Gaussian Process Based Model to Optimize Additively Manufactured Powder Microstructures From Phase Field Modeling

A persistent problem in the selective laser sintering process is to maintain the quality of additively manufactured parts, which can be attributed to the various sources of uncertainty. In this work, a two-particle phase-field microstructure model has been analyzed. The sources of uncertainty as the two input parameters were surface diffusivity and inter-particle distance. The response quantity of interest (QOI) was selected as the size of the neck region that develops between the two particles. Two different cases with equal and unequal sized particles were studied. It was observed that the neck size increased with increasing surface diffusivity and decreased with increasing inter-particle distance irrespective of particle size. Sensitivity analysis found that the inter-particle distance has more influence on variation in neck size than that of surface diffusivity. The machine learning algorithm Gaussian Process Regression was used to create the surrogate model of the QOI. Bayesian Optimization method was used to find optimal values of the input parameters. For equal-sized particles, optimization using Probability of Improvement provided optimal values of surface diffusivity and inter-particle distance as 23.8268 and 40.0001, respectively. The Expected Improvement as an acquisition function gave optimal values 23.9874 and 40.7428, respectively. For unequal sized particles, optimal design values from Probability of Improvement were 23.9700 and 33.3005, respectively, while those from Expected Improvement were 23.9893 and 33.9627, respectively. The optimization results from the two different acquisition functions seemed to be in good agreement.

AB0431 EXPLORING THE UTILITY OF THE AMERICAN COLLEGE OF RHEUMATOLOGY (ACR) CRISS IN PATIENTS WITH DIFFUSE CUTANEOUS SYSTEMIC SCLEROSIS

Background:The ACR Composite Response Index in diffuse cutaneous Systemic Sclerosis (CRISS) was developed to measure the probability of improvement in response to treatment in patients with early diffuse cutaneous SSc (dcSSc), accounting for new/worsening cardiopulmonary involvement and/or renal crisis, and changes in modified Rodnan skin score, forced vital capacity, health assessment questionnaire disability index, and patient’s and physician’s global impressions. In patients with SSc-ILD, treatment response may be reflected as slower progression, stabilisation or improvement.Objectives:Using data from patients with dcSSc and ILD in the placebo group of the SENSCIS trial, we analysed the probability of improvement using the ACR CRISS score at week 52. We also evaluated whether the CRISS numerator could provide information on the spectrum of responses in this patient population.Methods:The SENSCIS trial enrolled subjects with SSc-ILD with onset of first non-Raynaud symptom ≤7 years before screening, FVC ≥40% predicted, and fibrotic ILD ≥10% extent on an HRCT scan. Subjects on prednisone ≤10 mg/day (or equivalent) and/or stable therapy with mycophenolate or methotrexate were allowed to participate. Subjects were randomised to receive nintedanib or placebo. Subjects were not randomised by use of mycophenolate. In patients randomised to receive placebo who had dcSSc and/or mRSS >15 at baseline, we analysed the ACR CRISS and its numerator at week 52 in subgroups by use of mycophenolate at baseline. Analyses were exploratory and descriptive.Results:Of 117 analysed subjects in the placebo group who had dcSSc and/or mRSS >15 at baseline, 60 (51.3%) were taking mycophenolate at baseline. Compared with patients not taking mycophenolate at baseline, those taking mycophenolate had a lower mean age (48.4 [SD 11.8] vs 53.1 [13.4] years), lower mean FVC % predicted (68.8 [17.0] vs 73.0 [14.6]), and a greater proportion were female (76.7% vs 71.9%); median time since first onset of non-Raynaud symptom was similar (3.9 vs 4.5 years, respectively) as was mean (SD) mRSS (16.5 [7.7] vs 15.9 [8.0], respectively). One patient (taking mycophenolate at baseline) had limited cutaneous SSc. At week 52, median (Q1, Q3) ACR CRISS score was 0.036 (0.001, 0.601) in subjects taking mycophenolate and 0.002 (0.000, 0.112) in subjects not taking mycophenolate at baseline, and mean (SD) ACR CRISS score was 0.28 (0.37) in subjects taking mycophenolate and 0.16 (0.31) in subjects not taking mycophenolate at baseline (Figure 1). In these groups, respectively, 25.0% and 14.0% of subjects had CRISS score >0.6 (considered improved) at week 52. The CRISS numerator provided a broader distribution of response values, but was not informative in this patient population.Conclusion:In exploratory analyses, among subjects with dcSSc and ILD who received placebo in the SENSCIS trial, the proportion considered improved at week 52 based on ACR CRISS score was numerically greater in patients taking than not taking mycophenolate at baseline. There remains a need for composite scores that provide better interpretation of the magnitude of response in patients with SSc.Acknowledgements:The SENSCIS trial was funded by Boehringer Ingelheim. Medical writing support was provided by FleishmanHillard Fishburn, London, UK. The authors meet criteria for authorship as recommended by the International Committee of Medical Journal Editors (ICMJE).Disclosure of Interests:Francesco Del Galdo Speakers bureau: Actelion and AstraZeneca, Consultant of: Actelion, AstraZeneca, Boehringer Ingelheim, Capella BioScience, ChemomAb and Mitsubishi Tanabe Pharma, Grant/research support from: Capella BioScience, Kymab and Mitsubishi Tanabe Pharma, Oliver Distler Consultant of: AbbVie, Acceleron Pharma, Amgen, AnaMar, Arxx Therapeutics, Baecon Discovery, Bayer, Blade Therapeutics, Boehringer Ingelheim, ChemomAb, Corbus, CSL Behring, Galapagos NV, GlaxoSmithKline, Glenmark Pharmaceuticals, Horizon (Curzion) Pharmaceuticals, Inventiva, IQVIA, Italfarmaco, iQone, Kymera Therapeutics, Lilly, Medac, Medscape, Merck Sharp & Dohme, Mitsubishi Tanabe Pharma, Novartis, Pfizer, Roche, Sanofi, Serodapharm, Target Bioscience, Topadur Pharma and UCB, Grant/research support from: Kymera Therapeutics and Mitsubishi Tanabe Pharma, Christopher Denton Speakers bureau: Boehringer Ingelheim, Corbus, Janssen, and Mallinckrodt Pharmaceuticals, Consultant of: Acceleron Pharma, Arxx Therapeutics, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, Galapagos NV, GlaxoSmithKline, Horizon Therapeutics, Janssen, Mallinckrodt Pharmaceuticals, Roche, Sanofi and UCB, Grant/research support from: Arxx Therapeutics, GlaxoSmithKline and Servier, Yannick Allanore Consultant of: Boehringer Ingelheim, Medsenic, Menarini and Sanofi, Grant/research support from: Alpine Pharmaceuticals, Daniel Wachtlin Employee of: Currently an employee of Boehringer Ingelheim, Margarida Alves Employee of: Currently an employee of Boehringer Ingelheim, Dinesh Khanna Shareholder of: Eicos Sciences, Inc. (less than 5%), Consultant of: Acceleron Pharma, Actelion, AbbVie, Amgen, Bayer, Boehringer Ingelheim, CSL Behring, Corbus, Gilead Sciences, Galapagos NV, Genentech/Roche, GlaxoSmithKline, Horizon Therapeutics, Merck, Mitsubishi Tanabe Pharma, Sanofi-Aventis and United Therapeutics, Grant/research support from: Bayer, Bristol-Myers Squibb, Horizon Therapeutics, Immune Tolerance Network, National Institutes of Health and Pfizer, Employee of: Chief Medical Officer- CiviBioPharma/Eicos Sciences, Inc.

Accessible Virtual Arts Recreation for Wellbeing Promotion in Long-Term Care Residents

The efficacy of a technology-driven visual arts recreation activity, delivered virtually, was evaluated for its potential to achieve positive impacts, similar to traditional arts-interventions, on wellbeing in long-term care residents. Thirty-one residents (average age 86.8 years; SD = 9.4) engaged with the arts-intervention for 30-minutes, twice weekly, for 6 weeks with either a partner or as part of a group. Wellbeing indicators included self-reported psychological and health-related wellness, and attention capacity. Binomial tests of postintervention change revealed a significant above-chance probability of improvement in one or more wellbeing indicators ( p < .05). Postparticipation feedback survey scores were positive ( p < .05). Cognitive status did not influence outcome; however, other participant characteristics such as younger age, higher openness-to-experience (personality trait), and lower baseline mood were significantly associated with positive response to the intervention ( p < .05). Findings demonstrate technology may be an effective platform for promoting accessibility to beneficial arts-interventions for older adults.

Bayesian Optimization using Pseudo-Points

Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, and robotics. BO usually models the objective function by a Gaussian process (GP), and iteratively samples the next data point by maximizing an acquisition function. In this paper, we propose a new general framework for BO by generating pseudo-points (i.e., data points whose objective values are not evaluated) to improve the GP model. With the classic acquisition function, i.e., upper confidence bound (UCB), we prove that the cumulative regret can be generally upper bounded. Experiments using UCB and other acquisition functions, i.e., probability of improvement (PI) and expectation of improvement (EI), on synthetic as well as real-world problems clearly show the advantage of generating pseudo-points.

Prevalence of disability improvement as a potential outcome for multiple sclerosis trials

Background: The concept of improvement of disability recently emerged as a new target in multiple sclerosis (MS) studies since the approval of new potent drugs and for testing drugs for neuroprotection and repair. Objective: To propose a simple estimator for assessing and comparing the prevalence of improvement over time between groups. Methods: The prevalence of a transient condition takes into account the incidence and the duration of such condition. We propose here the application of a modified Kaplan–Meier estimator to evaluate and compare between groups the prevalence of improvement over time in a cohort of 121 patients treated with autologous hematopoietic stem cell transplantation. Results: The prevalence of improvement after 5 years from transplant was 50.3% (95%CI: [38.0–63.0]) in relapsing–remitting patients and 6.5% (95%CI: [0–17.8]) in secondary-progressive patients ( p < 0.001). Such a difference wouldn’t be evident considering the traditional cumulative probability of improvement at 5 years (55.5% in relapsing–remitting vs 33.4% in secondary-progressive patients, p = 0.10). Conclusion: This study shows the relevance of a new estimator of prevalence of improvement in MS. This estimator gives simple information on whether a drug can induce a durable improvement in disability and can be considered a potential outcome for trials assessing drugs for neuroprotection or repair.

Bayesian Optimization for Parameter of Discrete Weibull Regression

This study aim at optimizing the parameter θ of Discrete Weibull (DW) regression obtained by maximizing the likelihood function. Also to examine the strength of three acquisition functions used in solving auxiliary optimization problem. The choice of Discrete Weibull regression model among other models for fitting count data is due to its robustness in fitting count data. Count data of hypertensive patients visits to the doctor was obtained at Medicare Clinics Ota, Nigeria, and was used for the analysis. First, parameter θ and β were obtained using Metropolis Hasting Monte Carlo Markov Chain (MCMC) algorithm. Then Bayesian optimization was used to optimize the parameter the likelihood function of DW regression, given β to examine what θ would be, and making the likelihood function of DW the objective function. Upper confidence bound (UCB), Expectation of Improvement (EI), and probability of Improvement (PI) were used as acquisition functions. Results showed that fitting Bayesian DW regression to the data, there is significant relationship between the response variable, β and the covariate. On implementing Bayesian optimization to obtain parameter new parameter θ of discrete Weibull regression using the known β, the results showed promising applicability of the technique to the model, and found that EI fits the data better relative to PI and UCB in terms of accuracy and speed.

A Fast-Converging Ensemble Infilling Approach Balancing Global Exploration and Local Exploitation: The Go-Inspired Hybrid Infilling Strategy

Infilling strategies have been proposed for decades and are widely used in engineering problems. It is still challenging to achieve an effective trade-off between global exploration and local exploitation. In this paper, a novel decision-making infilling strategy named the Go-inspired hybrid infilling (Go-HI) strategy is proposed. The Go-HI strategy combines multiple individual infilling strategies, such as the mean square error (MSE), expected improvement (EI), and probability of improvement (PoI) strategies. The Go-HI strategy consists of two major parts. In the first part, a tree-like structure consisting of several subtrees is built. In the second part, the decision value for each subtree is calculated using a cross-validation (CV)-based criterion. Key factors that significantly influence the performance of the Go-HI strategy, such as the number of component infilling strategies and the tree depth, are explored. Go-HI strategies with different component strategies and tree depths are investigated and also compared with four baseline adaptive sampling strategies through three numerical functions and one engineering case. Results show that the number of component infilling strategies exerts a larger influence on the global and local performance than the tree depth; the Go-HI strategy with two component strategies performs better than the ones with three; the Go-HI strategy always outperforms the three component infilling strategies and the other four benchmark strategies in global performance and robustness and saves much computational cost.

Fiscal Consolidations in Polish Local Governments in the Period 2008–2016: Course and Causes

The article presents strategies of fiscal consolidations conducted in Poland in cities with powiat rights, and a study of causes behind the decision to conduct such fiscal consolidations. Local governments showing an improvement of their public finances have been distinguished; and based on that, a description of various methods of reduction of expenditures, deficit or debt in Polish local governments has been presented. An analysis of data shows the dominance of consolidations on the revenue side. The study has identified few cases of expenditure cuts, and they mostly included investments, public transport and housing expenditures. Besides the description of data concerning fiscal consolidations, the article also presents a multivariate probit regression model and logistic regression model estimated based on panel data aimed at studying the determinants of conducting financial consolidations in a given local government. The hypotheses that macroeconomic variables and the election cycle have a statistically significant effect on the decision to conduct consolidation have been positively verified; whereas the hypothesis that other political factors significantly affect the probability of improvement of the financial situation of a local government have been verified negatively.