Robust Formulation Research Articles

Product development and quality of pharmacy compounded chenodeoxycholic acid capsules for Dutch cerebrotendinous xanthomatosis patients.

Introduction: In 2017 the drug chenodeoxycholic acid (CDCA) became unavailable to Dutch patients with the rare inborn error of metabolism cerebrotendinous xanthomatosis (CTX). This was a direct result of a steep price increase after CDCA was authorized in the EU as an orphan drug. As a result, Dutch health insurance companies were unable to reimburse this drug and the availability of CDCA to patients with CTX was directly at risk creating an unmet medical need. CTX is characterized by juvenile cataract, tendon xanthomas, infantile-onset diarrhea, psychomotor retardation and progressive cerebellar ataxia. Treatment with CDCA, when initiated before neurological symptoms are present, can prevent the onset of neurological complications. Methods: To assure continuation of patient treatment with a high quality product, the hospital pharmacy of the Amsterdam UMC developed CDCA capsules as a pharmacy preparation. A simple and robust formulation was developed for capsules in a broad dose range of 35-250mg, ensuring that both pediatric and adult patients can receive an exact dose tailored to their specific needs. Capsules are prepared manually on a small scale for the individual patient. To assure the quality of the product, product validation and stability studies were performed. Results: The results show that the product complies with all specifications based on the requirements of the European Pharmacopoeia. The capsules contain the declared amount of CDCA, no degradation product or other (microbiological) impurities are formed during the production process and the capsules show a quick dissolution profile. Stability studies indicate that it is a stable product and no impurities increase or arise over time. These results show that these pharmacy preparations are of high quality and comply to Good Manufacturing Practice (GMP) requirements. Discussion: Through our research, we have demonstrated that pharmacy compounding can be a viable alternative in situations where immediate access to essential medication is crucial or when certain drugs are temporarily inaccessible. The purpose of this paper is to offer comprehensive guidance to other pharmacies to improve the availability of currently inaccessible drugs through the practice of pharmacy compounding, thereby facilitating improved patient care.

A new design method for stiffened plate based on topology optimization with min-max length-scale control

Stiffened plates are widely used in engineering due to their excellent manufacturing and mechanical properties. This paper introduces a novel method for designing stiffener plates that combines the H-DGTP formulation, robust topology optimization formulation, and maximum length-scale control. In comparison to existing methods, the proposed approach not only provides a clear layout of stiffeners but also optimizes their height. Sensitivity analysis of all design variables is derived for utilization with gradient-based optimizers. The study demonstrates that the implementation of the robust filter approach enables precise control of both structural features and gap widths, effectively avoiding sharp angles. Moreover, as the maximum length approaches the minimum length, the stiffeners assume uniform thickness, which better meets engineering requirements. Numerical examples are presented to validate the effectiveness of the proposed method.

Topology Optimization of Compliant Mechanisms Considering Manufacturing Uncertainty, Fatigue, and Static Failure Constraints

This study presents a new robust formulation for the topology optimization of compliant mechanisms, addressing the design challenges while considering manufacturability, static strength, and fatigue failure. A three-field density projection is implemented to control the minimum size of both real-phase and null-phase material structures to meet the manufacturing process requirements. The static strength is evaluated via the sum of the amplitude and the mean absolute value of the signed von Mises stress. The fatigue failure is solved via the modified Goodman criterion. The real output displacement is optimized by adding artificial springs to the prescribed value. This approach is implemented based on an improved solid isotropic material with penalization (SIMP) interpolation method to describe and solve the optimization model and derive the shape sensitivity of the optimization problem. Finally, two numerical examples are applied to illustrate the effectiveness of the presented method.

Constrained optimization of offshore wind turbine positions under uncertain wind conditions with correlated data

Offshore wind power production is subject to substantial uncertainty due to variability in wind conditions. Planning of new wind parks should take these uncertainties into account by means of stochastic modeling and uncertainty quantification. Wind speed and wind direction exhibit dependence that needs to be properly modeled to yield reliable uncertainty estimates. In particular, the dependence is strong when data are averaged over a short time-horizon, e.g., on a monthly rather than an annual basis. In this work we introduce a stochastic model for wind speed and wind direction using Rosenblatt transformation and an empirical model. This allows efficient numerical quadrature that replaces thousands of Monte Carlos samples by a few tens of model evaluations at quadrature points in parametric space. A robust design formulation for maximizing power production honoring dependent data is proposed and demonstrated on data from the North Sea site Sørlige Nordsjø II.

Comparison of the Immune Responses to Different Formulations of BC02-Adjuvanted HPV Types 16 and 18 Bivalent Vaccines in Mice.

To achieve maximum efficacy, vaccines, such as subunit, recombinant, and conjugate vaccines, necessitate the incorporation of immunostimulators/adjuvants. Adjuvants play a vital role in bolstering and extending the strength of the immune response while also influencing its type. As antigen and adjuvant formulations become more intricate, it becomes imperative to establish a well-characterized and robust formulation to ensure consistent and reproducible outcomes in preclinical and clinical studies. In the present study, an HPV bivalent vaccine was developed using a BC02 adjuvant in conjunction with HPV 16 and 18 L1 VLP antigens produced from an E. coli expression system. The study involved evaluating the adjuvant formulation and in vivo immunogenicity in mice. Remarkably, a medium-dose of BCG-CpG-DNA combined with a low-dose of aluminum hydroxide substantially enhanced the immunogenicity of HPV16 and 18 VLPs, resulting in improved cellular and humoral immune responses.

Acquisition of temporal patterns from electronic health records: an application to multimorbid patients

BackgroundThe exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illnesses, detect drug adverse reactions, and more. This paper presents an approach for the extraction of patient evolution patterns from electronic health records written in Catalan and/or Spanish.MethodsWe propose a robust formulation for extracting Temporal Association Rules (TARs) that goes beyond simple rule extraction by considering the sequence of multiple visits. Our highly configurable algorithm leverages this formulation to extract Temporal Association Rules from sequences of medical instances. We can generate rules in the desired format, content, and temporal factors while accounting for different levels of abstraction of medical instances. To demonstrate the effectiveness of our methodology, we applied it to extract patient evolution patterns from clinical histories of multimorbid patients suffering from heart disease and stroke who visited Primary Care Centers (CAP) in Catalonia. Our main objective is to uncover complex rules with multiple temporal steps, that comprise a set of medical instances.ResultsAs we are working with real-world, error-prone data, we propose a process of validation of the results by expert practitioners in primary care. Despite our limited dataset, the high percentage of patterns deemed correct and relevant by the experts is promising. The insights gained from these patterns can inform preventive measures and help detect risk factors, ultimately leading to better treatments and outcomes for patients.ConclusionOur algorithm successfully extracted a set of meaningful and relevant temporal patterns, especially for the specific type of multimorbid patients considered. These patterns were evaluated by experts and demonstrated the ability to predict risk factors that are commonly associated with certain diseases. Moreover, the average time gap between the occurrence of medical events provided critical insight into the term of these risk factors. This information holds significant value in the context of primary healthcare and preventive medicine, highlighting the potential of our method to serve as a valuable medical tool.

Robust optimization for spread quality and shortfall in guaranteed targeted display advertising planning

The problem addressed in this paper is the impression allocation planning of Guaranteed Targeted Display Advertising (GTDA) problem in online display advertising market. In this problem, a publisher desires (1) an even spread of impressions, i.e., ad exposures, across various audience segments, and (2) a maximum delivery of ads, i.e., fulfillment of the advertisers’ required impressions. For the publisher, the allocation of impressions needs to be appropriately planned to guarantee spread quality and delivery fulfillment simultaneously. The impression supplies are commonly assumed to be deterministic. In reality, however, they are stochastic due to the nature of unpredictability beforehand and variability with individual differences and social hotspots. We study a stochastic GTDA problem, and propose a stochastic programming model under uncertain impression supplies, where chance constraints are employed. Facing the challenge of data scarcity, we propose a novel distributional robust optimization formulation, which optimizes the spread quality, measured by Gini-based metrics, together with delivery shortfall minimization, under the worst-case probability distribution of impression supplies. To efficiently solve the problem, the distributionally robust chance constrained formulation is conservatively approximated by a parametric second-order cone program, for which an efficient interactive iteration heuristic approach is proposed. Computational experiments are conducted to show the advantages of our model, and effectiveness and robustness of our algorithm in comparison with several adapted state-of-the-art methods. Further managerial insights are also drawn.

A robust interface finite element formulation for modeling brittle material failure problems

AbstractFailure of many brittle materials and structures can be modeled using interface‐oriented finite elements combined with intrinsic cohesive zone models. The discontinuous Galerkin (DG) finite element method provides an innovative framework for modeling brittle crack propagation with zero‐thickness interface elements, which can accommodate extrinsic cohesive laws to avoid the artificial compliance required in intrinsic cohesive models. However, robust formulations and implementations of DG methods are critical in alleviating the well‐known convergence issues for both crack nucleation and propagation with reduced instability. This paper presents a robust interface element formulation by modifying the incomplete interior penalty Galerkin (IIPG) method, which successfully avoids the initial element interface penetration across elements that occurs prior to crack nucleation, and thereby greatly reduces the instability issue as cracks open. We further verified and validated our implementation by using a bar tension test and a beam fracturing benchmark. The robustness of our proposed interface element method was demonstrated by a micromechanics fiber/matrix debonding problem with 64 fibers embedded in a bulk matrix.

Designing a stochastic supply chain network: An error-bound-based heuristic

Designing a stochastic supply chain is challenging because demand is often partially observable or unknown in advance. This paper presents an alternative approach to address a three-echelon stochastic supply chain network design with insufficient demand information. We formulate a stochastic mixed-integer program with chance constraints. We identify a lower and upper bound on the value at risk to deal with chance constraints, thus inducing two mixed-integer linear approximations to the stochastic program. We develop a branch-and-price scheme to solve the approximations and a primal heuristic algorithm for initial columns. We then create a heuristic algorithm for solving the original problem by implementing sensitivity analysis and error bound on the approximations. Besides network size, computational experiments find that network design performance depends on aggregate service levels and maximum demand variations. The aggregate behavior of service levels has an inverse impact on the network design performance, while maximum coefficients of demand variations present a positive effect. Computational results reveal that our proposed algorithms perform well compared to normal distributions and a robust optimization formulation. Our models and algorithms suggest a feasible network design tool for handling uncertainties in supply chains.

Development and In-vitro Evaluation of Dexamethasone Enriched Nanoemulsion for Ophthalmic Indication

Background: Dexamethasone (DEX) is a glucocorticosteroid used in the treatment of steroid-responsive inflammatory conditions of the eye. The currently marketed formulations pose several issues, like poor drug residence time, resulting in frequent administration of the formulation, making them less effective. Objective: The present study aims to provide comprehensive data encompassing the designing, op-timization, development, and characterization of DEX nanoemulsion (DEX NE) for treating in-flammatory conditions of the anterior segment of the eye by employing the Quality by Design (QbD) approach. Methods: A Plackett-Burman Design (PBD) was employed to screen seven independent variables, such as oil concentration, surfactant concentration, polymer concentration, homogenization speed and time, microfluidization pressure and cycles, and their influence on critical quality attributes (CQAs), such as globule size, zeta potential, and viscosity, was evaluated. Furthermore, the Box-Behnken design (BBD) was employed for optimization, and design space was generated to obtain the optimized DEX NE. Results: The experimental results after DEX NE characterization reveal a globule size of 181 ± 90 nm with a zeta potential of -21.03 ± 1.68 mV and a viscosity of 19.99 cp. Furthermore, the drug re-lease study of simulated tear fluid demonstrated prolonged and steady release for up to 48 hr. Cyto-toxicity assay of DEX NE exhibited good cell viability. Conclusion: All these findings pave the way for a better understanding of developing a robust, safe, and non-toxic formulation for ocular drug delivery.

Fragility-based lot-sizing in veterinary pharmaceutical plants under demand uncertainty

We study a production lot-sizing problem inspired by a veterinary pharmaceutical plant in which demands are uncertain. First, we develop a deterministic capacitated lot-sizing model for the production of animal pesticides, performed in three machine-specific stages. Second, we propose a traditional robust optimisation formulation following the popular budget-of-uncertainty approach. Third, we derive a novel fragility-based approach that circumvents well-known issues with traditional robust optimisation approaches, such as the estimation of budgets of uncertainty, the over-conservatism of robust solutions and the sensitivity of solutions to the decision maker's risk attitude. The fragility-based approach is grounded in the idea of minimising violations, over the full uncertainty support, from a user-specified cost target. It avoids the estimation of budgets of uncertainty and produces less conservative solutions via explicit modelling of constraint violation. We demonstrate the effectiveness of our approach on instances built upon real data provided by our industrial partner, a major player in the Brazilian veterinary pharmaceutical sector. The results show that our fragility-based approach reduces average total costs across all instances and maintains greater model stability under different target estimations. It also preserves cost savings when bottlenecks are introduced in production and when inventory costs and capacities are varied.

Linear Source Approximation in MPACT for Efficient and Robust Multiphysics Whole-Core Simulations

This work seeks to improve upon an existing formulation of the Method of Characteristics (MOC) with a Linear Source Approximation (LSA) for problems that use nonconstant cross sections like multiphysics feedback and the two-dimensional/one-dimensional (2D/1D) formulation. The previous LSA formulation for lattice physics calculations uses precomputed coefficients that are dependent on the multigroup total or transport cross sections, and the method can be demonstrated to lack robustness when there are negative sources. In this paper, the method is reformulated to eliminate the cross-section dependence of the precomputed coefficients without adding additional operations, and a more robust formulation is also developed to prevent the calculation of negative sources. Thus, the method has increased efficiency and robustness in multiphysics and 2D/1D simulations. The new method is implemented in the MPACT code and tested on several light water reactor problems. The numerical results show that with the new Linear Source formulation, the number of mesh elements can be significantly reduced while maintaining accuracy, resulting in reduced run time and memory usage. Furthermore, our results demonstrate improved efficiency for cases with depletion, thermal-hydraulic feedback, and in three-dimensional (2D/1D) calculations without any robustness issues.

Multi-scenario and multi-stage robust NMPC with state estimation application on the Tennessee-Eastman process

This study presents the implementation of two discrete robust approaches to Non-linear Model Predictive Control (NMPC), multi-scenario NMPC (MSc-NMPC) and multi-stage NMPC (MS-NMPC), to the benchmark Tennessee-Eastman (TE) challenge, with Extended Kalman Filter (EKF) and Moving Horizon Estimation (MHE) as state estimators. The robust NMPC formulation results in closed-loop responses that prevent constraint violation and closely track the process set-point under parameter uncertainty, even in scenarios where traditional NMPC results in an unstable response for this process. Additionally, unconstrained state estimators such as EKF are unsuitable because the parameter uncertainty may cause estimates to fall outside the feasible region of the process, which ultimately destabilizes the process. MHE was able to overcome this challenge because it considers process constraints in its formulation. The additional computational time required to solve the robust NMPC formulations and MHE does not cause significant delays for the sampling time considered, demonstrating their applicability to challenging large-scale industrial chemical processes.

Modelling a bi-level multi-objective post-disaster humanitarian relief logistics network design problem under uncertainty

Post-disaster humanitarian relief logistics focuses on managing the supply and shipment of relief items. Owing to the complexity arising from the hierarchical decision relationship and the uncertainties of demand and transportation, it is challenging to develop an effective humanitarian relief logistics network (HRLN). A globalized robust bi-level multi-objective HRLN model involving uncertainties in transportation time, transportation cost and demand is developed to address this problem, in which an upper-level decision-maker focuses on the allocation problem and a lower-level decision-maker considers the supply problem. For tractability, the goal programming approach is employed to trade off conflicting timeliness, satisfaction and operational cost objectives. The proposed globalized robust bi-level HRLN model is reformulated as its globalized robust counterpart formulations under two different perturbation structures. The primal–dual approach is adopted to transform the bi-level models into equivalent mixed-integer conic programs. A case study concerning an Iranian earthquake is conducted to prove the performance of the proposed model. Globalized robust models are more flexible and able to immunize against uncertainty by comparison with traditional robust optimization and deterministic solutions. The managerial insights of using the globalized robust bi-level optimization approach are reported for disaster response management. The design of an emergency aviation network to circumvent the effects of COVID-19 could be considered in future research.

On the impact of dimensionally-consistent and physics-based inner products for POD-Galerkin and least-squares model reduction of compressible flows

Model reduction of the compressible Euler equations based on proper orthogonal decomposition (POD) and Galerkin orthogonality or least-squares residual minimization requires the selection of inner product spaces in which to perform projections and measure norms. The most popular choice is the vector-valued L2(Ω) inner product space. This choice, however, yields dimensionally-inconsistent reduced-order model (ROM) formulations which often lack robustness. In this work, we try to address this weakness by studying a set of dimensionally-consistent inner products with application to the compressible Euler equations. First, we demonstrate that non-dimensional inner products have a positive impact on both POD and Galerkin/least-squares ROMs. Second, we further demonstrate that physics-based inner products based on entropy principles result in drastically more accurate and robust ROM formulations than those based on non-dimensional L2(Ω) inner products. As test cases, we consider the following problems: the one-dimensional Sod shock tube, a two-dimensional Riemann problem, a two-dimensional Kelvin-Helmholtz instability, and two-dimensional homogeneous isotropic turbulence.

Prototype-Based Support Example Miner and Triplet Loss for Deep Metric Learning

Deep metric learning aims to learn a mapping function that projects input data into a high-dimensional embedding space, facilitating the clustering of similar data points while ensuring dissimilar ones are far apart. The most recent studies focus on designing a batch sampler and mining online triplets to achieve this purpose. Conventionally, hard negative mining schemes serve as the preferred batch sampler. However, most hard negative mining schemes search for hard examples in randomly selected mini-batches at each epoch, which often results in less-optimal hard examples and thus sub-optimal performances. Furthermore, Triplet Loss is commonly adopted to perform online triplet mining by pulling the hard positives close to and pushing the negatives away from the anchor. However, when the anchor in a triplet is an outlier, the positive example will be pulled away from the centroid of the cluster, thus resulting in a loose cluster and inferior performance. To address the above challenges, we propose the Prototype-based Support Example Miner (pSEM) and Triplet Loss (pTriplet Loss). First, we present a support example miner designed to mine the support classes on the prototype-based nearest neighbor graph of classes. Following this, we locate the support examples by searching for instances at the intersection between clusters of these support classes. Second, we develop a variant of Triplet Loss, referred to as a Prototype-based Triplet Loss. In our approach, a dynamically updated prototype is used to rectify outlier anchors, thus reducing their detrimental effects and facilitating a more robust formulation for Triplet Loss. Extensive experiments on typical Computer Vision (CV) and Natural Language Processing (NLP) tasks, namely person re-identification and few-shot relation extraction, demonstrated the effectiveness and generalizability of the proposed scheme, which consistently outperforms the state-of-the-art models.

Convex Relaxation Approaches to Robust RSS-TOA Based Source Localization in NLOS Environments

For handling unreliable datasets contaminated by the non-line-of-sight (NLOS) bias errors, this correspondence statistically robustifies the traditional least squares type hybrid received signal strength and time-of-arrival location estimator using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> and Huber loss functions. The two robust formulations are then tackled via different convex relaxation approaches. Numerical results are presented to make fair comparisons with the commonly adopted balancing parameter based scheme, demonstrating the positioning accuracy superiority of the proposed methods in various NLOS environments.

Differentiable Stripe Patterns for Inverse Design of Structured Surfaces

Stripe patterns are ubiquitous in nature and everyday life. While the synthesis of these patterns has been thoroughly studied in the literature, their potential to control the mechanics of structured materials remains largely unexplored. In this work, we introduce Differentiable Stripe Patterns---a computational approach for automated design of physical surfaces structured with stripe-shaped bi-material distributions. Our method builds on the work by Knöppel and colleagues [2015] for generating globally-continuous and equally-spaced stripe patterns. To unlock the full potential of this design space, we propose a gradient-based optimization tool to automatically compute stripe patterns that best approximate macromechanical performance goals. Specifically, we propose a computational model that combines solid shell finite elements with XFEM for accurate and fully-differentiable modeling of elastic bi-material surfaces. To resolve non-uniqueness problems in the original method, we furthermore propose a robust formulation that yields unique and differentiable stripe patterns. We combine these components with equilibrium state derivatives into an end-to-end differentiable pipeline that enables inverse design of mechanical stripe patterns. We demonstrate our method on a diverse set of examples that illustrate the potential of stripe patterns as a design space for structured materials. Our simulation results are experimentally validated on physical prototypes.

Resilience improvement model of distribution network based on two-stage robust optimization

Frequent natural disasters have a serious impact on the power system. It is urgent to ensure the normal operation of the distribution network under disaster events. This paper divides the stages of improving the resilience of the distribution network into two aspects: long-term investment planning and short-term response. We combine the decision-making process with the chronological order of natural disasters, and a N-k uncertainty set is applied to describe the uncertainty of distribution network damage. We propose a line hardening and distributed generations configuration strategy for the purpose of enhancing the resilience of distribution networks and countering extreme natural disasters. The problem is expressed as a tri-level robust optimization formulation in order to minimize the load shedding under the worst line damaged scheme. The first level corresponds to the long-term investment planning and preparation of pre-disaster stage. The second level represents the stage of line damage, and the third level matches the optimal scheduling during the post-disaster period. On the basis, this mixed integer linear programming problem is transformed into a solvable form, which is calculated by a modified Column and Constraint Generation (C&CG) algorithm. Case studies show that our proposed model can reduce load shedding to 70% of the original level, and the running time of the algorithm is less than 60 s, which verifies the model can effectively enhance the resilience of the distribution network and enhance its ability to respond to natural disasters.

Rational Design of Topical Semi-Solid Dosage Forms-How Far Are We?

Specific aspects of semi-solid dosage forms for topical application include the nature of the barrier to be overcome, aspects of susceptibility to physical and chemical instability, and a greater influence of sensory perception. Advances in understanding the driving forces of skin penetration as well as the design principles and inner structure of formulations, provide a good basis for the more rational design of such dosage forms, which still often follow more traditional design approaches. This review analyses the opportunities and constraints of rational formulation design approaches in the industrial development of new topical drugs. As the selection of drug candidates with favorable physicochemical properties increases the speed and probability of success, models for drug selection based on theoretical and experimental approaches are discussed. This paper reviews how progress in the scientific understanding of mechanisms and vehicle-influence of skin penetration can be used for rational formulation design. The characterization of semi-solid formulations is discussed with a special focus on modern rheological approaches and analytical methods for investigating and optimizing the chemical stability of active ingredients in consideration of applicable guidelines. In conclusion, the combination of a good understanding of scientific principles combined with early consideration of regulatory requirements for product quality are enablers for the successful development of innovative and robust semi-solid formulations for topical application.