Daniel Rivera

Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants' time-varying processes to make inferences about a particular intervention's effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study parti...

The T lymphocyte antigens, which may have a role in protection against tularemia, were predicted by immunoinformatics analysis of Francisella tularensis Schu4. Twenty-seven class II putative promiscuous epitopes and 125 putative class I supertype epitopes were chosen for synthesis; peptides were tested in vitro for their ability to bind HLA and to induce immune responses from PBMCs of 23 previously infected subjects. While the immune responses of individual subjects showed heterogeneity, 95% of the subjects responded strongly to a pool of 27 promiscuous peptides; 25%, 33%, and 44% of subjects responded to pools of 25 A2, A24, and B7 peptides, respectively. These data can aid in the development of novel epitope-based and subunit tularemia vaccines.

Abstract. Protein therapeutics have recently emerged as a viable means of treating chronic diseases and are beginning to rival small-molecule drugs in market share. Although their promise of targeted therapy is a major medical advance, repeated administrations in many cases lead to ...

Epitope-driven vaccines are created from selected sub-sequences of proteins, or epitopes, derived by scanning the protein sequences of pathogens for patterns of amino acids that permit binding to human MHC molecules. We developed a prototype tuberculosis (TB) vaccine that contains epitopes derived by (1) EpiMer mapping of previously published secreted proteins derived from Mycobacterium tuberculosis (Mtb), and (2) EpiMatrix mapping

The design of epitope-driven vaccines that address the global variability of HIV has been significantly hampered by concerns about conservation of the vaccine epitopes across clades of HIV. We developed two computer-driven methods for improving epitope-driven HIV vaccines: the Epi-Assembler, which derives representative or "immunogenic consensus sequence" (ICS) epitopes from multiple viral variants, and VaccineCAD, which reduces junctional immunogenicity when epitopes are aligned in a string-of-beads format for insertion in a DNA expression vector. In this study, we report on 20 ICS HIV-1 peptides. The core 9-mer contained in these consensus peptides was conserved in 105-2250 individual HIV-1 strains. Nineteen of the 20 ICS epitopes (95%) evaluated in this study were confirmed in ELISpot assays using peripheral blood monocytes obtained from 13 healthy HIV-1 infected subjects. Twenty-five ICS peptides (all 20 of the peptides evaluated in this study and 5 additional ICS epitopes) were then aligned in a pseudoprotein string using "VaccineCAD", an epitope alignment tool that eliminates immunogenicity created by the junctions between the epitopes. Reordering the construct reduced the immunogenicity of the junctions between epitopes as measured by EpiMatrix, an epitope mapping algorithm. The reordered construct was also a more effective immunogen in vivo when tested in HLA-DR transgenic mice. These data confirm the utility of bioinformatics tools to design novel vaccines containing "immunogenic consensus…

We propose a dynamical systems model that captures the daily fluctuations of human weight change, incorporating both physiological and psychological factors. The model consists of an energy balance integrated with a mechanistic behavioral model inspired by the Theory ...

The T lymphocyte antigens, which may have a role in protection against tularemia, were predicted by immunoinformatics analysis of Francisella tularensis Schu4. Twenty-seven class II putative promiscuous epitopes and 125 putative class I supertype epitopes were chosen for synthesis; peptides were tested in vitro for their ability to bind HLA and to induce immune responses from PBMCs of 23 previously infected subjects. While the immune responses of individual subjects showed heterogeneity, 95% of the subjects responded strongly to a pool of 27 promiscuous peptides; 25%, 33%, and 44% of subjects responded to pools of 25 A2, A24, and B7 peptides, respectively. These data can aid in the development of novel epitope-based and subunit tularemia vaccines.

Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior ca...

There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm mini...

The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in partic...

The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.

Among health behaviors, physical activity has the most extensive record of research using passive sensors. Control systems and other system dynamic approaches have long been considered applicable for understanding human behavior, but only recently has the technology provided the precise and intensive longitudinal data required for these analytic approaches. Although sensors provide intensive data on the patterns and variations of physical activity over time, the influences of these variations are often unmeasured. Health behavior theories provide an explanatory framework of the putative mediators of physical activity changes. Incorporating the intensive longitudinal measurement of these theoretical constructs is critical to improving the fit of control system model of physical activity and for advancing behavioral theory. Theory-based control models also provide guidance on the nature of the controllers which serve as the basis for just-in-time adaptive interventions based on these ...

ABSTRACT This paper examines the use of system identification to describe time-varying phenomena in a smoking cessation intervention. The analysis is facilitated by the availability of intensive longitudinal data that enables the application of system identification techniques. Two model structures are considered; one involves the concept of statistical mediation, while the other describes a feedback mechanism. In fitting these models to intensive longitudinal data from a University of Wisconsin clinical trial that studied bupropion and counseling as smoking cessation aids, we focus on the relationship between craving and smoking. Here, we find craving features inverse response and smoking behavior features a dramatic reduction on the quit date, followed by a resumption in smoking. Analyzing the resulting models, we find that they differ in how they describe smoking resumption, and the case is made that the feedback mechanism more appropriately describes the relationship between craving and smoking.

Data-centric estimation methods such as Model-on-Demand and Direct Weight Optimization form attractive techniques for estimating unknown functions from noisy data. These methods rely on generating a local function approximation from a database of regressors at the current operating point with the process repeated at each new operating point. This paper examines the design of optimal input signals formulated to produce informative data to be used by local modeling procedures. The proposed method specifically addresses the distribution of the regressor vectors. The design is examined for a linear time-invariant system under amplitude constraints on the input. The resulting optimization problem is solved using semidefinite relaxation methods. Numerical examples show the benefits in comparison to a classical PRBS input design.

The development of control-oriented decision policies for inventory management in supply chains has received considerable interest in recent years, and demand modeling to supply forecasts for these policies is an important component of an effective solution to this problem. Drawing from the problem of control-relevant identification, we present an approach for demand modeling based on data that relies on a

Behavioral scientists have historically relied on static modeling methodologies. The rise in mobile and wearable sensors has made intensive longitudinal data (ILD) - behavioral data measured frequently over time - increasingly available. Consequently, analytical frameworks are emerging that seek to reliably quantify dynamics reflected in these data. Employing an input-output perspective, dynamical systems models from engineering can characterize time-varying behaviors as processes of change. Specifically, ILD and parameter estimation routines from system identification can be leveraged together to offer parsimonious and quantitative descriptions of dynamic behavioral constructs. The utility of this approach for facilitating a better understanding of health behaviors is illustrated with two examples. In the first example, dynamical systems models are developed for Social Cognitive Theory (SCT), a prominent concept in behavioral science that considers interrelationships between personal factors, the environment, and behaviors. Estimated models are then obtained that explore the role of SCT in a physical activity intervention. The second example uses ILD to model day-to-day changes in smoking levels as a craving-mediated process of behavior change.

The chronic, relapsing nature of tobacco use represents a major challenge in smoking cessation treatment. Recently, novel intervention paradigms have emerged that seek to adjust treatments over time in order to meet a patient's changing needs. This article demonstrates that Hybrid Model Predictive Control (HMPC) offers an appealing framework for designing these optimized, time-varying smoking cessation interventions. HMPC is a particularly appropriate approach as it recognizes that intervention doses must be assigned in predetermined, discrete units while retaining receding-horizon, constraint-handling, and combined feedback and feedforward capabilities. Specifically, an intervention algorithm is developed here in which counseling and two pharmacotherapies are manipulated to reduce daily smoking and craving levels. The potential usefulness of such an intervention is illustrated through simulated treatment of a quit attempt in a hypothetical patient, which highlights that prioritizing reduction in craving over total daily smoking levels significantly reduces craving levels, suppresses relapse, and successfully rejects time-varying disturbances such as stress, all while adhering to several practical operational constraints and resource use considerations.

Cigarette smoking remains a major public health issue. Despite a variety of treatment options, existing intervention protocols intended to support attempts to quit smoking have low success rates. An emerging treatment framework, referred to as adaptive interventions in behavioral health, addresses the chronic, relapsing nature of behavioral health disorders by tailoring the composition and dosage of intervention components to an individual's changing needs over time. An important component of a rapid and effective adaptive smoking intervention is an understanding of the behavior change relationships that govern smoking behavior and an understanding of intervention components' dynamic effects on these behavioral relationships. As traditional behavior models are static in nature, they cannot act as an effective basis for adaptive intervention design. In this article, behavioral data collected daily in a smoking cessation clinical trial is used in development of a dynamical systems model that describes smoking behavior change during cessation as a self-regulatory process. Drawing from control engineering principles, empirical models of smoking behavior are constructed to reflect this behavioral mechanism and help elucidate the case for a control-oriented approach to smoking intervention design.

Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants' time-varying processes to make inferences about a particular intervention's effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study participants provided 42 daily assessments of pre-quit and post-quit withdrawal symptoms. Regression splines were used to approximate smooth functions of craving and negative affect and to estimate the variables' derivatives for each participant. We then modeled the dynamics of nicotine craving using standard input-output dynamical systems models. These models provide a more detailed characterization of the post-quit craving process than do traditional longitudinal models, including information regarding the type, magnitude, and speed of the response to an input. The results, in conjunction with standard engineering control theory techniques, could potentially be used by tobacco researchers to develop a more effective smoking intervention.

Self-regulation, a key component of the addiction process, has been challenging to model precisely in smoking cessation settings, largely due to the limitations of traditional methodological approaches in measuring behavior over time. However, increased availability of intensive longitudinal data (ILD) measured through ecological momentary assessment facilitates the novel use of an engineering modeling approach to better understand self-regulation. Dynamical systems modeling is a mature engineering methodology that can represent smoking cessation as a self-regulation process. This article shows how a dynamical systems approach effectively captures the reciprocal relationship between day-to-day changes in craving and smoking. Models are estimated using ILD from a smoking cessation randomized clinical trial. A system of low-order differential equations is presented that models cessation as a self-regulatory process. It explains 87.32% and 89.16% of the variance observed in craving and smoking levels, respectively, for an active treatment group and 62.25% and 84.12% of the variance in a control group. The models quantify the initial increase and subsequent gradual decrease in craving occurring postquit as well as the dramatic quit-induced smoking reduction and postquit smoking resumption observed in both groups. Comparing the estimated parameters for the group models suggests that active treatment facilitates craving reduction and slows postquit smoking resumption. This article illustrates that dynamical systems modeling can effectively leverage ILD in order to understand self-regulation within smoking cessation. Such models quantify group-level dynamic responses in smoking cessation and can inform the development of more effective interventions in the future.

This paper considers the use of constrained minimum crest factor multisine signals as inputs for plant-friendly identification testing of chemical process systems. The methodology presented here effectively integrates operating restrictions, information-theoretic ...

This paper examines the application of model predictive control (MPC), an advanced control technique originating from the process industries, to supply chain management (SCM) problems arising in semiconductor manufacturing. The main goal of this work is to demonstrate the usefulness of MPC as a tactical decision policy that is an integral part of a comprehensive hierarchical decision framework aimed at

A method is outlined for designing Smith predictor controllers that provide robust performance despite real parameter uncertainties in the process model. Insight into the design process is gained by viewing the Smith predictor from the perspective of Internal Model Control. Performance requirements are written in terms of a frequency-domain weight restricting the magnitude of the closed-loop sensitivity function. A general

Control-relevant model reduction problems for SISO H sub (2), H sub ( infinity), and mu-controller synthesis. DE Rivera, M Morari International Journal of Control 46:22, 505-527, 1987. The problem of model reduction in the context of control system design is investigated. ...

Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behavior change. System identification problems that draw from two modeling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modeling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.

... DE Rivera is with the Department of Chemical Engineering, Arizona State University, Tempe, AZ 85287-6006 USA (e-mail: daniel.rivera@asu.edu). KD Smith is with Intel Corporation, Chandler, AZ 85226 USA (e-mail: kirk.d.smith@intel.com). ...

... DE Rivera is with the Department of Chemical and Materials Engineering and the Control Systems Engineering Laboratory, Institute for Manufacturing Enterprise Systems, Arizona State University, Tempe, AZ 85287-6006 USA (e-mail: daniel.rivera@asu.edu). ...

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty.

The paper proposes a method to optimize the cost and time of a project. The method considers principles from risk management and applying model predictive control (MPC). The control variables (continuous or discrete) are the mitigation actions that must be executed in order to reduce risk exposure. Risk impacts are considered to be stochastic variables to model uncertainties that could

A two layer hierarchical framework for optimization, control, and scheduling of semi-conductor reentrant lines is proposed. In this framework, model predictive control (MPC) is used at the top layer for real-time optimization (RTO). This layer acts as an interface between long-term planning (months) and scheduling (minutes). An ℓ1-norm MPC, which uses a discrete linear model, addresses the long-term (shifts) inventory

A two-layer production control method applied to discrete event reentrant semiconductor manufacturing lines is investigated. A modified l1-norm predictive controller/optimizer is proposed as a coordinator in the highest layer and a distributed control policy is used as a follow-up controller in the lowest layer. The use of a model predictive control (MPC) formulation allows the scheduling algorithm to simultaneously solve

Page 1. Centralized Model Predictive Control Strategies for Inventory Management in Semiconductor Manufacturing Supply Chains Wenlin Wang, Daniel E. Rivera' Department of Chemical and Materials Engineering Arizona State University, Tempe, Arizona 85287-6006 ...

Highly interactive systems are ill-conditioned and highly sensitive to model uncertainty, which imposes limitations to achievable closed-loop performance. In this paper, the goal is to develop an identification testing framework meaningful to highly interactive systems based on the ...