advantage of relevant nutrients (ammonium and ortho-phosphate) and CO2 loads. The proposed scheme of microalgae integration in existing biogas plants aims at producing approximately 250 t·y−1 of microalgal biomass, targeting the biostimulants market that is currently under rapid expansion. A full-scale biorefinery was designed to treat 50 kt·y−1 of raw liquid digestate from AD and 0.45 kt·y−1 of CO2 from biogas upgrading, and 0.40 kt·y−1 of sugar-rich solid by-products from a local confectionery industry. An innovative three-stage cultivation process was designed, modelled, and verified, including: i) microalgae inoculation in tubular PBRs to select the desired algal strains, ii) microalgae cultivation in raceway ponds under greenhouses, and iii) heterotrophic microalgae cultivation in fermenters. A detailed economic assessment of the proposed biorefinery allowed to compute a biomass production cost of 2.8 ± 0.3 €·kg DW−1, that is compatible with current downstream process costs to produce biostimulants, suggesting that the proposed nutrient recovery route is feasible from the technical and economic perspective. Based on the case study analysis, a discussion of process, bioproducts and policy barriers that currently hinder the development of microalgae-based biorefineries is presented.

An experimental method to calculate average charge of metal ions by electrolysis at different temperatures is proposed. Aluminium undergoes dissolution to the Al3+ ions at all temperatures. Iron undergoes dissolution to the Fe2+ or the Fe3+ ions and copper undergoes dissolution to the Cu+ or the Cu2+. It depends on temperature and electric current density. Direct electric current value and anode mass decreasing were measured during electrolysis into concentrated NaCl solution in water (5 mol/kg or 23.1 %, freezing point equals −22 °C, pH 6.5–7.5) at room temperature and 100 °C. The average charges of copper, iron, and aluminium ions were calculated using Faraday's law of electrolysis at electric current density 3000 A/m2 (or 30 A/dm2 = 3 mA/mm2): +3 for aluminium; +2 for iron; and +1 for copper at room temperature, and +3 for aluminium; +2 for iron; and +1.5 for copper at temperature 100 °C. The main condition was zAl = 3. We concluded that calculations of the average metal ions charges, zFe and zCu, were correct since zAl = 3. The result is as follows: the Al3+, the Fe2+, and the Cu+ ions dissolve into concentrated NaCl solution in water at room temperature; the Al3+, the Fe2+, the Cu+ and the Cu2+ ions (50 %/50 %) dissolve into the solution at temperature 100 °C. We have obtained experimentally and by mathematical modelling that aluminium anodes (cylindrical or spherical) dissolve into the solution more rapidly with temperature increasing during electrolysis accordingly to the Arrhenius law, while copper anodes (cylindrical or spherical) dissolve more slowly with temperature increasing from room temperature to temperature 180 °C like “inverse Arrhenius law”. Iron electrochemical corrosion rate practically does not depend on temperature below 100 °C (and, obviously, up to 180 °C) like “zeroth Arrhenius law”. The spherical anode effect is practically the same as the cylindrical anode effect. The galvanic current dependence on ratio of cathodic surface area to anodic surface area in the system Fe-chromium (Cr) stainless steel and the time of the iron anodes dissolution in motionless electrolyte and in moving electrolyte are also analysed using literature experimental data.

Background/Purpose: A dental implant is an orthodontic prosthesis inserted into the patient's jawbone by a surgical procedure to compensate for a lost tooth. Upon osseointegration (bone-implant fusion), the abutment and crown can be attached to the implant to complete the implantation process. To guarantee the successful implantation of dental prosthetics, it's vital to monitor the primary stability in micro-displacement. This research aims to create a two-degree-of-freedom (2 DOF) mathematical model of dental implant prosthetics, which will be used to evaluate the micro-displacement under electromagnetic excitation. This mathematical model development will verify the functionality and accuracy of the electromagnetic RFA technique by estimating the resonance frequency and associated micro frequency of dental implants. Methods: Dental implant systems are evaluated according to the frequency of vibration corresponding to the maximum micro-displacement (micro-mobility). The stiffness and damping values of the mathematical model were determined using Finite Element Analysis (FEA). Using concepts and equations of vibration, the maximum displacement of the implant is determined. Results: Variations in input frequency ranges have also been compared to observe the micro displacement and resonance frequencies. In MATLAB, an input frequency of 5 to 15 kHz was provided. The maximum micro displacements and the resonance frequencies were plotted in the time domain and frequency domain using Fast Fourier Transform (FFT) in MATLAB. Conclusions: The findings of this research reveal that input frequency ranges of 5 to 15 kHz can be used to determine the resonance frequencies of a dental implant and the corresponding micro-displacements. there is a need in the medical field for a non-invasive device with a 2 DOF modeling concept that can detect even small changes in implant stability which ultimately determines both the short- and long-term clinical effectiveness of implant treatment.

The invasion of vector-borne diseases depends on the type of specific features of the vector and hosts at play. Within the Culex pipiens complex, differences in ecology, biology, and vector competence can influence the risk of West Nile virus (WNV) outbreaks. To determine which life-history traits affect WNV invasion into susceptible communities the most, we constructed an epidemiological Susceptible-Exposed-Infectious-Recovered model with three vector (eco)types, Culex pipiens pipiens, Cx. pip. molestus, and their hybrids, and two vertebrate hosts, birds (as amplifying hosts) and humans (as dead-end hosts). We investigated how differences in feeding preferences and transmission rates influenced WNV transmission across different habitats and two seasons (Spring versus Summer), to investigate the impact of increasing mosquitoes on the WNV transmission risk. Our results showed that vector feeding preferences and the transmission rate between mosquitoes and birds were the parameters that most influenced WNV invasion risk. Even though our model did not predict WNV invasion across any of the studied environments, we found that natural habitats displayed the highest susceptibility to WNV invasion. Pipiens (eco)type acted as the primary vector in all habitats. Hybrids, contrary to common opinion, showed minimal involvement in WNV transmission. However, it is important to interpret our study results with caution due to the possibility of idealized spring and summer seasons being reflected in the field-collected data. Our study could be a tool to enhance current vector surveillance and control programs by targeting specific vector types in specific environments, especially in natural habitat, which are most responsive to environmental shifts. The joint approach based on epidemiological modelling based on field collected data can help to reduce wasted time and economic costs while maximizing the efficiency of local public health authorities.

Continued efforts to reduce the burden of COVID-19 require the consideration of additional booster doses and emerging oral antivirals. This study explored the individual- and population-level impacts of booster dose and oral antivirals in Indonesia, Fiji, Papua New Guinea, and Timor-Leste. Our mathematical model included age structure, vaccine coverage, prevalence of comorbidities, and immunity from prior infection fit to incidence data from our study settings. We explored a range of eligibility criteria and found that boosters had the largest impact per dose when prioritised to high-risk adults and adults who had not previously received a booster. Antivirals were most effective in settings with low vaccine-derived immunity. In general, fewer antivirals than booster doses were required to prevent a hospitalisation or death. Only in settings with very high vaccine uptake was the impact per dose of providing booster doses to high-risk adults comparable to providing oral antivirals to high-risk adults. Together, booster doses and oral antivirals could prevent 80%, 64%, 49%, and 65% of deaths, and 38%, 37%, 16%, and 34% of hospitalisations in Fiji, Indonesia, Papua New Guinea, and Timor-Leste respectively. Therefore, our findings support the continued provision of COVID-19 booster doses to high-risk adults in 2023, and advocate for increased access to oral antivirals, especially in settings with low vaccine coverage such as Papua New Guinea. Future work should consider the threshold at which self-financing of COVID-19 oral antivirals would be viable for middle-income countries in South-East Asia and the Pacific.

The lateritic ore drying in the Cuban nickel producing industry is realized within flighted rotary dryers. In this investigation, performance indicators in regards to transfer of momentum, heat and mass were evaluated. The dryers operate in a concurrent configuration with combustion gas, at a productivity between 40 t h−1 and 50 t h−1. The distribution function of the residence time (RTD) was best fitted to a model of a multi-branch tanks-in-series system, theoretical residence time was 51 ± 2 min and experimental mean residence time 61 min, at a rate of 45 t h−1 and hydraulic efficiency 1.23, due to the presence of dead-zoon. Mass and energy balance was made following a “black box” model, as results, the specific fuel consumption was 27.25 ± 0.25 kg fuel t−1 of wet ore, specific energy consumption 79.66 ± 0.95 kg fuel t−1 of H2O evaporated, energy efficiency 97.28 ± 0.01 %, thermal efficiency 66.88 ± 0.71 % and drying efficiency 98.77 ± 0.12 %. Mathematical modelling was made using a system of differential equations, the rate of drying in falling rate period was estimated by Arrhenius equation, then, temperature profile and ore moisture content along the dryer was simulated. The model provided a successful predictive performance; for an inlet gas temperature between 850 °C and 900 °C, the ore moisture was reduced form 33.0 % (wet basis) to a range depending on the dryer productivity, from 3.0 % to 7.1 %. Designing a computerized system that implements these algorithms can benefit on efficiency and productivity of the production plant.

The vast majority of engineering issues, particularly heat transport equations, have a non-linear form. The analytical solution of temperature distribution, fin effectiveness, and fin efficiency of straight convective fins that transport heat with temperature-dependent thermal conductivity convection is derived in this work using the Direct Akbari-Ganji method (DAGM). DAGM is an effective methodology that is very simple and takes less time to solve non-linear temperature distribution fin problems. In the present investigation, Direct Akbari-Ganji technique offers desirable solutions to heat transfer problems. The round-off error is smaller compared to other methods. For the purpose of calibrating the error accuracy of the suggested direct technique, this result is compared with the numerical and earlier solution. The outcomes show that the DAGM for heat transfer equations in engineering situations has excellent validity and considerable promise. Finally, it is clear that when the thermo-geometric fin parameter increases, so does the fin efficiency. The homogeneity of the fin is due to the variable thermal conductivity. Four convection conditions with constant coefficients, growing and decaying convection coefficient with fin temperature are discussed.

This study proposes a non-linear mathematical model for analysing the effect of COVID-19 dynamics on the student population in higher education institutions. The theory of positivity and boundedness of solution is used to investigate the well-posedness of the model. The disease-free equilibrium solution is examined analytically. The next-generation operator method calculates the basic reproduction number (R0). Sensitivity analyses are carried out to determine the relative importance of the model parameters in spreading COVID-19. In light of the sensitivity analysis results, the model is further extended to an optimal control problem by introducing four time-dependent control variables: personal protective measures, quarantine (or self-isolation), treatment, and management measures to mitigate the community spread of COVID-19 in the population. Simulations evaluate the effects of different combinations of the control variables in minimizing COVID-19 infection. Moreover, a cost-effectiveness analysis is conducted to ascertain the most effective and least expensive strategy for preventing and controlling the spread of COVID-19 with limited resources in the student population.

Objectives: Primary and booster vaccinations are crucial in COVID-19 control. This study aimed to assess the minimum booster coverage to hamper potential surge of COVID-19 cases in 2023 in Indonesia, a low-resource setting country. Methods: We used a modified SEIR compartment model to assess different scenarios in booster coverage across West Java population: 35%, 50%, and 70%. We fitted the model, then we calculated the potential active cases in 2023 if each scenario was met before 2022 ends. A heat map of predicted cases from various booster coverages and time frames was produced and matched with vaccination rate's function to determine feasible time frames. Results: A minimum of 70% booster coverage in West Java is needed to reduce 90% of potential COVID-19 cases and avert possible surge in 2023. The booster doses should be distributed before February 2023 to achieve its optimum preventive effect. Delays in achieving minimum booster coverage is acceptable, but higher booster coverage will be required. Conclusions: For better COVID-19 control in Indonesia, booster vaccination is warranted, as presented by a case study in West Java. Sufficient vaccine supplies, infrastructure, and healthcare workers should be ensured to support a successful booster vaccination program.

Summary: Background: Respiratory syncytial virus (RSV) is a leading cause of hospitalisation and mortality in young children globally. The social distancing measures implemented against COVID-19 in Lombardy (Italy) disrupted the typically seasonal RSV circulation during 2019–2021 and caused substantially more hospitalisations during 2021–2022. The primary aim of this study is to quantify the immunity gap-defined as the increased proportion of the population naïve to RSV infection following the relaxation of COVID-19 restrictions in Lombardy, which has been hypothesised to be a potential cause of the increased RSV burden in 2021–2022. Methods: We developed a catalytic model to reconstruct changes in the age-dependent susceptibility profile of the Lombardy population throughout the COVID-19 pandemic. The model is calibrated to routinely collected hospitalisation, syndromic, and virological surveillance data and tested for alternative assumptions on age-dependencies in the risk of RSV infection throughout the pandemic. Findings: We estimate that the proportion of the Lombardy population naïve to RSV infection increased by 60.8% (95% CrI: 55.2–65.4%) during the COVID-19 pandemic: from 1.4% (95% CrI: 1.3–1.6%) in 2018–2019 to 2.3% (95% CrI: 2.2–2.5%) before the 2021–2022 season, corresponding to an immunity gap of 0.87% (95% CrI: 0.87–0.88%). We found evidence of heterogeneity in RSV transmission by age, suggesting that the COVID-19 restrictions had variable impact on the contact patterns and risk of RSV infection across ages. Interpretation: We estimate a substantial increase in the population-level susceptibility to RSV in Lombardy during 2019–2021, which contributed to an increase in primary RSV infections in 2021–2022. Funding: UK Medical Research Council (MRC), UK Foreign, Commonwealth & Development Office (FCDO), EDCTP2 programme, European Union, Wellcome Trust, Royal Society, EU-MUR PNRR INF-ACT.

The evolution of SARS-CoV-2 has demonstrated that emerging variants can set back the global COVID-19 response. The ability to rapidly assess the threat of new variants is critical for timely optimisation of control strategies.We present a novel method to estimate the effective transmission advantage of a new variant compared to a reference variant combining information across multiple locations and over time. Through an extensive simulation study designed to mimic real-time epidemic contexts, we show that our method performs well across a range of scenarios and provide guidance on its optimal use and interpretation of results. We also provide an open-source software implementation of our method. The computational speed of our tool enables users to rapidly explore spatial and temporal variations in the estimated transmission advantage.We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44–1.47) and 1.29 (95% CrI 1.29–1.30) times more transmissible than the wild type, using data from England and France respectively. We further estimate that Delta is 1.77 (95% CrI 1.69–1.85) times more transmissible than Alpha (England data).Our approach can be used as an important first step towards quantifying the threat of emerging or co-circulating variants of infectious pathogens in real-time.

This research suggests a comprehensive 3D model for modelling photocatalytic conversion of CO2 to methane, hydrogen and carbon monoxide in a packed bed reactor. This research includes two parts: designing the reactor's geometry using a new method in ''blender'' and using the computational fluid dynamics (CFD) technique to study and analyse the reaction, transport of phenomenon and light intensity through the reactor. Laminar flow, chemical reaction, mass transfer and optics physics were considered together to solve the equations. The surface reaction in the reactor follows a modified version of the Langmuir-Hinshelwood equation that evaluates the light profile in the reactor and the blockage of the catalyst's surface over time. Thus, a new method for 3D modelling light profiles in the reactor is introduced. The rate of reaction continues to increase with the pressure, and after 1 atm, the rate becomes steady. In the first 17 h, the methane rate is the highest, and then the carbon monoxide rate overcomes the methane rate. The rate of hydrogen is considerably lower than the other products. Changing pellets from spheres to Raschig rings causes growth in the probability density function (PDF) at the first moments. In methane's PDF, the amount of Raschig and sphere are 0.25 and 0.18, respectively, at the start of the reaction. Thus, the Raschig ring operates more effectively at the beginning moments of the process but eventually is outweighed after an hour by spherical particles. In the end, the validation of modelling and results were investigated with the aid of experimental data.

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.

Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level).In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.

Design and optimization of microalgae processes have traditionally relied on the application of unsegregated mathematical models, thus neglecting the impact of cell-to-cell heterogeneity. However, there is experimental evidence that the latter one, including but not limited to variation in mass/size, internal composition and cell cycle phase, can play a crucial role in both cultivation and downstream processes. Population balance equations (PBEs) represent a powerful approach to develop mathematical models describing the effect of cell-to-cell heterogeneity. In this work, the potential of PBEs for the analysis and design of microalgae processes are discussed. A detailed review of PBE applications to microalgae cultivation, harvesting and disruption is reported. The review is largely focused on the application of the univariate size/mass structured PBE, where the size/mass is the only internal variable used to identify the cell state. Nonetheless, the need, addressed by few studies, for additional or alternative internal variables to identify the cell cycle phase and/or provide information about the internal composition is discussed. Through the review, the limitations of previous studies are described, and areas are identified where the development of more reliable PBE models, driven by the increasing availability of single-cell experimental data, could support the understanding and purposeful exploitation of the mechanisms determining cell-to-cell heterogeneity.

Local energy communities require tools to select their most fitting community members, power-sharing strategy and technologies for their goals. This work aims to develop a model and a methodology to optimise local energy communities. We evaluate the presence of a battery energy storage system with different capacities and ownership options. Besides, we test two different sharing strategies like static and variable coefficients. Finally, we characterise local energy communities’ demand by comparing residential and commercial loads and varying the number of consumption points. We apply the method to a case study consisting of a 100 kWp photovoltaic installation in Valencia simulated with an hourly resolution for a whole year. We use real consumption data from households and commercial buildings and the current administrative requirements, obtaining the flows and status of each component within the local energy community at every moment. We assess each alternative’s economic performance, autarchy degree, and the amount of avoided greenhouse gasses emissions. Results indicate that a local energy community well optimised can fulfil economic, environmental or self-consumption goals. Results only advise installing a storage to increase the degree of self-consumption. Moreover, we obtain the best financial and environmental results in large communities with a 75% residential consumption.

The use of nanomaterials as a means of recovering heavy and light oil from petroleum reservoirs has increased over the preceding twenty years. Most researchers have found that injecting a nanoparticle dispersion (nanofluids) has led to good results and increased the amount of oil that can be recovered. In this research, we aim to imitate the three-dimensional hexagonal prism in the existence of SiO2 and Al2O3 nanoparticles for better oil recovery. Porosity (0.1≤φ≤0.4), mass flow rate (0.05mL/min≤Q≤0.05ml/min), nanoparticle concentration (0.01≤ψ≤0.04), and the effect of relative permeability (kr) on oil and water saturation in the presence of gravity under different time durations are all investigated. The result obtained for the model is verified with existing experimental data. The results indicated that the infulence of nanoparticle volume fraction (VF) is significant in enhancing the oil recovery rate. It is also observed that at low porosity values the oil recovery is maximum. The maximum oil recovery is attained at low values of mass flow rate in the 3D hexagonal prism in the presence of silicon and aluminium nanoparticles It is also observed that the use of SiO2 gives a better oil recovery rate than Al2O3. It is also observed that maximum oil recovery is obtained at 99% at a flow rate of 0.05 mL/min in the presence of silicon injection.

Crystallization is an important process, which possesses an undeniable role in many chemical industries. In recent years, significant endeavors have been made to increase the efficiency of this process via designing novel crystallizers. Development of numerical models and their efficient solutions is known as an important approach to justify the behavior of contributed mechanism in crystallization process. The emergence of various complexities towards the modeling of crystallization process is due to the existence of disparate mechanisms such as crystal birth, growth, and death processes. The prominent purpose of this study is to review various employed models to justify crystal birth, death, and growth in the crystallization process. Additionally, various numerical approaches like method of moments (MOM), discretized sizing technique (DST) and method of weighted residuals (MOWR) are presented in this review paper and their advantages and disadvantages to solve PBE are discussed in detail. At the end, future perspectives towards increasing the efficiency of various employed approaches foe solving population balance equation (PBE) in the crystallization process are presented, which can be of great interest for expert readers as well as non-expert investigators to be more acquainted with the undeniable role of mathematical modeling approaches in the crystallization process.

The energy demands in Zimbabwe have drastically increased due to the improvement of the country`s economy. To supply the electricity, Zimbabwe imports most of its energy but the demands are yet to be fulfilled. Bio-waste provide an alternative energy source, however, it has not been fully explored and their quantities are currently unknown. Therefore, this study assessed the bio-wastes availability for bioenergy generation in Zimbabwe. Yield data on biomasses were obtained from the international databases of Food and Agricultural Organization which was validated by the data obtained from Zimbabwean Ministry of Lands and Agriculture as well as from research institutes. The information compiled included biomass from agriculture, municipal solid waste, livestock's dung and municipal sewage sludge. Forests were not considered due to the government prohibition of forests exploitations. The results showed that a total of 49 Gtons of bio-wastes are sustainably available producing a total of 539 PJ of energy which constitute ∼42.3% of the total energy required in Zimbabwe. Among the available bio-wastes, crop residues with ∼48 Gtons produce the largest amount of energy ∼502 PJ followed by animal dung ∼36.2 PJ, then MSW ∼9.1 TJ and lastly by MSS ∼0.8 TJ. Assessment of the bio-waste availability is vital for bio-energy technology to be used sustainably implemented in Zimbabwe.

Summary: Background: Plasmodium vivax malaria is challenging to control and eliminate. Treatment with radical cure drugs fails to target the hidden asymptomatic and hypnozoite reservoirs in populations. PvSeroTAT, a novel serological test-and-treat intervention using a serological diagnostic to screen hypnozoite carriers for radical cure eligibility and treatment, could accelerate P. vivax elimination. Methods: Using a previously developed mathematical model of P. vivax transmission adapted to the Brazilian context as a case study for implementation, we evaluate the public health impact of various deployment strategies of PvSeroTAT as a mass campaign. We compare relative reductions in prevalence, cases averted, glucose-6-phosphate dehydrogenase (G6PD) tests, and treatment doses of PvSeroTAT campaigns to strengthened case management alone or mass drug administration (MDA) campaigns across different settings. Findings: Deploying a single round of PvSeroTAT with 80% coverage to treat cases with a high efficacy radical cure regimen with primaquine is predicted to reduce point population prevalence by 22.5% [95% UI: 20.2%–24.8%] in a peri-urban setting with high transmission and by 25.2% [95% UI: 9.6%–42.2%] in an occupational setting with moderate transmission. In the latter example, while a single PvSeroTAT achieves 9.2% less impact on prevalence and averts 300 less cases per 100,000 than a single MDA (25.2% [95% UI: 9.6%–42.2%] point prevalence reduction versus 34.4% [95% UI: 24.9%–44%]), PvSeroTAT requires 4.6 times less radical cure treatments and G6PD tests. Layering strengthened case management and deploying four rounds of PvSeroTAT six months apart is predicted to reduce point prevalence by a mean of 74.1% [95% UI: 61.3%–86.3%] or more in low transmission settings with less than 10 cases per 1000 population. Interpretation: Modelling predicts that mass campaigns with PvSeroTAT are predicted to reduce P. vivax parasite prevalence across a range of transmission settings and require fewer resources than MDA. In combination with strengthened case management, mass campaigns of serological test-and-treat interventions can accelerate towards P. vivax elimination. Funding: This project was funded in part by the Bill and Melinda Gates Foundation and the National Health and Medical Research Council.

Background: Mpox (monkeypox), a disease historically endemic to Africa, has seen its largest outbreak in 2022 by spreading to many regions of the world and has become a public health threat. Informed policies aimed at controlling and managing the spread of this disease necessitate the use of adequate mathematical modeling strategies. Objective: In this scoping review, we sought to identify the mathematical models that have been used to study mpox transmission in the literature in order to determine what are the model classes most frequently used, their assumptions, and the modelling gaps that need to be addressed in the context of the epidemiological characteristics of the ongoing mpox outbreak. Methods: This study employed the methodology of the PRISMA guidelines for scoping reviews to identify the mathematical models available to study mpox transmission dynamics. Three databases (PubMed, Web of Science and MathSciNet) were systematically searched to identify relevant studies. Results: A total of 5827 papers were screened from the database queries. After the screening, 35 studies that met the inclusion criteria were analyzed, and 19 were finally included in the scoping review. Our results show that compartmental, branching process, Monte Carlo (stochastic), agent-based, and network models have been used to study mpox transmission dynamics between humans as well as between humans and animals. Furthermore, compartmental and branching models have been the most commonly used classes. Conclusions: There is a need to develop modeling strategies for mpox transmission that take into account the conditions of the current outbreak, which has been largely driven by human-to-human transmission in urban settings. In the current scenario, the assumptions and parameters used by most of the studies included in this review (which are largely based on a limited number of studies carried out in Africa in the early 80s) may not be applicable, and therefore, can complicate any public health policies that are derived from their estimates. The current mpox outbreak is also an example of how more research into neglected zoonoses is needed in an era where new and re-emerging diseases have become global public health threats.

Infectious disease models are widely used by epidemiologists to improve the understanding of transmission dynamics and disease natural history, and to predict the possible effects of interventions. As the complexity of such models increases, however, it becomes increasingly challenging to robustly calibrate them to empirical data. History matching with emulation is a calibration method that has been successfully applied to such models, but has not been widely used in epidemiology partly due to the lack of available software. To address this issue, we developed a new, user-friendly R package hmer to simply and efficiently perform history matching with emulation. In this paper, we demonstrate the first use of hmer for calibrating a complex deterministic model for the country-level implementation of tuberculosis vaccines to 115 low- and middle-income countries. The model was fit to 9–13 target measures, by varying 19–22 input parameters. Overall, 105 countries were successfully calibrated. Among the remaining countries, hmer visualisation tools, combined with derivative emulation methods, provided strong evidence that the models were misspecified and could not be calibrated to the target ranges. This work shows that hmer can be used to simply and rapidly calibrate a complex model to data from over 100 countries, making it a useful addition to the epidemiologist’s calibration tool-kit.

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.

An analytical expression of the effectiveness factor of photocatalytic reactors that accounts for external mass transfer limitations is presented. The solution is based on Robin boundary conditions introducing the Sherwood number as the parameter that accounts for the external mass transfer limitations. Regions highlighting free diffusion, diffusion limitations, and external mass transfer limitations are identified based on the Thiele Modulus and the Sherwood number. At low values of the Sherwood number, the internal diffusion and reaction rate-limiting regions are surpassed by the external mass transfer limitations. A cut-off value of the Sherwood number below which external mass transfer limitations cannot be ignored is 0.55. The evaluation of the model showed that under Dirichlet boundary condition the model converges to literature models from similar studies. This 1-D model looked at the Sherwood number across a broad scope and accounted for a broader range of limitations associated with immobilized photocatalytic films.

United Nations charter to build a sustainable future has paved the way for the introduction of the Sustainability Development Goals (SDGs) at a global forum. In particular, SDG 11 is aligned with the idea of developing cities and communities that provide quality human life, by attaining net-zero discharge and self-sustainability. In line with the efforts of the global community, biochar has emerged as a viable solution due to its ability to convert waste into value. Finding applications in a spectrum of domains, biochar is being studied for use as an adsorbent, a co-catalyst to promote industrial-grade reactions and as a feed for fuel cells. Moreover, the inclusion of biochar as a soil enhancement material advocates the implementation of closed-loop nutrient cycles. Hence, it is imperative to have a proper understanding of the biomass characteristics, the hydrothermal treatment and the process parameters to be adopted for the production of char in order to identify biomass feedstock based on the application. The current work provides insight into the key factors and conditions employed for the production of biochar based on the plethora of applications. In order build a basic framework to aid in the production of char, the development of a statistical correlation was undertaken to determine the feed and optimum process parameters for the production of biochar based on its applications.

Summary: Background: Evidence suggests HSV-2 infection increases HIV acquisition risk and HIV/HSV-2 coinfection increases transmission risk of both infections. We analysed the potential impact of HSV-2 vaccination in South Africa, a high HIV/HSV-2 prevalence setting. Methods: We adapted a dynamic HIV transmission model for South Africa to incorporate HSV-2, including synergistic effects with HIV, to evaluate the impact of: (i) cohort vaccination of 9-year-olds with a prophylactic vaccine that reduces HSV-2 susceptibility; (ii) vaccination of symptomatically HSV-2-infected individuals with a therapeutic vaccine that reduces HSV shedding. Findings: An 80% efficacious prophylactic vaccine offering lifetime protection with 80% uptake could reduce HSV-2 and HIV incidence by 84.1% (95% Credibility Interval: 81.2–86.0) and 65.4% (56.5–71.6) after 40 years, respectively. This reduces to 57.4% (53.6–60.7) and 42.1% (34.1–48.1) if efficacy is 50%, 56.1% (53.4–58.3) and 41.5% (34.2–46.9) if uptake is 40%, and 29.4% (26.0–31.9) and 24.4% (19.0–28.7) if protection lasts 10 years. An 80% efficacious therapeutic vaccine offering lifetime protection with 40% coverage among symptomatic individuals could reduce HSV-2 and HIV incidence by 29.6% (21.8–40.9) and 26.4% (18.5–23.2) after 40 years, respectively. This reduces to 18.8% (13.7–26.4) and 16.9% (11.7–25.3) if efficacy is 50%, 9.7% (7.0–14.0) and 8.6% (5.8–13.4) if coverage is 20%, and 5.4% (3.8–8.0) and 5.5% (3.7–8.6) if protection lasts 2 years. Interpretation: Prophylactic and therapeutic vaccines offer promising approaches for reducing HSV-2 burden and could have important impact on HIV in South Africa and other high prevalence settings. Funding: WHO, NIAID.

The ability to accurately forecast the spread of coronavirus disease 2019 (COVID-19) is of great importance to the resumption of societal normality. Existing methods of epidemic forecasting often ignore the comprehensive analysis of multiple epidemic prevention measures. This paper aims to analyze various epidemic prevention measures through a compound framework. Here, a susceptible-vaccinated-infected-recovered-deceased (SVIRD) model is constructed to consider the effects of population mobility among origin and destination, vaccination, and positive retest populations. And we further use real-time observations to correct the model trajectory with the help of data assimilation. Seven prevention measures are used to analyze the short-term trend of active cases. The results of the synthetic scene recommended that four measures—improving the vaccination protection rate (IVPR), reducing the number of contacts per person per day (RNCP), selecting the region with less infected people as origin A (SES-O) and limiting population flow entering from A to B per day (LAIP-OD)—are the most effective in the short-term, with maximum reductions of 75%, 53%, 35% and 31%, respectively, in active cases after 150 days. The results of the real-world experiment with Hong Kong as the origin and Shenzhen as the destination indicate that when the daily vaccination rate increased from 5% to 9.5%, the number of active cases decreased by only 7.35%. The results demonstrate that reducing the number of contacts per person per day after productive life resumes is more effective than increasing vaccination rates.

Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases.We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest.Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.