Climate change impacts of conventional sewage sludge treatment and disposal

Sewage sludge (SS) management remains a challenge across the world. We quantified the potential climate change impacts of eight conventional technology configurations (TCs) for SS treatment and disposal by considering four different energy exchanges and using a life cycle assessment (LCA) model that employed uncertainty distributions for 104 model parameters. All TCs showed large climate change loads and savings (net values ranging from 123 to 1148 kg CO₂-eq/t TS) when the energy exchange was with a fossil-based energy system, whereas loads and savings were approximately three times lower when the energy exchange was with a renewable energy system. Uncertainty associated with the climate change results was more than 100% with fossil-energy exchange and low TS content of SS but was lower for renewable energy. Landfilling had the greatest climate change impact, while thermal drying with incineration had the highest probability of providing better climate change performance than other TCs. The global sensitivity analysis identified nine critical technological parameters. Many of them can be easily measured for relevant SS and technology levels to improve specific estimates of climate change impact. When all scenarios were optimized to the 20% best cases, thermal drying with incineration outperformed the other TCs. This paper contributes to better quantifying the climate change impacts of different technologies used for sludge treatment given changing energy systems and identifies crucial parameters for further technological development.

An explicit robust stability condition for uncertain time-varying first-order plus dead-time systems

First-order plus dead-time (FOPDT) models are broadly used in process control to represent damped dynamic processes with time delays. An explicit condition for parameter- and delay-dependent robust stability of FOPDT systems with varying uncertain parameters and delay is derived in this paper. An internal model control (IMC) approach is proposed to parameterize stabilizing controllers that satisfy the output tracking objective in time-varying FOPDT systems represented by an uncertain first-order dynamic model with a time-varying delay in the control input. The small-gain theorem is used to derive an explicit necessary and sufficient parameter-dependent robust stability condition as a function of the nominal system gain, nominal varying delay, nominal time constant, and the bounds of the parameter uncertainties. An equivalent proportional-integral-derivative (PID) controller is then extracted to facilitate the implementation of the proposed IMC-based robust control. The application of the proposed explicit robust stability condition is studied in the context of air-fuel ratio (AFR) control in lean-burn spark ignition (SI) engines with a large time-varying transport delay in the control loop due to the placement of the universal exhaust gas oxygen (UEGO) sensor downstream the catalytic converter.

How Meaningful is the Elite Quality Index Ranking?

The Elite Quality Index (EQx) attempts to measure the propensity of elites-on aggregate-to create value, rather than to rent seek. The index has attracted worldwide media and press attention. In their articles, journalists have based their analyses primarily on their own countries' position in the EQx ranking. But how meaningful is the EQx ranking? How do the uncertainties underlying some of the assumptions made in the index propagate to the country rankings? We conduct a global uncertainty and sensitivity analysis (UA and SA) of the EQx and compute Sobol' first and total order sensitivity indices using state of the art estimators, in order to scrutinise the implications of index assumptions and assess the reliability of the EQx ranking. The UA suggests that the EQx ranking of 2021 (EQx2021) is largely stable for the top 50 countries, but exhibits considerable uncertainties especially for middle and lower performing countries. The SA highlights the handling of missing data, the normalisation process and the weighting scheme as most important methodological choices, while the largest potential for improvement is observed in how raw missing indicator data is handled.

Analysis of an age-structured tuberculosis model with treatment and relapse

A new tuberculosis model consisting of ordinary differential equations and partial differential equations is established in this paper. The model includes latent age (i.e., the time elapsed since the individual became infected but not infectious) and relapse age (i.e., the time between cure and reappearance of symptoms of tuberculosis). We identify the basic reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {R}_{0}$$\end{document}R0 for this model, and show that the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {R}_{0}$$\end{document}R0 determines the global dynamics of the model. If \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {R}_{0}<1$$\end{document}R0<1, the disease-free equilibrium is globally asymptotically stable, which means that tuberculosis will disappear, and if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {R}_{0}>1$$\end{document}R0>1, there exists a unique endemic equilibrium that attracts all solutions that can cause the spread of tuberculosis. Based on the tuberculosis data in China from 2007 to 2018, we use Grey Wolf Optimizer algorithm to find the optimal parameter values and initial values of the model. Furthermore, we perform uncertainty and sensitivity analysis to identify the parameters that have significant impact on the basic reproduction number. Finally, we give an effective measure to reach the goal of WHO of reducing the incidence of tuberculosis by 80% by 2030 compared to 2015.

Mathematical modelling of the role of mucosal vaccine on the within-host dynamics of Chlamydia trachomatis

A mathematical model of the within-host replicative dynamics of C. trachomatis infection and its interactions with the immune system, in the presence of a mucosal vaccine, is presented. Our aim is to estimate the requisite efficacy of an efficacious mucosal vaccine that could promote a stable disease-free state in vivo. Sensitivity analysis was used to quantify how variability in the model parameters influence the value of the disease threshold R₀. This shows that the two most important factors to be considered for achieving a disease-free state state in vivo, based on their influence on R₀, are the efficacy of the Chlamydia vaccine, and the rate at which the humoral immune response protects healthy epithelial cells from infection. Numerical simulations of the model show that a vaccine with a minimum efficacy of 86% may be required for the in vivo control of Chlamydia burden. Such effective but imperfect Chlamydia vaccine could confer long-term protective immunity to genital Chlamydia infections. Conditions under which lower vaccine efficacies may suffice are also explored.

Designing a robust index for WWTP energy efficiency: The ENERWATER water treatment energy index

The development and use of composite indexes has exploded in the last 15 years as a tool to summarise the large amount of information available nowadays. To ensure that composite indicators reflect faithfully the purpose of evaluation and are widely accepted and used, the users must understand the relationship between individual sub-indicators and the result (transparency) and the evaluation should not depend on weights, lack of information, etc. (robustness). It is proposed here for the first time a composite index to measure energy efficiency in a wastewater treatment plant, from the definition of the individual sub-indicators to the assignation of an energy label, easy to communicate to a broad public. Using as a demonstration real data from 44 wastewater treatment plants, the index robustness is tested and improved by uncertainty and sensitivity analysis results, finally achieving a robust algorithm which can be used by the large majority of wastewater treatment plants.

Mathematical Assessment of the Role of Early Latent Infections and Targeted Control Strategies on Syphilis Transmission Dynamics

A new multi-stage deterministic model for the transmission dynamics of syphilis, which incorporates disease transmission by individuals in the early latent stage of syphilis infection and the reversions of early latent syphilis to the primary and secondary stages, is formulated and rigorously analysed. The model is used to assess the population-level impact of preventive (condom use) and therapeutic measures (treatment using antibiotics) against the spread of the disease in a community. It is shown that the disease-free equilibrium of the model is globally-asymptotically stable whenever the associated control reproduction number (denoted by [Formula: see text]) is less than unity. A special case of the model is shown to have a unique and globally-asymptotically stable endemic equilibrium whenever the associated reproduction number (denoted by [Formula: see text]) exceeds unity. Uncertainty and sensitivity analysis of the model, using parameter values and ranges relevant to syphilis transmission dynamics in Nigeria, show that the top three parameters that drive the syphilis infection (with respect to [Formula: see text]) are the disease transmission rate ([Formula: see text]), compliance in condom use (c) and efficacy of condom ([Formula: see text]). Numerical simulations of the model show that the targeted treatment of secondary syphilis cases is more effective than the targeted treatment of individuals in the primary or early latent stage of syphilis infection.

Finding sensitive parameters in internal dose calculations for radiopharmaceuticals commonly used in clinical nuclear medicine

Internal dosimetry after incorporation of radionuclides requires standardized biokinetic and dosimetric models. The aim of the present work was to identify the parameters and the components of the models which contribute most to dosimetric uncertainty. For this a method was developed allowing for the calculation of the uncertainties of the absorbed dose coefficients. More specifically, the sampling-based regression method and the variance-based method were used to develop and apply a global method of sensitivity analysis. This method was then used to quantify the impact of various biokinetic and dosimetric parameters on the uncertainty of internal doses associated with the incorporation of seven common radiopharmaceuticals. It turned out that the correlation between biokinetic parameters and time-integrated activity or calculated absorbed dose is strongest when the source and target organ are identical, in accordance with the ICRP and the MIRD approach. According to the ICRP approach, the parameter F_s which describes the fractional distribution of any incorporated radioactivity to organ S, has the greatest correlation with the time-integrated activity in the corresponding source organ or with the calculated dose in the corresponding target organ. In contrast, the MIRD approach suggested several biokinetic parameters with similar correlation. The dosimetric parameters usually contribute more to uncertainty in the calculated dose coefficients than the biokinetic parameters, in both approaches. The results obtained are helpful for the revision of biokinetic models for radiopharmaceuticals, because the most important parameters in clinical applications can now be identified and investigated in future studies.

Modelling the bioaccumulation of persistent organic pollutants in agricultural food chains for regulatory exposure assessment

New models for estimating bioaccumulation of persistent organic pollutants in the agricultural food chain were developed using recent improvements to plant uptake and cattle transfer models. One model named AgriSim was based on K _OW regressions of bioaccumulation in plants and cattle, while the other was a steady-state mechanistic model, AgriCom. The two developed models and European Union System for the Evaluation of Substances (EUSES), as a benchmark, were applied to four reported food chain (soil/air-grass-cow-milk) scenarios to evaluate the performance of each model simulation against the observed data. The four scenarios considered were as follows: (1) polluted soil and air, (2) polluted soil, (3) highly polluted soil surface and polluted subsurface and (4) polluted soil and air at different mountain elevations. AgriCom reproduced observed milk bioaccumulation well for all four scenarios, as did AgriSim for scenarios 1 and 2, but EUSES only did this for scenario 1. The main causes of the deviation for EUSES and AgriSim were the lack of the soil-air-plant pathway and the ambient air-plant pathway, respectively. Based on the results, it is recommended that soil-air-plant and ambient air-plant pathway should be calculated separately and the K _OW regression of transfer factor to milk used in EUSES be avoided. AgriCom satisfied the recommendations that led to the low residual errors between the simulated and the observed bioaccumulation in agricultural food chain for the four scenarios considered. It is therefore recommended that this model should be incorporated into regulatory exposure assessment tools. The model uncertainty of the three models should be noted since the simulated concentration in milk from 5th to 95th percentile of the uncertainty analysis often varied over two orders of magnitude. Using a measured value of soil organic carbon content was effective to reduce this uncertainty by one order of magnitude.

Country-Specific Conditions for Work and Family Reconciliation: An Attempt at Quantification

The country-specific conditions for work and family reconciliation (family policies, labour market structures and gender norms) are believed to influence tensions between paid employment and childbearing. So far there have been very few attempts to quantify these conditions into a single measure which would allow for comparisons across countries of the magnitude of the barriers that working parents encounter. Such a quantitative index could also facilitate a quantitative investigation of the association between the macro-level conditions for work and family reconciliation and fertility at the individual level. In this paper, we seek to fill this gap by proposing a quantitative index of country-specific conditions for work and family reconciliation, which may be used, for example, in a two-level regression framework. The index takes into account all three components of the conditions for work and family reconciliation. We also perform a series of uncertainty and sensitivity analyses which verify the robustness of our assumptions and which illustrate the range of the index volatility.

Mathematical analysis of swine influenza epidemic model with optimal control

A deterministic model is designed and used to analyze the transmission dynamics and the impact of antiviral drugs in controlling the spread of the 2009 swine influenza pandemic. In particular, the model considers the administration of the antiviral both as a preventive as well as a therapeutic agent. Rigorous analysis of the model reveals that its disease-free equilibrium is globally asymptotically stable under a condition involving the threshold quantity-reproduction number R c . The disease persists uniformly ifR c > 1 and the model has a unique endemic equilibrium under certain condition. The model undergoes backward bifurcation if the antiviral drugs are completely efficient. Uncertainty and sensitivity analysis is presented to identify and study the impact of critical model parameters on the reproduction number. A time dependent optimal treatment strategy is designed using Pontryagin's maximum principle to minimize the treatment cost and the infected population. Finally the reproduction number is estimated for the influenza outbreak and model provides a reasonable fit to the observed swine (H1N1) pandemic data in Manitoba, Canada, in 2009.

Unraveling the contribution of pancreatic beta-cell suicide in autoimmune type 1 diabetes

In type 1 diabetes, an autoimmune disease mediated by autoreactive T-cells that attack insulin-secreting pancreatic beta-cells, it has been suggested that disease progression may additionally require protective mechanisms in the target tissue to impede such auto-destructive mechanisms. We hypothesize that the autoimmune attack against beta-cells causes endoplasmic reticulum stress by forcing the remaining beta-cells to synthesize and secrete defective insulin. To rescue beta-cell from the endoplasmic reticulum stress, beta-cells activate the unfolded protein response to restore protein homeostasis and normal insulin synthesis. Here we investigate the compensatory role of unfolded protein response by developing a multi-state model of type 1 diabetes that takes into account beta-cell destruction caused by pathogenic autoreactive T-cells and apoptosis triggered by endoplasmic reticulum stress. We discuss the mechanism of unfolded protein response activation and how it counters beta-cell extinction caused by an autoimmune attack and/or irreversible damage by endoplasmic reticulum stress. Our results reveal important insights about the balance between beta-cell destruction by autoimmune attack (beta-cell homicide) and beta-cell apoptosis by endoplasmic reticulum stress (beta-cell suicide). It also provides an explanation as to why the unfolded protein response may not be a successful therapeutic target to treat type 1 diabetes.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

Introducing uncertainty analysis of nucleation and crystal growth models in Process Analytical Technology (PAT) system design of crystallization processes

This paper presents the application of uncertainty and sensitivity analysis as part of a systematic model-based process monitoring and control (PAT) system design framework for crystallization processes. For the uncertainty analysis, the Monte Carlo procedure is used to propagate input uncertainty, while for sensitivity analysis, global methods including the standardized regression coefficients (SRC) and Morris screening are used to identify the most significant parameters. The potassium dihydrogen phosphate (KDP) crystallization process is used as a case study, both in open-loop and closed-loop operation. In the uncertainty analysis, the impact on the predicted output of uncertain parameters related to the nucleation and the crystal growth model has been investigated for both a one- and two-dimensional crystal size distribution (CSD). The open-loop results show that the input uncertainties lead to significant uncertainties on the CSD, with appearance of a secondary peak due to secondary nucleation for both cases. The sensitivity analysis indicated that the most important parameters affecting the CSDs are nucleation order and growth order constants. In the proposed PAT system design (closed-loop), the target CSD variability was successfully reduced compared to the open-loop case, also when considering uncertainty in nucleation and crystal growth model parameters. The latter forms a strong indication of the robustness of the proposed PAT system design in achieving the target CSD and encourages its transfer to full-scale implementation.