ACT-R based human digital twin to enhance operators' performance in process industries.

To ensure safe and efficient operation, operators in process industries have to make timely decisions based on time-varying information. A holistic assessment of operators' performance is, therefore, challenging. Current approaches to operator performance assessment are subjective and ignore operators' cognitive behavior. In addition, these cannot be used to predict operators' expected responses during novel situations that may arise during plant operations. The present study seeks to develop a human digital twin (HDT) that can simulate a control room operator's behavior, even during various abnormal situations. The HDT has been developed using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. It mimics a human operator as they monitor the process and intervene during abnormal situations. We conducted 426 trials to test the HDT's ability to handle disturbance rejection tasks. In these simulations, we varied the reward and penalty parameters to provide feedback to the HDT. We validated the HDT using the eye gaze behavior of 10 human subjects who completed 110 similar disturbance rejection tasks as that of the HDT. The results indicate that the HDT exhibits similar gaze behaviors as the human subjects, even when dealing with abnormal situations. These indicate that the HDT's cognitive capabilities are comparable to those of human operators. As possible applications, the proposed HDT can be used to generate a large database of human behavior during abnormalities which can then be used to spot and rectify flaws in novice operator's mental models. Additionally, the HDT can also enhance operators' decision-making during real-time operation.

Integrated approach for optimal sensor placement and state estimation: A case study on water distribution networks.

The objective of the design and operation of any water distribution network (WDN) includes meeting the desired demand at sufficient pressure at all nodes. However, this requires situational awareness; in other words, the knowledge of system state variables such as pressure and flow throughout the network. In this work, a hybrid approach is developed for sensor placement (SP) and state estimation (SE) that exploits the underlying correlation structure in the data, along with the principles governing the flow through circular pipes. The problem of SP in WDN is addressed since measuring the state variables throughout the network is not practical. The problem of SE that maps to a matrix completion problem under certain physical and logical constraints is solved later. The completed matrix represents the state of WDN at any given time. Benchmark networks used in literature were used to evaluate the proposed approach. The mean absolute percentage error (MAPE) of less than 5% was obtained while estimating the head available at nodes. The knowledge of the states in the entire network could help operate the network adaptively.

On developing a framework for detection of oscillations in data.

Oscillation is a phenomenon very commonly observed in systems, ranging from simple ones to complex distributed network. Several techniques have been proposed in the literature for detecting oscillations to study their importance in domains ranging from physiology to climate studies. However, there is a lack of a common framework accommodative of important features of data such as non-stationarity, intermittent oscillations, measurement noise, multimodal oscillations, and the like. In this article, we outline a framework that addresses these challenges, the results of which can then be analyzed along with appropriate knowledge about the underlying system. We present results of an extensive simulation study that establishes the robustness and reliability of the proposed technique and demonstrate its applicability to real datasets in climate and in industrial datasets.

Frequency-induced morphology alterations in microconfined biological cells.

Low-intensity therapeutic ultrasound has demonstrated an impetus in bone signaling and tissue healing for decades now. Though this technology is clinically well proven, still there are breaches in studies to understand the fundamental principle of how osteoblast tissue regenerates physiologically at the cellular level with ultrasound interaction as a form of acoustic wave stimuli. Through this article, we illustrate an analysis for cytomechanical changes of cell membrane periphery as a basic first physical principle for facilitating late downstream biochemical pathways. With the help of in situ single-cell direct analysis in a microfluidic confinement, we demonstrate that alteration of low-intensity pulse ultrasound (LIPUS) frequency would physically perturb cell membrane and establish inherent cell oscillation. We experimentally demonstrate here that, at LIPUS resonance near 1.7 MHz (during 1-3 MHz alteration), cell membrane area would expand to 6.85 ± 0.7% during ultrasound exposure while it contracts 44.68 ± 0.8% in post actuation. Conversely, cell cross-sectional area change (%) from its previous morphology during and after switching off LIPUS was reversibly different before and after resonance. For instance, at 1.5 MHz, LIPUS exposure produced 1.44 ± 0.5% expansion while in contrast 2 MHz instigates 1.6 ± 0.3% contraction. We conclude that alteration of LIPUS frequency from 1-3 MHz keeping other ultrasound parameters like exposure time, pulse repetition frequency (PRF), etc., constant, if applied to a microconfined biological single living cell, would perturb physical structure reversibly based on the system resonance during and post exposure ultrasound pulsing. We envision, in the near future, our results would constitute the foundation of mechanistic effects of low-intensity therapeutic ultrasound and its allied potential in medical applications. Graphical Abstract Frequency Dependent Characterization of Area Strain in Cell Membrane by Microfluidic Based Single Cell Analysis.

A novel approach for benchmarking and assessing the performance of state estimators.

State estimation is a widely adopted soft sensing technique that incorporates predictions from an accurate model of the process and measurements to provide reliable estimates of unmeasured variables. The reliability of such estimators is threatened by measurement related challenges and model inaccuracies. In this article, a method for benchmarking of state estimation techniques is proposed. This method can be used to quantify the performance and hence reliability of an estimator. The Hurst exponents of a posteriori filtering errors are analyzed to characterize a benchmark (minimum mean squared error) estimator, similar to the minimum variance control benchmark developed for control loops. A distance metric is then used to quantify the extent of deviation of an estimator from the benchmark. The proposed technique is developed for linear systems and extended to non-linear systems with single as well as multiple measurable variables. Simulation studies are carried out with Kalman based as well as Monte Carlo based estimators whose computational details are significantly different. Results reveal that the technique serves as a tool that can quantify the performance and assess the reliability of a state estimator. The strengths and limitations of the proposed technique are discussed with guidelines on applications and deployment of the technique in a real life system.

Selective transformations between nanoparticle superlattices via the reprogramming of DNA-mediated interactions.

The rapid development of self-assembly approaches has enabled the creation of materials with desired organization of nanoscale components. However, achieving dynamic control, wherein the system can be transformed on demand into multiple entirely different states, is typically absent in atomic and molecular systems and has remained elusive in designed nanoparticle systems. Here, we demonstrate with in situ small-angle X-ray scattering that, by using DNA strands as inputs, the structure of a three-dimensional lattice of DNA-coated nanoparticles can be switched from an initial 'mother' phase into one of multiple 'daughter' phases. The introduction of different types of reprogramming DNA strands modifies the DNA shells of the nanoparticles within the superlattice, thereby shifting interparticle interactions to drive the transformation into a particular daughter phase. Moreover, we mapped quantitatively with free-energy calculations the selective reprogramming of interactions onto the observed daughter phases.

Stoichiometric control of DNA-grafted colloid self-assembly.

There has been considerable interest in understanding the self-assembly of DNA-grafted nanoparticles into different crystal structures, e.g., CsCl, AlB2, and Cr3Si. Although there are important exceptions, a generally accepted view is that the right stoichiometry of the two building block colloids needs to be mixed to form the desired crystal structure. To incisively probe this issue, we combine experiments and theory on a series of DNA-grafted nanoparticles at varying stoichiometries, including noninteger values. We show that stoichiometry can couple with the geometries of the building blocks to tune the resulting equilibrium crystal morphology. As a concrete example, a stoichiometric ratio of 3:1 typically results in the Cr3Si structure. However, AlB2 can form when appropriate building blocks are used so that the AlB2 standard-state free energy is low enough to overcome the entropic preference for Cr3Si. These situations can also lead to an undesirable phase coexistence between crystal polymorphs. Thus, whereas stoichiometry can be a powerful handle for direct control of lattice formation, care must be taken in its design and selection to avoid polymorph coexistence.

Designing DNA-grafted particles that self-assemble into desired crystalline structures using the genetic algorithm.

In conventional research, colloidal particles grafted with single-stranded DNA are allowed to self-assemble, and then the resulting crystal structures are determined. Although this Edisonian approach is useful for a posteriori understanding of the factors governing assembly, it does not allow one to a priori design ssDNA-grafted colloids that will assemble into desired structures. Here we address precisely this design issue, and present an experimentally validated evolutionary optimization methodology that is not only able to reproduce the original phase diagram detailing regions of known crystals, but is also able to elucidate several previously unobserved structures. Although experimental validation of these structures requires further work, our early success encourages us to propose that this genetic algorithm-based methodology is a promising and rational materials-design paradigm with broad potential applications.