Values Of Key Parameters Research Articles

Modelling of hypothetical SARS-CoV-2 point of care tests for routine testing in residential care homes: rapid cost-effectiveness analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes coronavirus disease 2019 (COVID-19), which at the time of writing (January 2021) was responsible for more than 2.25 million deaths worldwide and over 100,000 deaths in the UK. SARS-CoV-2 appears to be highly transmissible and could rapidly spread in residential care homes. The work undertaken aimed to estimate the clinical effectiveness and cost-effectiveness of viral detection point-of-care tests for detecting SARS-CoV-2 compared with laboratory-based tests in the setting of a hypothetical care home facility for elderly residents. The perspective was that of the NHS in 2020. The setting was a hypothetical care home facility for elderly residents. Care homes with en suite rooms and with shared facilities were modelled separately. A discrete event simulation model was constructed to model individual residents and simulate the spread of SARS-CoV-2 once it had entered the residential care facility. The numbers of COVID-19-related deaths and critical cases were recorded in addition to the number of days spent in isolation. Thirteen strategies involving different hypothetical SARS-CoV-2 tests were modelled. Recently published desirable and acceptable target product profiles for SARS-CoV-2 point-of-care tests and for hospital-based SARS-CoV-2 tests were modelled. Scenario analyses modelled early release from isolation based on receipt of a negative SARS-CoV-2 test result and the impact of vaccination. Incremental analyses were undertaken using both incremental cost-effectiveness ratios and net monetary benefits. Cost-effectiveness results depended on the proportion of residential care facilities penetrated by SARS-CoV-2. SARS-CoV-2 point-of-care tests with desirable target product profiles appear to have high net monetary benefit values. In contrast, SARS-CoV-2 point-of-care tests with acceptable target product profiles had low net monetary benefit values because of unnecessary isolations. The benefit of allowing early release from isolation depended on whether or not the facility had en suite rooms. The greater the assumed efficacy of vaccination, the lower the net monetary benefit values associated with SARS-CoV-2 point-of-care tests, when assuming that a vaccine lowers the risk of contracting SARS-CoV-2. There is considerable uncertainty in the values for key parameters within the model, although calibration was undertaken in an attempt to mitigate this. Some degree of Monte Carlo sampling error persists because of the timelines of the project. The example care home simulated will also not match those of decision-makers deciding on the clinical effectiveness and cost-effectiveness of introducing SARS-CoV-2 point-of-care tests. Given these limitations, the results should be taken as indicative rather than definitive, particularly the cost-effectiveness results when the relative cost per SARS-CoV-2 point-of-care test is uncertain. SARS-CoV-2 point-of-care tests have considerable potential for benefit for use in residential care facilities, but whether or not this materialises depends on the diagnostic accuracy and costs of forthcoming SARS-CoV-2 point-of-care tests. More accurate results would be obtained when there is more certainty on the diagnostic accuracy of and the reduction in time to test result associated with SARS-CoV-2 point-of-care tests when used in the context of residential care facilities, the proportion of care home penetrated by SARS-CoV-2 and the levels of immunity once vaccination is administered. These parameters are currently uncertain. This report was commissioned by the National Institute for Health Research (NIHR) Evidence Synthesis programme as project number 132154. This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 39. See the NIHR Journals Library website for further project information.

(Student Battery Slam Best Presentation Award Winner) Combining Experimentation and Computation for Accelerated Understanding of Electrode Morphology in Redox Flow Batteries

Redox flow batteries (RFBs) hold promise as an electrochemical intermediary between intermittent renewable power generation and the grid.1 Within the RFB architecture, porous electrodes are an integral component, contributing to a myriad functions, such as providing surface sites for redox reactions, dispersing electrolyte throughout the void matrix, and shuttling electrons through its conductive network.2 Carbonaceous materials, such as cloths, papers, and felts, are commonly used in state-of-the-art RFB embodiments, despite their tailored morphologies in polymer electrolyte fuel cells. Consequently, these media engender suboptimal performance in RFBs, necessitating advancement of electrode structures specifically designed for flow battery operation.While there have been previous systematic studies of RFB electrode structure/function relationships,3,4 most have been limited by manufacturing constraints, restricting the explorable design space. However, recent computational efforts have enabled broad sweeping property analyses,5 leveraging both data science and machine learning algorithms to identify key parameter values. These modeling and statistical pursuits, combined with advanced cell diagnostics, provide a platform for high-throughput material screening in the furtherance of electrode engineering.In this presentation, we highlight some of the computational tools we have developed for simulated analyses of electrode functionalities in RFBs. We then present experimental tools for in situ and operando electrode benchmarking, which can also be simulated with the aforementioned computation toolkit. Finally, we conclude by demonstrating how this combined platform can be used across a wide variety of electrode domains, revealing performance comparisons and micron-scale designs to direct manufacturing avenues.AcknowledgmentsThis work was funded by the Joint Center for Energy Storage Research, an Energy Innovation Hub of the U.S. Department of Energy, Office of Science, Basic Energy (De-AC02-06CH11357). K.M.T. recognizes additional support from the U.S. NSF Graduate Research Fellowship (1122374).References A. Z. Weber et al., J. Appl. Electrochem, 41, 1137 (2011).K. J. Kim et al., J. Mater. Chem. A, 3, 16913–16933 (2015).A. Forner-Cuenca, E. E. Penn, A. M. Oliveira, and F. R. Brushett, J. Electrochem. Soc., 166, A2230–A2241 (2019).K. M. Tenny, A. Forner-Cuenca, Y.-M. Chiang, and F. R. Brushett, J. Electrochem. En. Conv. Stor, 17 (2020).Z. Cheng et al., Applied Energy, 279, 115530 (2020).

Production resource planning for product transition considering learning effects

To generate continuing profits as well as to retain market share, companies need to regularly introduce new products into the market, replacing outdated products and enticing buyers to purchase new products. Thus, planning the product transition from the phase-out of a product to the phase-in of a product is a challenging task for both production planners and marketing managers. One significant concern in particular is how to account for the learning effect associated with the production of two successive products that share a single type of production resource. To address this problem of designing production plans for two successive products, we develop an MINLP (Mixed-Integer Non-Linear Programming) model that minimizes the total operating cost for managing the product transition. In doing so, we consider the number of production resources, the lengths of the product production periods, and the number of setups for both products during the ramp-down and ramp-up periods. We study the behaviors of the proposed model with the help of numerical experiments by varying the values of key parameters, including the learning effect and its impact on performance under different operational scenarios. Leveraging the results from the sensitivity analyses, the operational characteristics for managing the product transition are discussed.

Parametric Study on Residential Passive House Building in Different Chinese Climate Zones

With the increasing of building energy consumptions, the related issues of energy crisis and environmental pollution become more and more prominent. As an effective energy-saving technology, the passive house (PH) has been widely applied in China to reduce the building energy utilization. However, the design and application of PH vary with different climate conditions. Therefore, it is significant to conduct the parameterization of PH and propose a suitable design zoning of PH in China. In our study, a comprehensive feasibility analysis of the implementation of PH is performed, which chooses 31 representative cities covering 5 climatic regions. The sensitivity analysis firstly filters the key parameters that heavily affect energy consumption. The results indicate that the key parameters include external wall heat transfer coefficient (WU), basement ceiling heat transfer coefficient (BCU), solar heat gain coefficient (SHGC), glass G value (UG), heat recovery efficiency (HERE) and humidity recovery efficiency (HURE). Then, with the multiple regression approach, the values of key parameters are optimized. Based on the determined values of sensitive parameters, the design zoning of PH in China is finally proposed, which can guide the design of PH as well as enhance the application of PH in China.

Surf Zone Waves at the Onset of Breaking: 2. Predicting Breaking and Breaker Type

AbstractThis is the second of a two‐part series concerning remote observation and wave‐by‐wave analysis of the onset of breaking for spilling and plunging waves in the surf zone. Nearshore phase‐averaged and phase‐resolving wave models parameterize and directly simulate wave breaking and require realistic critical values of key wave parameters, such as the depth‐limited breaking index γ, steepness, or phase speed to initialize wave breaking. Using LIDAR line‐scans and infrared imagery, we observe over 1,600 breaking waves at the US Army Corps of Engineers Field Research Facility (FRF) in Duck, NC, and examine these parameters on a wave‐by‐wave basis at the onset of breaking for 413 spilling and 111 plunging waves. We find that γ is maximum near the onset of breaking at values consistent with those previously observed at the FRF, but that γ for plunging waves (0.73 ≤ γP ≤ 0.81) is greater than γ for spilling waves (0.63 ≤ γS ≤ 0.71). Direct estimates of wave face slope are maximum at the onset of breaking, approximately 22° for spilling and 30° for plunging waves. Using the relationship between γ and wave face slope, we develop a threshold for the onset of breaking that is a linear function of the two parameters. Wave face slope and γ are further used together to quantify whether a spilling‐ or plunging‐type breaker is more likely. We test the Miche steepness limit on our depth‐limited breaking data and find it correctly predicts only 10% of the plunging breakers and none of the spilling breakers in the surf zone.

Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter

Simulation of fracture initiation, propagation, and arrest is a problem of interest for many applications in the scientific community. There are a number of numerical methods used for this purpose, and among the most widely accepted is the combined finite-discrete element method (FDEM). To model fracture with FDEM, material behavior is described by specifying a combination of elastic properties, strengths (in the normal and tangential directions), and energy dissipated in failure modes I and II, which are modeled by incorporating a parameterized softening curve defining a post-peak stress-displacement relationship unique to each material. In this work, we implement a data assimilation method to estimate key model parameter values with the objective of improving the calibration processes for FDEM fracture simulations. Specifically, we implement the ensemble Kalman filter assimilation method to the Hybrid Optimization Software Suite (HOSS), a FDEM-based code which was developed for the simulation of fracture and fragmentation behavior. We present a set of assimilation experiments to match the numerical results obtained for a Split Hopkinson Pressure Bar (SHPB) model with experimental observations for granite. We achieved this by calibrating a subset of model parameters. The results show a steady convergence of the assimilated parameter values towards observed time/stress curves from the SHPB observations. In particular, both tensile and shear strengths seem to be converging faster than the other parameters considered.

Chamber Optimization for Comprehensive Improvement of Cone Crusher Productivity and Product Quality

This study aims to analyze the impact of key structural parameters such as the bottom angle of the mantle, the length of the parallel zone, and the eccentric angle on the productivity and product quality of the cone crusher and optimize the crushing chamber to improve the crusher performance. The amount of ore in the blockage layer was calculated by analyzing the movement state of the ore in the crushing chamber. Considering the amount of ore uplift further, the traditional mathematical model of crusher productivity was revised. Then, a mathematical model for dual-objective optimization of productivity and product quality of cone crusher was established. Furthermore, taking the existing C900 cone crusher as the research object, the influence of key parameters on the performance of the crusher was researched. And the optimal values of key structural parameters were obtained. Finally, based on the iron ore coarsely crushed by the gyratory crusher, the dynamic characteristics of the C900 cone crusher were simulated by using the discrete element method (DEM), and the simulation results are basically consistent with the numerical analysis results. Results show that considering the amount of ore uplift in the blockage layer, the revised mathematical model of crusher productivity can better characterize the actual productivity. The bottom angle of the mantle and the length of the parallel zone are within the range of 50°–60°and 140 mm–190 mm, respectively. The productivity shows a positive correlation with the bottom angle and a negative correlation with the length of the parallel zone. But the dependence of product quality on the angle and the length is just the opposite. The eccentric angle is within the range of 1.4°–2° and its decrease has a negative effect on the productivity and product quality.

Modelling of hypothetical SARS-CoV-2 point-of-care tests on admission to hospital from A&E: rapid cost-effectiveness analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes coronavirus disease 2019. At the time of writing (October 2020), the number of cases of COVID-19 had been approaching 38 million and more than 1 million deaths were attributable to it. SARS-CoV-2 appears to be highly transmissible and could rapidly spread in hospital wards. The work undertaken aimed to estimate the clinical effectiveness and cost-effectiveness of viral detection point-of-care tests for detecting SARS-CoV-2 compared with laboratory-based tests. A further objective was to assess occupancy levels in hospital areas, such as waiting bays, before allocation to an appropriate bay. The perspective was that of the UK NHS in 2020. The setting was a hypothetical hospital with an accident and emergency department. An individual patient model was constructed that simulated the spread of disease and mortality within the hospital and recorded occupancy levels. Thirty-two strategies involving different hypothetical SARS-CoV-2 tests were modelled. Recently published desirable and acceptable target product profiles for SARS-CoV-2 point-of-care tests were modelled. Incremental analyses were undertaken using both incremental cost-effectiveness ratios and net monetary benefits, and key patient outcomes, such as death and intensive care unit care, caused directly by COVID-19 were recorded. A SARS-CoV-2 point-of-care test with a desirable target product profile appears to have a relatively small number of infections, a low occupancy level within the waiting bays, and a high net monetary benefit. However, if hospital laboratory testing can produce results in 6 hours, then the benefits of point-of-care tests may be reduced. The acceptable target product profiles performed less well and had lower net monetary benefits than both a laboratory-based test with a 24-hour turnaround time and strategies using data from currently available SARS-CoV-2 point-of-care tests. The desirable and acceptable point-of-care test target product profiles had lower requirement for patients to be in waiting bays before being allocated to an appropriate bay than laboratory-based tests, which may be of high importance in some hospitals. Tests that appeared more cost-effective also had better patient outcomes. There is considerable uncertainty in the values for key parameters within the model, although calibration was undertaken in an attempt to mitigate this. The example hospital simulated will also not match those of decision-makers deciding on the clinical effectiveness and cost-effectiveness of introducing SARS-CoV-2 point-of-care tests. Given these limitations, the results should be taken as indicative rather than definitive, particularly cost-effectiveness results when the relative cost per SARS-CoV-2 point-of-care test is uncertain. Should a SARS-CoV-2 point-of-care test with a desirable target product profile become available, this appears promising, particularly when the reduction on the requirements for waiting bays before allocation to a SARS-CoV-2-infected bay, or a non-SARS-CoV-2-infected bay, is considered. The results produced should be informative to decision-makers who can identify the results most pertinent to their specific circumstances. More accurate results could be obtained when there is more certainty on the diagnostic accuracy of, and the reduction in time to test result associated with, SARS-CoV-2 point-of-care tests, and on the impact of these tests on occupancy of waiting bays and isolation bays. These parameters are currently uncertain. This report was commissioned by the National Institute for Health Research (NIHR) Evidence Synthesis programme as project number 132154. This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 21. See the NIHR Journals Library website for further project information.

Maintenance, update and further development of EFSA's Chemical Hazards: OpenFoodTox 2.0

The present document represents the summary of the activities undertaken during the second year of the framework contract (OC/EFSA/SCER/2018/01) with the overall objectives of maintaining, updating and further developing the OpenFoodTox database (“OpenFodTox 2.0”). OpenFoodTox has been developed over the last 8 years to map hazard data as published in EFSA documents (opinions, statements, and conclusions) dealing with chemicals risk assessment of food and feed. The repository holds summary data on identification of chemicals, document descriptors, hazard identification, and hazard characterisation. Overall, OpenFoodTox 2.0 now includeshazard data from more thanfrom 10300 assessments, 2140 EFSA outputs and 5100 chemicals with new entries for 138 new substances, 437 hazard assessments from 181 EFSA documents. The data collection for new data types has been continued with further expansion of the data model to incorporate new data types including physicochemical properties (OHT 1 to 23-5), degradation and bioaccumulation data (OHT 32 and 33), toxicokinetic data (OHT 58), intermediate effects (OHT 201), “New Approach methodologies (NAMs)” and Exposure Information (OHT 301 to 306). OpenFoodTox content was enriched with physico-chemical properties and PK/TK data including quantitative values for key parameters (Cmax, AUC, half-life)obtained from EFSA documents published between 2018 and 2020 and progressively integrated in the database. Overall, 3658 experimental physico-chemical data records for 522 substances (138 EFSA outputs) and 1986 PK/TK records were added for 315 substances (108 EFSA outputs). Case studies onOHT 201 to report mechanistic data are illustrated. Furthermore, preliminary results on new QSAR models are here presented as part of the design of an in silico integrative tool allowing for the description and prediction of hazard properties of chemicals for “OpenFoodTox 2”.

Parallel disassembly sequence planning using improved ant colony algorithm

Disassembly is one of the most crucial links in the product life cycle. Disassembly sequence planning (DSP) is a combination of optimization problem and has been studied by many researchers. Asynchronous parallel disassembly planning (APDP) eliminates the requirement that manipulators must start and stop their tasks in the working process. In this paper, a new asynchronous parallel disassembly sequence planning method is proposed, which to deal with the problems of highly time-dependent on asynchronous parallel disassembly mode. The disassembly time model has been divided into execution time and preparation time. The former represents the time consumes by removing a part, and the latter means the time consumes on the disassembly tool preparation while the disassembly position switch. The disassembly matrix and the disassembly work area collision matrix are also utilized to avoid infeasible sequences. Based on the max-min ant system, a waiting strategy is proposed to solve asynchronous parallel disassembly sequence planning problem. To further shorten disassembly process, a time overlap strategy is developed to take advantage of waiting time. Finally, a bevel gear reducer is utilized as an example to discuss and analysis the value of key parameters, as well as the effectiveness of the algorithm and strategies proposed in the paper.

Monitoring Robust Estimates for Compositional Data

In a number of recent articles Riani, Cerioli, Atkinson and others advocate the technique of monitoring robust estimates computed over a range of key parameter values. Through this approach the diagnostic tools of choice can be tuned in such a way that highly robust estimators which are as efficient as possible are obtained. This approach is applicable to various robust multivariate estimates like S- and MM-estimates, MVE and MCD as well as to the Forward Search in whichmonitoring is part of the robust method. Key tool for detection of multivariate outliers and for monitoring of robust estimates is the Mahalanobis distances and statistics related to these distances. However, the results obtained with thistool in case of compositional data might be unrealistic since compositional data contain relative rather than absolute information and need to be transformed to the usual Euclidean geometry before the standard statistical tools can be applied. Various data transformations of compositional data have been introduced in the literature and theoretical results on the equivalence of the additive, the centered, and the isometric logratio transformation in the context of outlier identification exist. To illustrate the problem of monitoring compositional data and to demonstrate the usefulness of monitoring in this case we start with a simple example and then analyze a real life data set presenting the technologicalstructure of manufactured exports. The analysis is conducted with the R package fsdaR, which makes the analytical and graphical tools provided in the MATLAB FSDA library available for R users.

A Continuous Statistical Phasing Framework for the Analysis of Forensic Mitochondrial DNA Mixtures.

Despite the benefits of quantitative data generated by massively parallel sequencing, resolving mitotypes from mixtures occurring in certain ratios remains challenging. In this study, a bioinformatic mixture deconvolution method centered on population-based phasing was developed and validated. The method was first tested on 270 in silico two-person mixtures varying in mixture proportions. An assortment of external reference panels containing information on haplotypic variation (from similar and different haplogroups) was leveraged to assess the effect of panel composition on phasing accuracy. Building on these simulations, mitochondrial genomes from the Human Mitochondrial DataBase were sourced to populate the panels and key parameter values were identified by deconvolving an additional 7290 in silico two-person mixtures. Finally, employing an optimized reference panel and phasing parameters, the approach was validated with in vitro two-person mixtures with differing proportions. Deconvolution was most accurate when the haplotypes in the mixture were similar to haplotypes present in the reference panel and when the mixture ratios were neither highly imbalanced nor subequal (e.g., 4:1). Overall, errors in haplotype estimation were largely bounded by the accuracy of the mixture’s genotype results. The proposed framework is the first available approach that automates the reconstruction of complete individual mitotypes from mixtures, even in ratios that have traditionally been considered problematic.

Fatigue behaviour of corroded stud based on crack growth theory

In order to promote the application and development of steel–concrete composite beams in bridges, the fatigue performance of corroded studs was investigated. Based on fatigue crack propagation theory and the fatigue failure mechanism of corroded studs, a fatigue life prediction model for corroded studs was proposed. The values of key parameters in the fatigue life prediction model of corroded studs were obtained. Moreover, the fatigue life of corroded studs was analysed. Results show that the error between the calculated value and the test value of the fatigue life of corroded studs is less than 10%, and the fatigue life prediction model for corroded studs is sufficiently accurate. As the corrosion rate of a stud increases, its fatigue life rapidly decreases. When the corrosion rate of the stud increases from 5 to 50%, the fatigue life of the stud decreases from 20 to 90%, respectively; thus corrosion greatly reduces the fatigue life of the stud.

Evaluating three commonly used infiltration methods for permeable surfaces in urban areas using the SWMM and STORM

Abstract Climate change and urbanization increase the pressure on combined sewer systems in urban areas resulting in elevated combined sewer overflows, degraded water quality in receiving waters, and changing stream flows. Permeable surfaces offer infiltration potential, which can contribute to alleviate the runoff to combined sewer systems. The variation in urban soil characteristics and the initial moisture conditions before a rainfall event are important factors affecting the infiltration process and consequently runoff characteristics. In this study, the urban hydrological models SWMM and STORM are used to evaluate the Green-Ampt, Horton, and Holtan infiltration methods for three urban sandy soils. A sensitivity analysis was carried out on a set of key parameter values. In addition, long-term simulations were conducted to evaluate the ability to account for initial soil moisture content. The results showed that the Holtan method's ability to account for both available storage capacity and maximum infiltration rate, as well as evapotranspiration in the regeneration of infiltration capacity, gave the best result with regard to runoff behaviour, especially for long-term simulations. Furthermore, the results from the urban sandy soils with different infiltration rate at saturation, together with a high sensitivity to the degree of sensitivity for maximum infiltration rate under dry conditions and minimum infiltration rate under wet conditions, indicate that field measurements of infiltration rate should be carried out at saturation for these soils.

Determination of ASI and THIV parameters based on the results of experimental and numerical research in relation to EU standards

Determining the values of ASI (Acceleration Severity Index) and THIV (Theoretical Head Impact Velocity) parameters during tests allows you to assign an appropriate class for a given type of object to determine the safety level and to give the CE marking. The paper presents the methodology for determining these parameters based on the EN 1317-1 and EN 12767 standards. The paper also presents a tool created with the use of the Python programming language, which, based on the results of experimental tests or the results of numerical calculations, allows to determine the ASI and THIV values. The values of key parameters from the point of view of normative tests were calculated based on the results of experimental tests of the road sign supporting mast and numerical analysis carried out for the same case using the Finite Element Method and LS-Dyna software, following the EN 12767 standard.

THE MORTARS FOR DRILL-INJECTION PILES CREATION

The article presents information about development of the composition of the mortars which are based on cement and sand-cement with the addition of a superplasticizing admixture for performing well crimping during drill-injection piles creation. The need to develop new compositions of the mortars was opened due to the discovery of some defects in the experimental drill-injection pile shafts which were created by using cement mortar with the addition of liquid glass, after their excavation and examination. These defects (massive soil penetration into the pile shaft near its wellhead and soil and mortar mixing along the perimeter of the piles) are takes place owing the insufficient mortar density, its high shrinkage and low strength. The developed compositions are free of these cons and have increased value of mortar key parameters (density, strength, shrinkage) which is too important during the drill-injection piles creations. After laboratory tests which were done to identify the optimal ratio of mortar components and determine their main parameters, control tests of these solutions were carried out on the investigation site to check the possibility of their use on existing technological equipment. Field tests of the developed mortars confirmed the possibility of their use for drill-injection piles creation. Using the developed compositions of mortars drill-injection piles were successfully performed for the foundation strengthening on some objects in Tyumen.

Generic Dynamical Model of PEM Electrolyser under Intermittent Sources

Proton Exchange Membrane (PEM) water electrolysis system is one of the promising technologies to produce green hydrogen from renewable energy sources (wind and solar). However, performance and dynamic analysis of PEM water electrolysis systems are challenging due to the intermittent nature of such sources and involved multi-physical behaviour of the components and subsystems. This study proposes a generic dynamical model of the PEM electrolysis system represented in a modular fashion using Bond Graph (BG) as a unified modelling approach. Causal and functional properties of the BG facilitate the formal PEM electrolyser model to adapt and to fit the different configurations of the electrolyser ranging from laboratory scale to industrial scale. The system-specific key parameter values are identified optimally for a laboratory-scale electrolyser system running on a multi-source energy platform using experimental data. The mean absolute percentage error between simulation and experimental data is found to be less than 5%. The performance characteristic curves of the electrolyser are predicted at different operating temperatures using the identified key parameters. The predicted performance is in good agreement with the expected behaviour of the electrolyser found in the literature. The model also estimates the different energy losses and the real-time efficiency of the system under dynamic inputs. With these capabilities, the developed model provides an economical mean for design, control, and diagnosis development of such systems.

A note on estimating the Cox-Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome.

In 2019 we published a pair of articles in Statistics in Medicine that describe how to calculate the minimum sample size for developing a multivariable prediction model with a continuous outcome, or with a binary or time-to-event outcome. As for any sample size calculation, the approach requires the user to specify anticipated values for key parameters. In particular, for a prediction model with a binary outcome, the outcome proportion and a conservative estimate for the overall fit of the developed model as measured by the Cox-Snell R2 (proportion of variance explained) must be specified. This proposal raises the question of how to identify a plausible value for R2 in advance of model development. Our articles suggest researchers should identify R2 from closely related models already published in their field. In this letter, we present details on how to derive R2 using the reported C statistic (AUROC) for such existing prediction models with a binary outcome. The C statistic is commonly reported, and so our approach allows researchers to obtain R2 for subsequent sample size calculations for new models. Stata and R code is provided, and a small simulation study.

An updated cost-effectiveness analysis of pazopanib versus sunitinib as first-line treatment for locally advanced or metastatic renal cell carcinoma in Italy

Objective To assess the cost-effectiveness of pazopanib versus sunitinib as a first-line treatment for patients with metastatic renal cell carcinoma (mRCC) from an Italian National Health Service perspective, considering the evolving Italian landscape in terms of new reimbursement agreements trend. Methods This analysis is an update of the previously published cost-effectiveness analysis to incorporate recent 2019 costs and additional changes regarding drug discounting. A partitioned-survival analysis model with three different health states (progression-free survival, post-progression survival, and dead) was utilized. Outcomes included progression-free life years, post-progression life years, overall life years, quality-adjusted life years (QALYs), and costs calculated for both treatments. Cost-effectiveness was assessed in terms of incremental costs per QALY gained and the net monetary benefit (NMB) of pazopanib versus sunitinib. In the base case analysis, a time horizon of 5 years was used and future costs and QALYs were discounted at a 3% annual discount rate. An impact of methodological and parameter uncertainly on base case results was evaluated using probabilistic and deterministic sensitivity analyses. Results In the base case, pazopanib had higher QALYs (+0.060) at lower costs (−€5,857) versus sunitinib, hence it dominated sunitinib. At willingness-to-pay thresholds of €30,000 and €50,000 per QALY, the NMB with pazopanib were €7,647 and €8,841 per patient, respectively, versus sunitinib. The probability that pazopanib is cost-effective versus sunitinib was estimated to be 97.5% at a cost-effectiveness threshold of €20,000, 95.4% at a threshold of €30,000, and 90.2% at a threshold of €50,000 per QALY. Cost-effectiveness results were robust to changes in key parameter values and assumptions as demonstrated by deterministic sensitivity analyses. Conclusions Pazopanib is likely to represent a cost-effective treatment option compared with sunitinib as a first-line treatment for patients with metastatic RCC in Italy.

On Issue of Strip’s Break during Cold Rolling

The strip breaks during the cold rolling lead to significant losses in a continuous mill: downtime, associated with the elimination of consequences, and additional consumption of metal. Strip break occurs at a specific strip location, which further complicates the identification and analysis of its root causes, since a critical deviation of the parameters in only one small section of the strip may already be the cause of the subsequent break during cold rolling. In this work, the methods for checking hypotheses and regression modeling are used to identify the key parameters of hot-rolled strips that affect the strip break. Then, on the basis of the developed mathematical models, threshold values for key parameters are determined, exceeding which increases the probability of strip break.