Remembering the Work of Phillip L. Geissler: A Coda to His Scientific Trajectory.

Phillip L. Geissler made important contributions to the statistical mechanics of biological polymers, heterogeneous materials, and chemical dynamics in aqueous environments. He devised analytical and computational methods that revealed the underlying organization of complex systems at the frontiers of biology, chemistry, and materials science. In this retrospective we celebrate his work at these frontiers.

Development and validation of machine learning models to predict frailty risk for elderly.

AIMS: Early identification and intervention of the frailty of the elderly will help lighten the burden of social medical care and improve the quality of life of the elderly. Therefore, we used machine learning (ML) algorithm to develop models to predict frailty risk in the elderly. DESIGN: A prospective cohort study. METHODS: We collected data on 6997 elderly people from Chinese Longitudinal Healthy Longevity Study wave 6-7 surveys (2011-2012, 2014). After the baseline survey in 1998 (wave 1), the project conducted follow-up surveys (wave 2-8) in 2000-2018. The osteoporotic fractures index was used to assess frailty. Four ML algorithms (random forest [RF], support vector machine, XGBoost and logistic regression [LR]) were used to develop models to identify the risk factors of frailty and predict the risk of frailty. Different ML models were used for the prediction of frailty risk in the elderly and frailty risk was trained on a cohort of 4385 elderly people with frailty (split into a training cohort [75%] and internal validation cohort [25%]). The best-performing model for each study outcome was tested in an external validation cohort of 6997 elderly people with frailty pooled from the surveys (wave 6-7). Model performance was assessed by receiver operating curve and F2-score. RESULTS: Among the four ML models, the F2-score values were similar (0.91 vs. 0.91 vs. 0.88 vs. 0.90), and the area under the curve (AUC) values of RF model was the highest (0.75), followed by LR model (0.74). In the final two models, the AUC values of RF and LR model were similar (0.77 vs. 0.76) and their accuracy was identical (87.4% vs. 87.4%). CONCLUSION: Our study developed a preliminary prediction model based on two different ML approaches to help predict frailty risk in the elderly. IMPACT: The presented models from this study can be used to inform healthcare providers to predict the frailty probability among older adults and maybe help guide the development of effective frailty risk management interventions. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Detecting frailty at an early stage and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce frailty-related burden. Identifying risk factors for frailty could be beneficial to provide tailored and personalized care intervention for older adults to more accurately prevent or improve their frail conditions so as to improve their quality of life. REPORTING METHOD: The study has adhered to STROBE guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Developing a model describing voluntary residency attrition: a qualitative study.

BACKGROUND: Many factors influencing residency attrition are identified in the literature, but what role these factors play and how they influence each other remains unclear. Understanding more about the interaction between these factors can provide background to put the available evidence into perspective and provide tools to reduce attrition. The aim of this study was therefore to develop a model that describes voluntary residency attrition. METHODS: Semi-structured interviews were held with a convenient sample of orthopaedic surgery residents in the Netherlands who dropped out of training between 2000 and 2018. Transcripts were analysed using a constructivist grounded theory approach. Concepts and themes were identified by iterative constant comparison. RESULTS: Seventeen interviews with former residents were analysed and showed that reasons for voluntary attrition were different for each individual and often a result of a cumulative effect. Individual expectations and needs determine residents' experiences with the content of the profession, the professional culture and the learning climate. Personal factors like previous clinical experiences, personal circumstances and personal characteristics influence expectations and needs. Specific aspects of the residency programme contributing to attrition were type of patient care, required skills for the profession, work-life balance and interpersonal interaction. CONCLUSIONS: This study provides a model for voluntary resident attrition showing the factors involved and how they interact. This model places previous research into perspective, gives implications for practice on the (im)possibilities of preventing attrition and opens possibilities for further research into resident attrition.

Porcine model of a complicated skin and soft tissue infection caused by Pseudomonas aeruginosa.

Pseudomonas aeruginosa poses a significant threat to both immunocompetent and immunocompromised individuals, often resulting in life-threatening infections. With increasing antimicrobial resistance, novel therapeutic strategies are urgently needed. Although animal models are crucial for preclinical studies, limited data are available for porcine models, more specifically for P. aeruginosa complicated skin and soft tissue infections (cSSTIs). This study presents a novel porcine model inducing and sustaining cSSTI for 14 days. Six pigs (120 wounds) were used for the development of infections, and within this group, two pigs (40 wounds) were used to evaluate the progression of the cSSTI infection. The model demonstrated bacterial loads of more than 107 CFU/gram of tissue or higher. The cSSTI fully developed within three days and remained well above these levels until day 14 post-infection. Due to the immunocompetence of this model, all the immunological processes associated with the response to the presence of infection and the wound healing process are preserved.

Preclinical testing of antimicrobials for cystic fibrosis lung infections: current needs and future priorities.

A workshop was held by the PIPE-CF strategic research centre to consider preclinical testing of antimicrobials for cystic fibrosis (CF). The workshop brought together groups of people from the CF community to discuss current challenges and identify priorities when developing CF therapeutics. This paper summarizes the key points from the workshop from the different sessions, including talks given by presenters on the day and round table discussions. Currently, it is felt that there is a large disconnect throughout the community, with communication between patients, clinicians and researchers being the main issue. This leads to little consideration being given to factors such as treatment regimes, routes of administration and side effects when developing new therapies, that could alter the day-to-day lifestyles of people living with CF. Translation of numerical data that are obtained in the laboratory to successful outcomes of clinical trials is also a key challenge facing researchers today. Laboratory assays in preclinical testing involve basing results on bacterial clearance and decrease in viable cells, when these are not factors that are considered when determining the success of a treatment in the clinic. However, there are several models currently in development that seek to tackle some of these issues, such as the organ-on-a-chip technology and adaptation of a hollow-fibre model, as well as the development of media that aim to mimic the niche environments of a CF respiratory tract. It is hoped that by summarizing these opinions and discussing current research, the communication gap between groups can begin to close.

Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.

BACKGROUND: We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19. METHODS: We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including >40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 h. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration. RESULTS: Overall, 3706 patients (2587 in the development and 1119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79-0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78. CONCLUSIONS: Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors.

Falls and fracture risk screening in primary care: update and validation of a postal screening tool for community dwelling older adults recruited to UK Prevention of Falls Injury Trial (PreFIT).

BACKGROUND: Postal screening has not previously been validated as a method for identifying fall and fracture risk in community-dwelling populations. We examined prognostic performance of a postal risk screener used in the UK Prevention of Falls Injury Trial (PreFIT; ISRCTN71002650), to predict any fall, recurrent falls, and fractures over 12 months. We tested whether adding variables would improve screener performance. METHODS: Nine thousand eight hundred and eight community-dwelling participants, aged 70 years and older, and 63 general practices in the UK National Health Service (NHS) were included in a large, pragmatic cluster randomised trial comparing screen and treat fall prevention interventions. The short postal screener was sent to all participants in the trial intervention arms as an A4 sheet to be completed and returned to the GP (n = 6,580). The postal screener items were embedded in the baseline pre-randomisation postal questionnaire for all arms of the trial (n = 9,808). We assessed discrimination and calibration using area under the curve (AUC). We identified additional predictors using data from the control arm and applied these coefficients to internal validation models in the intervention arm participants. We used logistic regression to identify additional predictor variables. FINDINGS: A total of 10,743 falls and 307 fractures were reported over 12 months. Over one third of participants 3,349/8,136 (41%) fell at least once over 12 month follow up. Response to the postal screener was high (5,779/6,580; 88%). Prediction models showed similar discriminatory ability in both control and intervention arms, with discrimination values for any fall AUC 0.67 (95% CI 0.65 to 0.68), and recurrent falls (AUC 0.71; 95% CI 0.69, 0.72) but poorer discrimination for fractures (AUC 0.60; 95% CI 0.56, 0.64). Additional predictor variables improved prediction of falls but had modest effect on fracture, where AUC rose to 0.71 (95% CI 0.67 to 0.74). Calibration slopes were very close to 1. CONCLUSION: A short fall risk postal screener was acceptable for use in primary care but fall prediction was limited, although consistent with other tools. Fracture and fall prediction were only partially reliant on fall risk although were improved with the additional variables.

A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study.

BACKGROUND: Natural language processing (NLP) models such as bidirectional encoder representations from transformers (BERT) hold promise in revolutionizing disease identification from electronic health records (EHRs) by potentially enhancing efficiency and accuracy. However, their practical application in practice settings demands a comprehensive and multidisciplinary approach to development and validation. The COVID-19 pandemic highlighted challenges in disease identification due to limited testing availability and challenges in handling unstructured data. In the Netherlands, where general practitioners (GPs) serve as the first point of contact for health care, EHRs generated by these primary care providers contain a wealth of potentially valuable information. Nonetheless, the unstructured nature of free-text entries in EHRs poses challenges in identifying trends, detecting disease outbreaks, or accurately pinpointing COVID-19 cases. OBJECTIVE: This study aims to develop and validate a BERT model for detecting COVID-19 consultations in general practice EHRs in the Netherlands. METHODS: The BERT model was initially pretrained on Dutch language data and fine-tuned using a comprehensive EHR data set comprising confirmed COVID-19 GP consultations and non-COVID-19-related consultations. The data set was partitioned into a training and development set, and the model's performance was evaluated on an independent test set that served as the primary measure of its effectiveness in COVID-19 detection. To validate the final model, its performance was assessed through 3 approaches. First, external validation was applied on an EHR data set from a different geographic region in the Netherlands. Second, validation was conducted using results of polymerase chain reaction (PCR) test data obtained from municipal health services. Lastly, correlation between predicted outcomes and COVID-19-related hospitalizations in the Netherlands was assessed, encompassing the period around the outbreak of the pandemic in the Netherlands, that is, the period before widespread testing. RESULTS: The model development used 300,359 GP consultations. We developed a highly accurate model for COVID-19 consultations (accuracy 0.97, F1-score 0.90, precision 0.85, recall 0.85, specificity 0.99). External validations showed comparable high performance. Validation on PCR test data showed high recall but low precision and specificity. Validation using hospital data showed significant correlation between COVID-19 predictions of the model and COVID-19-related hospitalizations (F1-score 96.8; P<.001; R2=0.69). Most importantly, the model was able to predict COVID-19 cases weeks before the first confirmed case in the Netherlands. CONCLUSIONS: The developed BERT model was able to accurately identify COVID-19 cases among GP consultations even preceding confirmed cases. The validated efficacy of our BERT model highlights the potential of NLP models to identify disease outbreaks early, exemplifying the power of multidisciplinary efforts in harnessing technology for disease identification. Moreover, the implications of this study extend beyond COVID-19 and offer a blueprint for the early recognition of various illnesses, revealing that such models could revolutionize disease surveillance.

The SPN Pediatric Nursing Excellence Model: Differentiating pediatrics.

The question of what makes an "excellent" pediatric nurse has been asked frequently by both pediatric and non-pediatric nurses for many years. Longevity in the practice setting, increased formal education in the care of children and families, positive satisfaction surveys post encounter, quantity of professional presentations and publications, and specialty certification are often listed when discussing pediatric nursing excellence. However, pediatric nursing excellence (PNE) is not well defined. Current recognition mechanisms such as clinical ladders, Magnet© and Pathways© programs, and Benner's stages of clinical competence are not specific for pediatric nursing practice. Once the characteristics of pediatric nursing excellence are determined, they can be used as the basis for identifying pediatric-specific quality indicators. In 2020, SPN initiated a project to define the construct of "pediatric nursing excellence". Two years later, SPN published its Pediatric Nursing Excellence Model, consisting of a visual depiction accompanied by definitions of 16 concepts that comprise the PNE Model. This article presents the five stages of the development process, the components of a model of pediatric nursing excellence, and the potential uses of such a model.

A unified modelling framework for type I (discrete) settling and rising of microplastics in primary sedimentation tanks.

Sewage treatment plants (STPs) are considered as a significant source of microplastic pollution into the terrestrial and aquatic environment. Existing observations suggest that primary treatment accounts for major microplastics removal in STPs, though with high variability due to the complex nature of the polymer compositions, abundance, and sizes in the incoming sewage. Here, we develop a unified modelling framework to simulate the Type I (or discrete) settling or rising behaviour of microplastics to predict their eventual fate in Primary Sedimentation Tank (PST). The model was developed as per the conventional design protocol for PST involving Stokes equation and modifications as per flow regime for settling of nylon and polystyrene microplastics. It was subsequently validated with independent column experiments for both settling (nylon and polystyrene) and rising (low-density polyethylene and polypropylene) microplastics in different size ranges. The validated model was then applied for multiple realistic scenarios of polymer compositions, relative abundance, and size distributions in the incoming sewage. The model predicts removals ranging from 12% to 94% for a mixture of microplastics in the size fraction 0-500 µm. Model simulations also suggest better microplastics removal with the integration of skimming in PST, and optimization of surface overflow velocity.

Identification of the joint line in revision total knee arthroplasty using a multiple linear regression model: a cadaveric study.

INTRODUCTION: The results of revision total knee arthroplasty (rTKA) may be compromised by excessive joint line (JL) elevation. It is critical but challenging in reestablishing the JL in rTKA. Previous studies have confirmed that, biomechanically and clinically, JL elevation should not exceed 4 mm. Image-based studies described several approaches to locate the JL intraoperatively, however magnification errors could occur. In this cadaveric study, we aim to define an accurate and reliable method to determine the JL. MATERIALS AND METHODS: Thirteen male and eleven female cadavers were used, with an average age of death being 48.3 years. The transepicondylar width (TEW), the distance from the medial (MEJL) and lateral (LEJL) epicondyle, adductor tubercle (ATJL), fibular head (FHJL) and tibial tubercle (TTJL) to the JL were measured in 48 knees. Intra- and interobserver reliability and validity were tested prior to any additional analysis. Pearson correlation and linear regression analysis were used to examine the correlations between landmark-JL distances (LEJL, MEJL, ATJL, FHJL and TTJL) and the TEW, and to further derive models for intraoperative JL determination. The accuracy of different models, quantified by errors between estimated and measured landmark-JL distances, was compared using the Friedman and post hoc Dunn tests. RESULTS: The intra- and inter-observer measurements for TEW, MEJL, LEJL, ATJL, TTJL and FHJL did not differ significantly (p > 0.05). Between genders, significant differences were found on TEW, MEJL, LEJL, ATJL, FHJL and TTJL (p < 0.05). There was no association between TEW and either FHJL or TTJL (p > 0.05), while ATJL, MEJL, and LEJL were found to be correlated with TEW (p < 0.05). Six models were derived: (1) MEJL = 0.37*TEW (r = 0.384), (2) LEJL = 0.28*TEW (r = 0.380), (3) ATJL = 0.47*TEW (r = 0.608), (4) MEJL = 0.413*TEW - 4.197 (R2 = 0.473), (5) LEJL = 0.236*TEW + 3.373 (R2 = 0.326), (6) ATJL = 0.455*TEW + 1.440 (R2 = 0.556). Errors were defined as deviations between estimated and actual landmark-JL distances. The mean absolute value of the errors, created by Model 1-6 was 3.18 ± 2.25, 2.53 ± 2.15, 2.64 ± 2.2, 1.85 ± 1.61, 1.60 ± 1.59 and 1.71 ± 1.5, respectively. The error could be limited to 4 mm in 72.9%, 83.3%, 72.9%, 87.5%, 87.5%, and 93.8% of the cases by referencing Model 1-6, respectively. CONCLUSION: Compared to previous image-based measurements, the current cadaveric study most closely resembles a realistic view of intraoperative settings and could circumvents magnification errors. We recommend using Model 6, the JL can be best estimated by referencing the AT and the ATJL can be calculated as ATJL (mm) = 0.455*TEW (mm) + 1.440 (mm).

Direct Tableting on a Continuous Manufacturing Line-Impact of Mixing Parameters, Material Densities, and Drug Load on Subsequent Process Parameters and Tablet Quality.

The continuous manufacturing (CM) of solid oral dosage forms has received increased attention in recent years and has become a leading technology in the pharmaceutical industry. A model has been developed based on process data from two design of experiments (DoEs), where the impact of the mixer process parameters, throughput (THR), hold up mass (HUM), impeller speed (IMP), and the input raw material bulk density (BDi), on the continuous process and the resulting drug product has been investigated. These statistical models revealed equations, describing process parameter interactions for optimization purposes. For the exit valve opening width (EV) at the bottom of the continuous mixer (CMT), the combination of high throughput (30 kg/h) and low impeller speed (300 rpm) resulted in optimal process conditions. Apparent bulk density of the blend (BD) within the process, fill depth (FD), and tensile strength (TS) were mainly impacted by input bulk density (BDi) of the tableting mixture, emphasizing the role of material attributes on the continuous manufacturing process. The apparent bulk density itself was, other than from the input bulk density, equally dependent from THR and IMP in opposite deflections. However, process parameters (THR and IMP) revealed a minor impact on the apparent BD compared to the input bulk density. FD was impacted mainly by THR ahead of IMP and the TS by IMP and THR to a similar extend, in opposite deflections. A simplified linear model to estimate the input bulk density revealed satisfactory prediction quality when included in the derived statistical model equations.

Recommendations for machine learning benchmarks in neuroimaging.

The field of neuroimaging has embraced methods from machine learning in a variety of ways. Although an increasing number of initiatives have published open-access neuroimaging datasets, specifically designed benchmarks are rare in the field. In this article, we first describe how benchmarks in computer science and biomedical imaging have fostered methodological progress in machine learning. Second, we identify the special characteristics of neuroimaging data and outline what researchers have to ensure when establishing a neuroimaging benchmark, how datasets should be composed and how adequate evaluation criteria can be chosen. Based on lessons learned from machine learning benchmarks, we argue for an extended evaluation procedure that, next to applying suitable performance metrics, focuses on scientifically relevant aspects such as explainability, robustness, uncertainty, computational efficiency and code quality. Lastly, we envision a collaborative neuroimaging benchmarking platform that combines the discussed aspects in a collaborative and agile framework, allowing researchers across disciplines to work together on the key predictive problems of the field of neuroimaging and psychiatry.

A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability.

There is an inherent tension in Quantitative Systems Pharmacology (QSP) between the need to incorporate mathematical descriptions of complex physiology and drug targets with the necessity of developing robust, predictive and well-constrained models. In addition to this, there is no "gold standard" for model development and assessment in QSP. Moreover, there can be confusion over terminology such as model and parameter identifiability; complex and simple models; virtual populations; and other concepts, which leads to potential miscommunication and misapplication of methodologies within modeling communities, both the QSP community and related disciplines. This perspective article highlights the pros and cons of using simple (often identifiable) vs. complex (more physiologically detailed but often non-identifiable) models, as well as aspects of parameter identifiability, sensitivity and inference methodologies for model development and analysis. The paper distills the central themes of the issue of identifiability and optimal model size and discusses open challenges.

Simulating indoor inorganic aerosols of outdoor origin with the inorganic aerosol thermodynamic equilibrium model ISORROPIA.

Outdoor aerosols can transform and have their composition altered upon transport indoors. Herein, IMAGES, a platform that simulates indoor organic aerosol with the 2-dimensional volatility basis set (2D-VBS), was extended to incorporate the inorganic aerosol thermodynamic equilibrium model, ISORROPIA. The model performance was evaluated by comparing aerosol component predictions to indoor measurements from an aerosol mass spectrometer taken during the summer and winter seasons. Since ammonia was not measured in the validation dataset, outdoor ammonia was estimated from aerosol measurements using a novel pH-based algorithm, while nitric acid was held constant. Modeled indoor ammonia sources included temperature-based occupant and surface emissions. Sensitivity to the nitric acid indoor surface deposition rate ß g , HNO 3 , g was explored by varying it in model runs, which did not affect modeled sulfate due to its non-volatile nature, though the fitting of a filter efficiency was required for good correlations of modeled sulfate with measurements in both seasons. Modeled summertime nitrate well-matched measured observations when ß g , HNO 3 , g = 2.75 h - 1 , but wintertime comparisons were poor, possibly due to missing thermodynamic processes within the heating, ventilating, and air-conditioning (HVAC) system. Ammonium was consistently overpredicted, potentially due to neglecting thirdhand smoke impacts observed in the field campaign, as well as HVAC impacts.

Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy.

BACKGROUND: Perinatal depression is estimated to affect ~ 12% of pregnancies and is linked to numerous negative outcomes. There is currently no model to predict perinatal depression at multiple time-points during and after pregnancy using variables ascertained early into pregnancy. METHODS: A prospective cohort design where 858 participants filled in a baseline self-reported survey at week 4-10 of pregnancy (that included social economics, health history, various psychiatric measures), with follow-up until 3 months after delivery. Our primary outcome was an Edinburgh Postnatal Depression Score (EPDS) score of 12 or more (a proxy for perinatal depression) assessed during each trimester and again at two time periods after delivery. Five gradient boosting machines were trained to predict the risk of having EPDS score > = 12 at each of the five follow-up periods. The predictors consisted of 21 variables from 3 validated psychometric scales. As a sensitivity analysis, we also investigated different predictor sets that contained: i) 17 of the 21 variables predictors by only including two of the psychometric scales and ii) including 143 additional social economics and health history predictors, resulting in 164 predictors. RESULTS: We developed five prognostic models: PND-T1 (trimester 1), PND-T2 (trimester 2), PND-T3 (trimester 3), PND-A1 (after delivery 1) and PND-A2 (delayed onset after delivery) that calculate personalised risks while only requiring that women be asked 21 questions from 3 validated psychometric scales at weeks 4-10 of pregnancy. C-statistics (also known as AUC) ranged between 0.69 (95% CI 0.65-0.73) and 0.77 (95% CI 0.74-0.80). At 50% sensitivity the positive predictive value ranged between 30%-50% across the models, generally identifying groups of patients with double the average risk. Models trained using the 17 predictors and 164 predictors did not improve model performance compared to the models trained using 21 predictors. CONCLUSIONS: The five models can predict risk of perinatal depression within each trimester and in two post-natal periods using survey responses as early as week 4 of pregnancy with modest performance. The models need to be externally validated and prospectively tested to ensure generalizability to any pregnant patient.

A model to assess the impact of digital technologies on the health-related quality of life.

OBJECTIVES: Health-related quality of life (HRQoL) is a vital instrument to account for individuals' well-being in various settings. However, no model of HRQoL allows for examining the effect of digital technology on HRQoL. Therefore, we extend an established HRQoL model by adding a digital technology-related construct. We refer to this extension as the technology-affected health-related quality of life (TA-HRQoL). METHODS: We investigate the extended TA-HRQoL model through a survey. In the survey, we exemplify the use of digital technology through a device for self-managing bladder dysfunction. Hence, we explore whether the model extension proposed is valid and how determinants of the HRQoL affect patients with bladder dysfunction. RESULTS: The results indicate that the use of digital technology improves the HRQoL. In our exemplary use scenario, the digital technology decreases bladder-related functional impairments and increases well-being and life satisfaction directly. CONCLUSIONS: Our study may provide evidence for the influence of digital technologies on the HRQoL, thus supporting our model extension. We consider our proposed TA-HRQoL model as valid and as useful to account for the influence of digital technology on an individual's HRQoL. With the TA-HRQoL model, the impact of a digital technology on an individual's HRQoL can be assessed.

Exploring Inductive Linearization for simulation and estimation with an application to the Michaelis-Menten model.

Nonlinear ordinary differential equations (ODEs) are common in pharmacokinetic-pharmacodynamic systems. Although their exact solutions cannot generally be determined via algebraic methods, their rapid and accurate solutions are desirable. Thus, numerical methods have a critical role. Inductive Linearization was proposed as a method to solve systems of nonlinear ODEs. It is an iterative approach that converts a nonlinear ODE into a linear time-varying (LTV) ODE, for which a range of standard integration techniques can then be used to solve (e.g., eigenvalue decomposition [EVD]). This study explores the properties of Inductive Linearization when coupled with EVD for integration of the LTV ODE and illustrates how the efficiency of the method can be improved. Improvements were based on three approaches, (1) incorporation of a convergence criterion for the iterative linearization process (for simulation and estimation), (2) creating more efficient step sizes for EVD (for simulation and estimation), and (3) updating the initial conditions of the Inductive Linearization (for estimation). The performance of these improvements were evaluated using single subject stochastic simulation-estimation with an application to a simple pharmacokinetic model with Michaelis-Menten elimination. The reference comparison was a standard non-stiff Runge-Kutta method with variable step size (ode45, MATLAB). Each of the approaches improved the speed of the Inductive Linearization technique without diminishing accuracy which, in this simple case, was faster than ode45 with comparable accuracy in the parameter estimates. The methods described here can easily be implemented in standard software programme such as R or MATLAB. Further work is needed to explore this technique for estimation in a population approach setting.

Modeling the association between socioeconomic features and risk of flood damage: A local-scale case study in Sri Lanka.

Floods cause severe damage to people as well as to properties. The same flood can cause different levels of damage to different households, but investigations into floods tend to be conducted on regional and national scales, thereby missing these local variations. It is therefore necessary to understand individual experiences of flood damage to implement effective flood management strategies on a local scale. The main objectives of this study were to develop a model that represents the relationship between socioeconomic conditions and flood damage at a local scale, and to understand the socioeconomic factors most closely tied to flood damage. The analysis is novel in that it considers not only the impact of flood characteristics, but also the impact of social, economic, and geographic factors on flood damage. This analysis derives from a quantitative modeling approach based on community responses, with the responses obtained through questionnaire surveys that consider four consecutive floods of differing severity. Path analysis was used to develop a model to represent the relationships between these factors. A randomly selected sample of 150 data points was used for model development, and nine random samples of 150 data points were used to validate the model. Results suggest that poor households, located in vulnerable, low-lying areas near rivers, suffer the most from being exposed to frequent, severe floods. Further, the results show that the socioeconomic factors with the most significant bearing on flood damage are per capita income and geographic location of the household. The results can be represented as a cycle, showing that social, economic, geographic, and flood characteristics are interrelated in ways that influence flood damage. This empirical analysis highlights a need for local-scale flood damage assessments, as offered in this article but seldom seen in other relevant literature. Our assessment was achieved by analyzing the impact of socioeconomic and geographic conditions and considering the relationship between flood characteristics and flood damage.

A Simulation-Based Approach to Aid Development of Software-Based Hardware Failure Detection and Mitigation Algorithms of a Mobile Robot System.

Mechatronic systems, like mobile robots, are fairly complex. They are composed of electromechanical actuation components and sensing elements supervised by microcontrollers running complex embedded software. This paper proposes a novel approach to aid mobile robotics developers in adopting a rigorous development process to design and verify the robot's detection and mitigation capabilities against random hardware failures affecting its sensors or actuators. Unfortunately, assessing the interactions between the various safety/mission-critical subsystem is quite complex. The failure mode effect analysis (FMEA) alongside an analysis of the failure detection capabilities (FMEDA) are the state-of-the-art methodologies for performing such an analysis. Various guidelines are available, and the authors decided to follow the one released by AIAG&VDA in June 2019. Since the robot's behavior is based on embedded software, the FMEA has been integrated with the hardware/software interaction analysis described in the ECSS-Q-ST-30-02C manual. The core of this proposal is to show how a simulation-based approach, where the mechanical and electrical/electronic components are simulated alongside the embedded software, can effectively support FMEA. As a benchmark application, we considered the mobility system of a proof-of-concept assistance rover for Mars exploration designed by the D.I.A.N.A. student team at Politecnico di Torino. Thanks to the adopted approach, we described how to develop the detection and mitigation strategies and how to determine their effectiveness, with a particular focus on those affecting the sensors.