Robust Formulation Research Articles

An Ecological Exploration of Addressing Child Maltreatment

This commentary examines the cases of “Bashiir” and “Jaquann” (Singer, Deboard-Lucas, & Fuentes, 2024) through an ecological lens as it pertains to addressing child maltreatment. The authors of these case studies offered a robust case formulation that provided an enhanced understanding of culturally informed treatment in cases where child maltreatment was a factor. This commentary explores ways in which the authors skillfully illustrated the theoretical tenets that shaped the treatment interventions, and offers an examination of key elements for further consideration related to child maltreatment.

Formulation and Evaluation of Immediate Release Tablets of Antimalarial Drug Hydroxychloroquine

The goal of the study was to develop and evaluate an immediate-release antimalarial tablet formulation for the effective management of malaria, an antimalarial drug as an active pharmaceutical ingredient using excipients. Appropriate pharmaceutical products and evaluate their properties in relation to the innovative product. The study was to develop an antimalarial formulation with in-vitro solubility properties equivalent to or better than the improved formulation. Therefore, an orally administered immediate-release tablet with a robust and reproducible formulation technique was developed. According to the study, the number of F3 definitions decreased the most and F8 had the most important medicinal properties. At 20 minutes, F4 appeared to lead the first high decay rate (98. 56%), followed closely by F5 (93.48%). The expansion of F1, F2 and F3 shows high decay rates at this time. Based on this information, one can analyze and compare the decay profiles of unmistakable definitions. Things such as composition, excipients used, and manufacturing geometry can influence degradation behavior, and this information may be necessary to optimize concentrations of substances of interest for drug delivery. It is then concluded that the definition number F5 may be an optimized detail.

Enhanced Euler–Lagrange Formulation for Analyzing Human Gait With Moving Base Reference

Abstract Euler–Lagrange's formulation is known for its systematic and simplified approach to deriving dynamics of complex systems. In order to apply the existing formulation to human gait dynamics, the base reference frame must be assumed as an inertial reference frame. Conventionally, the ankle joints or the hip joints are regarded as base reference frames during the stance and swing phases of human walking. As these joints are non-inertial in nature during actual locomotion, this assumption could result in inaccurate calculation of lower-limb joint torques and forces. Therefore, in this paper, an existing Euler–Lagrange-based formulation originally developed for fixed-base robotic manipulators is considered and modified to accommodate the movement of the base reference frame with respect to an inertial frame of reference defined outside the human body. The applicability of the modified formulation is studied, implemented, and validated using three standard and publicly available gait datasets covering the phases of walking and running. The joint torques obtained using the proposed dynamic model are compared with reference torques by calculating the mean absolute error values and visually through Bland–Altman plots. The obtained joint torque values and plots indicate a close agreement with published torques, thereby validating the accuracy of the proposed dynamic model. The robust formulation implementation makes it a valuable resource for researchers in this field, offering a reliable framework for gait analysis and the design of lower-limb prosthetics or exoskeletons.

Data-driven aerodynamic shape design with distributionally robust optimization approaches

We formulate and solve data-driven aerodynamic shape design problems with distributionally robust optimization (DRO) approaches. DRO aims to minimize the worst-case expected performance in a set of distributions that is informed by observed data with uncertainties. Building on the findings of the work Gotoh, et al. (2018), we study the connections between a class of DRO and robust design optimization, which is classically based on the mean–variance (standard deviation) optimization formulation pioneered by Taguchi. Our results provide a new perspective to the understanding and formulation of robust design optimization problems. It enables data-driven and statistically principled approaches to quantify the trade-offs between robustness and performance, in contrast to the classical robust design formulation that captures uncertainty only qualitatively. Our preliminary computational experiments on aerodynamic shape optimization in transonic turbulent flow show promising design results.

A Simple, Robust, and Versatile MATLAB Formulation of the Dynamic Memdiode Model for Bipolar-Type Resistive Random Access Memory Devices

Modeling in an emerging technology like RRAM devices is one of the pivotal concerns for its development. In the current bibliography, most of the models face difficulties in implementing or simulating unconventional scenarios, particularly when dealing with complex input signals. In addition, circuit simulators like Spice require long running times for high-resolution results because of their internal mathematical implementation. In this work, a fast, simple, robust, and versatile model for RRAM devices built in MATLAB is presented. The proposed model is a recursive and discretized version of the dynamic memdiode model (DMM) for bipolar-type resistive switching devices originally implemented in LTspice. The DMM model basically consists of two coupled equations: one for the current (non-linear current generator) and a second one for the memory state of the device (time-dependent differential equation). This work presents an easy-to-use tool for researchers to reproduce the experimental behavior of their devices and predict the outcome from non-trivial experiments. Three study cases are reported, aimed at capturing different phenomenologies: a frequency effect study, a cycle-to-cycle variability fit, and a stochastic resonance impact analysis.

Multi-objective shape optimization of TESLA-like cavities: Addressing stochastic Maxwell's eigenproblem constraints

In this paper, we present a robust formulation to address nonlinear equality-constrained and inequality-constrained multi-objective optimization problems for multi-cell accelerating cavities with TESLA-like geometry under consideration of uncertain parameters. We propose to explore a robust approach that employs the polynomial chaos expansion (PCE) to model the uncertainty. For the uncertainty quantification (UQ), the resulting random-dependent eigenvalue problem is solved using the stochastic collocation method (SCM) combined with PCE. As a result, the shape optimization of the studied multi-cell cavity is defined in terms of statistical moments, which serve as the cost functionals in the multi-objective (MO) steepest descent algorithm. Two versions of this method are developed and implemented. Finally, the optimization results and their implications for the operation of a multi-cell cavity are discussed.

Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review.

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.

Distributionally robust disaster relief planning under the Wasserstein set

We study a two-stage natural disaster management problem modeled as a stochastic program, where the first stage consists of a facility location problem, deciding where to open facilities and pre-allocate resources such as medical and food kits, and the second stage is a fixed-charge transportation problem, routing resources to affected areas after observing a disaster. Our model has binary variables present in both stages. Due to the lack of data, classical stochastic programming approaches may be ill-suited, and we propose a two-stage distributionally robust formulation with a Wasserstein ambiguity set, where we consider distributions consistent with historical data and a tunable parameter to control the level of risk aversion. We develop a tailored column-and-constraint generation (CCG) algorithm to solve an extensive reformulation, where scenarios are iteratively generated. We handle the presence of binary variables in the second stage by leveraging the structure of our support set and second-stage problem, and provide conditions under which the optimal value of the latter is concave with respect to the intensity of the disaster, leading to an efficient scenario generation procedure. We also show that our results extend to the case where the second stage is a fixed-charge network flow problem. We perform extensive computational experiments demonstrating the computational advantage of our method over classical CCG implementations on synthetic instances, and illustrate the benefits of our approach on a popular case study from the literature of hurricane threats on the Gulf of Mexico in the United States.

Characterisation and damage modelling of oxide/oxide composites considering temperature dependence

Continuous fibre reinforced ceramic matrix composites (CMCs) are increasingly being employed in safety critical applications. As a result, their use in these applications requires the adoption of appropriate design methodologies that are able to consider their complex non-linear mechanical behaviour. In this paper, a simple but robust formulation for continuum damage model, thermodynamically consistent and written under plane stress condition, is proposed for a laminated 2D woven composite material. This model is implemented via the Classical Laminate Theory (CLT) extended to non-linear behaviour to predict the behaviour of different stacking sequences. In-plane tensile tests were performed on an oxide/oxide composite up to 1000 °C to fully identify the model. The procedure is described and used on tests at room temperature before being extended higher temperature tests. Finally, the law and its coupling with the CLT are validated by means of tests on quasi-isotropic and cross-ply laminates at different temperatures.

Impacts of capacity redundancy and process flexibility on risk mitigation in e-waste recycling supply chain management

Recently Waste Electrical and Electronic equipment (WEEE) has become urgent environmental issues around the world. Generally, the e-waste recycling reverse supply chain (RSC) features higher uncertainties and complicated network structures, making the operation more difficult. Since the study on impact of hybrid uncertainty mitigation strategy in RSC is rare, we study the coupling effect of two strategies — capacity redundancy and process flexibility through the design of two hybrid strategies. A distributionally robust optimization model is proposed to handle both waste returns and resource yields uncertainties, and solved by a proposed Outer Approximation (OA) algorithm after an adaptive robust formulation. With numerical experiments based on real data implemented, our research finds conflict situation of using capacity redundancy and process flexibility strategies together. We also give guidelines for the use of hybrid strategies in RSC. We find that the optimal strategy changes as required service level increases. We also suggest moderate investment in capacity redundancy with long chain flexibility and resource centralization when applying hybrid strategy with optimal flexibility.

An enhanced motorcycle tyre model characterised through experimental riding data

Tyre-road interaction governs motorcycle dynamics; however, the most widespread tyre model formulations must be characterised through a dedicated test bench on the lab or road, unavailable to many interested subjects. This article proposed a new tyre model formulation, conceived to be characterised through riding data using standard instrumentation. Albeit its coefficients are identified through quasi-static, uncombined slip manoeuvres, the model addresses transient, combined manoeuvres and is adaptive to road friction levels and static weight through statistical relationships from the literature. A pre-existing formulation was improved and expanded. The model’s behaviour in demanding conditions was investigated through a high-fidelity simulation environment, using a Magic Formula tyre model as the reference. Next, the characterisation procedure was carried out using actual riding data. The model’s accuracy is shown by reproducing numerically one of the manoeuvres and through comparison with the results of a bench test. The proposed model could correctly reproduce the primary behaviour of a Magic Formula model, also concerning tyre moments and steering torque. Characterising the tyre model through real riding data proved feasible, and its robust formulation limited the propagation of estimation errors. The proposed tyre model formulation and characterisation procedure should interest, among others, those subjects that lack access to a tyre testing machine.

Towards robust and sparse linear discriminant analysis for image classification

Linear discriminant analysis (LDA) is a popular dimensionality reduction technique that has been widely used in pattern recognition. However, there exist a large number of redundant features and corrupted noise in real-world applications, which makes the performance of existing LDA methods degrade and thus leads to a decrease in classification accuracy. To address the above issues, we propose a novel robust and sparse LDA formulation dubbed RSLDA+. The key idea is introducing the mixed sparse regularization, i.e., ℓ0-norm plus ℓ2,0-norm, for feature representation and enforce ℓ0-norm for noise reduction. Furthermore, an optimization algorithm based on the alternating direction method of multipliers (ADMM) is developed in combination with hard thresholding operators. Extensive experiments on six common image datasets verify that the proposed RSLDA+ outperforms state-of-the-art LDA variants in classification accuracy. In addition, the ablation, robustness, convergence, stability, and sparsity are analyzed in detail. The results suggest that the proposed RSLDA+ provides an effective and robust method for image classification.

Forecasting Financial Crisis using Topological Data Analysis Approach

Traditional financial forecasting methods often struggle to capture the complex interactions and emerging patterns that precede financial crises. By leveraging on TDA, this research aims to uncover potential topological features that might serve as early warning signals for impending financial crises. The study adopts the utilization of Topological Data Analysis, an initiative mathematical framework to explore and analyze the inherent topological structures within financial data set, using secondary data from ”Yahoo Finance API”. The results of the analysis conducted using Python indicate that persistence homology in TDA successfully identifies key topology features associated with financial crises, implying its potential for developing early warning systems in the financial sector. The insights gained from this analysis could significantly enhance the early detection and proactive management of system risks in financial market, thereby contributing to more robust risk assessment and policy formulation strategies.

Enhancing supply chain resilience facing partial and complete disruptions: The application in the cooking oil industry

Nowadays, food supply chains are more vulnerable to disruptions due to competition, uncertain environments, and global catastrophes such as the recent pandemic. Due to the importance of addressing disruption risks, this study develops a hybrid mechanism to enhance resilience in a multi-product supply chain. The proposed mechanism improves supplier resilience in a Cooking Oil Supply Chain (COSC) network design. First, we identify elements that affect supplier resilience and present a mechanism based on the Analytical Network Process (ANP) to evaluate resilience. In addition, several strategies are determined to enhance network resilience. Moreover, a stochastic mathematical formulation is proposed to utilize suppliers’ scores and a combination of identified strategies to strengthen supply chain resilience. The developed stochastic model encompasses three objectives and considers possible partial and complete disruptions of the supply chain network. Furthermore, we develop a robust counterpart formulation to handle the disruption uncertainty of the COSC and utilize the Normalized Normal Constraint (NNC) method to generate Pareto optimal solutions. Lastly, we put the implemented mechanism into practice in a real-world case to demonstrate its effectiveness in enhancing supply chain resilience. Likewise, we unveil that the implementation of suitable resilience strategies within the supply chain can lead to substantial improvements in performance metrics. For example, the results obtained illustrate that by embracing resilience strategies, the proposed mechanism has the potential to reduce the overall cost of the supply chain by as much as 56%.

A New Hyaluronic Emulgel of Hesperetin for Topical Application-An In Vitro Evaluation.

The present study aimed to formulate and characterize a hesperetin formulation to achieve adequate deposition and retention of hesperetin in the epidermis as a target for some cosmetic/dermatological actions. To derive the final emulgel, various formulations incorporating different proportions of Polysorbate 80 and hyaluronic acid underwent testing through a Box-Behnken experimental design. Nine formulations were created until the targeted emulgel properties were achieved. This systematic approach, following the principles of a design of experiment (DoE) methodology, adheres to a quality-by-design (QbD) paradigm, ensuring a robust and purposeful formulation and highlighting the commitment to a quality-driven design approach. The emulsions were developed using the phase inversion method, optimizing the emulgel with the incorporation of hyaluronic acid. Physically stable optimized emulgels were evaluated for their globule size, surface charge, viscosity, pH, electrical conductivity, and hesperetin content. These assays, along with the temperature swing test, were used to select the optimal formulation. It was characterized by a droplet size, d[4,3], of 4.02 μm, a Z-potential of -27.8 mV, an O/W sign, a pH of 5.2, and a creamy texture and proved to be stable for at least 2 months at room temperature. Additionally, in vitro release kinetics from the selected emulgel exhibited a sustained release profile of hesperetin. Skin assays revealed adequate retention of hesperetin in the human epidermis with minimum permeation. Altogether, these results corroborate the promising future of the proposed emulgel in cosmetic or dermatological use on healthy or diseased skin.

Ship coping strategies for hurricane-induced port disruptions

PurposeDisruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.Design/methodology/approachThis paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.FindingsThe results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.Originality/valueSince the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

Analytical Characterization of Heterogeneities in mRNA-Lipid Nanoparticles Using Sucrose Density Gradient Ultracentrifugation.

Rational design and robust formulation processes are critical for optimal delivery of mRNA by lipid nanoparticles (LNPs). Varying degrees of heterogeneity in mRNA-LNPs can affect their biophysical and functional properties. Given the profound complexity of mRNA-LNPs, it is critical to develop comprehensive and orthogonal analytical techniques for a better understanding of these formulations. To this end, we developed a robust ultracentrifugation method for density-based separation of subpopulations of mRNA-LNPs. Four LNP formulations encapsulating human erythropoietin (hEPO) with varying functionalities were synthesized using two ionizable lipids, A and B, and two helper lipids, 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE) and 1,2-dierucoyl-sn-glycero-3-phosphoethanolamine (DEPE), along with cholesterol and DMG-PEG-2K. Upon ultracentrifugation on a sucrose gradient, a distinct pattern of "fractions" was observed across the gradient, from the less dense topmost fraction to the increasingly denser bottom fractions, which were harvested for comprehensive analyses. Parent LNPs, A-DOPE and B-DOPE, were resolved into three density-based fractions, each differing significantly in the hEPO expression following intravenous and intramuscular routes of administration. Parent B-DEPE LNPs resolved into two density-based fractions, with most of the payload and lipid content being attributed to the topmost fraction compared to the lower one, indicating some degree of heterogeneity, while parent A-DEPE LNPs showed remarkable homogeneity, as indicated by comparable in vivo potency, lipid numbers, and particle count among the three density-based fractions. This study is the first to demonstrate the application of density gradient-based ultracentrifugation (DGC) for a head-to-head comparison of heterogeneity as a function of biological performance and biophysical characteristics of parent mRNA-LNPs and their subpopulations.

Towards Robustness to Natural Variations and Distribution Shift (Student Abstract)

This research focuses on improving the robustness of machine learning systems to natural variations and distribution shifts. A design trade space is presented, and various methods are compared, including adversarial training, data augmentation techniques, and novel approaches inspired by model-based robust optimization formulations.

First-hand accounts of structural stigma toward people who use opioids on Reddit

People who use opioids face multilevel stigma that negatively affects their health and well-being and drives opioid-related overdose. Little research has focused on lived experience of the structural levels of stigma toward opioid use. This study identified and qualitatively analyzed Reddit content about structural stigma toward opioid use. Iterative, human-in-the-loop natural language processing methods were used to identify relevant posts and comments from an opioid-related subforum. Ultimately, 273 posts and comments were qualitatively analyzed via directed content analysis guided by a prominent conceptualization of stigma. Redditors described how structures—including governmental programs and policies, the pharmaceutical industry, and healthcare systems—stigmatize people who use opioids. Structures were reported to stigmatize through labeling (i.e., particularly in medical settings), perpetuating negative stereotypes, separating people who use opioids into those who use opioids “legitimately” versus “illegitimately,” and engendering status loss and discrimination (e.g., denial of healthcare, loss of employment). Redditors also posted robust formulations of structural stigma, mostly describing how it manifests in the criminalization of substance use, is often driven by profit motive, and leads to the pervasiveness of fentanyl in the drug supply and the current state of the overdose crisis. Some posts and comments highlighted interpersonal and structural resources (e.g., other people who use opioids, harm reduction programs, telemedicine) leveraged to navigate structural stigma and its effects. These findings reveal key ways by which structural stigma can pervade the lives of people who use opioids and show the value of social media data for investigating complex social processes. Particularly, this study's findings related to structural separation may help encourage efforts to promote solidarity among people who use opioids. Attending to first-hand accounts of structural stigma can help interventions aiming to reduce opioid-related stigma be more responsive to these stigmatizing structural forces and their felt effects.

Multiply Complementary Priors for Image Compressive Sensing Reconstruction in Impulsive Noise

Impulsive noise is always present in real-world image Compressive Sensing (CS) acquisition systems, where existing CS reconstruction performance may seriously deteriorate. In this article, we propose a robust CS formulation for image reconstruction to suppress outliers in the presence of impulsive noise. To address this issue, we consider a novel truncated-Cauchy loss function as the metric of residual error to elevate the reconstruction robustness. Specifically, we design a complementary priors model to incorporate nonconvex nonlocal low-rank prior and deep denoiser prior for high-accuracy image reconstruction. By means of the half-quadratic optimization theory and generalized soft-thresholding technique, we also develop an alternative optimization algorithm for solving the induced nonconvex optimization problem. Numerical simulations demonstrate the robustness and accuracy of the proposed robust CS method compared to some recent CS methods for image reconstruction in impulsive noise.