Distributed Data-driven Predictive Control via Dissipative Behavior Synthesis

This paper presents a distributed data-driven predictive control (DDPC) approach using the behavioral framework. It aims to design a network of controllers for an interconnected system with linear time-invariant (LTI) subsystems such that a given global (network-wide) cost function is minimized while desired control performance (e.g., network stability and disturbance rejection) is achieved using dissipativity in the quadratic difference form (QdF). By viewing dissipativity as a behavior and integrating it into the control design as a virtual dynamical system, the proposed approach carries out the entire design process in a unified framework with a set-theoretic viewpoint. This leads to an effective data-driven distributed control design, where the global design goal can be achieved by distributed optimization based on the local QdF conditions. The approach is illustrated by an example throughout the paper

Predictive Model for Thermal and Stress Field in Selective Laser Melting Process -- Part II

Finite Element Analysis (FEA) is used to predict the transient thermal cycle and optimize process parameters to analyze these effects on deformation and residual stresses. However, the process of predicting the thermal history in this process with the FEA method is usually time-consuming, especially for large-scale parts. In this paper, an effective predictive model of part deformation and residual stress was developed for accurately predicting deformation and residual stresses in large-scale parts. An equivalent body heat flux proposed from the single layer laser scan model was imported as the thermal load to the layer by layer model. The hatched layer is then heated up by the equivalent body heat flux and used as a basic unit element to build up the macroscale part. The thermal history and residual stress fields of two solid parts with different support structures during the SLM process were simulated. Layer heat source method has the capability for fast temperature prediction in the SLM process, while sacrificing modeling details for the computational time-saving purpose. Thus numerical modeling in this work can be a very useful tool for the parametric study of process parameters, residual stresses and deformations

Refractive-index nonlinearities of intersubband transitions in GaN/AlN quantum-well waveguides

The refractive-index nonlinearities of intersubband transitions in GaN/AlN quantum-well waveguides are investigated. A large spectral broadening of TM-polarized near-infrared pulses is observed after propagation through these devices due to intersubband self-phase modulation. From the measured data, a nonlinear refractive index n 2 of 1.8ϫ 10 −12 cm 2 / W is estimated at the operating wavelength of 1550 nm. A detailed theoretical model of the intersubband refractive index as a function of wavelength and optical intensity is then presented. This model assumes an inhomogeneously broadened transition line and is based on experimentally determined material and device parameters. The results of this study are finally used to discuss the prospects of nitride quantum wells for all-optical switching via cross-phase modulation

Original Article Sunitinib for patients with locally advanced or distantly metastatic dermatofibrosarcoma protuberans but resistant to imatinib

Abstract: Purpose: This study evaluated the efficacy and adverse effects of Imatinib therapy to advanced Dermatofibrosarcoma protuberan (DFSP) and Sunitinib therapy to advanced Dermatofibrosarcoma protuberan (DFSP) after Imatinib resistance. Methods: We analyzed the efficacy, adverse effects and survival of 95 patients with locally advanced or metastatic DFS

A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks

Online optimal energy management of plug-in hybrid electric vehicles has been continually investigated for better fuel economy. This paper proposed a predictive energy management strategy based on multi neural networks for a multi-mode plug-in hybrid electric vehicle. To attain it, firstly, the offline optimal results prepared for knowledge learning are derived by dynamic programming and Pontryagin’s minimum principle. Then, the mode recognition neural network is trained based on the optimal results of dynamic programming and the recurrent neural network is firstly exploited to realize online co-state estimation application. Consequently, the velocity prediction-based online model predictive control framework is established with the co-state correction and slacked constraints to solve the real-time optimal control sequence. A series of numerical simulation results validate that the optimal performance yielded from global optimal strategy can be exploited online to attain the satisfied cost reduction, compared with equivalent consumption minimum strategy, with the assistance of estimated real time co-state and slacked reference. In addition, the computation duration of proposed algorithm decreases by 23.40%, compared with conventional Pontryagin’s minimum principle-based model predictive control scheme, thereby proving its online application potential

The role of zinc in the adaptive evolution of polar phytoplankton

Zinc is an essential trace metal for oceanic primary producers with the highest concentrations in polar oceans. However, its role in the biological functioning and adaptive evolution of polar phytoplankton remains enigmatic. Here, we have applied a combination of evolutionary genomics, quantitative proteomics, co-expression analyses and cellular physiology to suggest that model polar phytoplankton species have a higher demand for zinc because of elevated cellular levels of zinc-binding proteins. We propose that adaptive expansion of regulatory zinc-finger protein families, co-expanded and co-expressed zinc-binding proteins families involved in photosynthesis and growth in these microalgal species and their natural communities were identified to be responsible for the higher zinc demand. The expression of their encoding genes in eukaryotic phytoplankton metatranscriptomes from pole-to-pole was identified to correlate not only with dissolved zinc concentrations in the upper ocean but also with temperature, suggesting that environmental conditions of polar oceans are responsible for an increased demand of zinc. These results suggest that zinc plays an important role in supporting photosynthetic growth in eukaryotic polar phytoplankton and that this has been critical for algal colonization of low-temperature polar oceans

Validating Antimetastatic Effects of Natural Products in an Engineered Microfluidic Platform Mimicking Tumor Microenvironment

Development of new, antimetastatic drugs from natural products has been substantially constrained by the lack of a reliable in vitro screening system. Such a system should ideally mimic the native, three-dimensional (3D) tumor microenvironment involving different cell types and allow quantitative analysis of cell behavior critical for metastasis. These requirements are largely unmet in the current model systems, leading to poor predictability of the in vitro collected data for in vivo trials, as well as prevailing inconsistency among different in vitro tests. In the present study, we report application of a 3D, microfluidic device for validation of the antimetastatic effects of 12 natural compounds. This system supports co-culture of endothelial and cancer cells in their native 3D morphology as in the tumor microenvironment and provides real-time monitoring of the cells treated with each compound. We found that three compounds, namely sanguinarine, nitidine, and resveratrol, exhibited significant antimetastatic or antiangiogenic effects. Each compound was further examined for its respective activity with separate conventional biological assays, and the outcomes were in agreement with the findings collected from the microfluidic system. In summary, we recommend use of this biomimetic model system as a new engineering tool for high-throughput evaluation of more diverse natural compounds with varying anticancer potentials

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages