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Semiactive model predictive control (sMPC) can be very effective, but its computational cost due to the inherent mixed-integer quadratic programming (MIQP) optimization precludes its use in real-time vibration control. This study proposes training neural networks (NNs) to predict in real-time only the MIQP's integer variables' values, called a strategy, for a given structure state. Because the number of strategies is exponential in the number of sMPC horizon steps, the resulting NN can be massive. This study proposes to reduce the NN dimension by exploiting the homogeneity-of-order-one nature of this control problem and, using state vector statistics, to efficiently choose training samples. The single large NN is proposed to be split into several much smaller NNs, each predicting a strategy grouping, that together uniquely and efficiently predict the strategy. Given the strategy's integer values, the MIQP optimization reduces to a quadratic programming (QP) problem, solved using a fast QP solver with proposed adaptations: exploiting optimization efficiencies and bounding sub-optimality; using several NN predictions; and reverting to a simpler (suboptimal) semiactive control algorithm upon occasional incorrect NN predictions or QP solver nonconvergence. Shear building examples demonstrate significant online computational cost reductions with control performance comparable to the conventional MIQP-based control. 

Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied proteins or those with previously uncharacterized functions or multiple activities. We present a machine learning algorithm named CLEAN (contrastive learning–enabled enzyme annotation) to assign EC numbers to enzymes with better accuracy, reliability, and sensitivity compared with the state-of-the-art tool BLASTp. The contrastive learning framework empowers CLEAN to confidently (i) annotate understudied enzymes, (ii) correct mislabeled enzymes, and (iii) identify promiscuous enzymes with two or more EC numbers—functions that we demonstrate by systematic in silico and in vitro experiments. We anticipate that this tool will be widely used for predicting the functions of uncharacterized enzymes, thereby advancing many fields, such as genomics, synthetic biology, and biocatalysis. 

Large-scale seismic structural tests are crucial to validating both structural design methodologies and the effectiveness of seismic isolation devices. However, considering the significant costs of such tests, it is essential to leverage data from completed tests by taking advantage of numerical models of the tested structures, updated using data collected from the experiments, to complete additional studies that may be difficult, unsafe or impossible to physically test. However, updating complex numerical models poses its own challenges. The first contribution of this paper is to develop a multi-stage model updating method suitable for high-order models of base-isolated structures, which is motivated and evaluated through modeling and model updating of a full-scale four-story base-isolated reinforced-concrete frame building that was tested in 2013 at the NIED E-Defense laboratory in Japan. In most studies involving model updating, all to-be-updated parameters are typically updated simultaneously; however, given the observation that the superstructure in this study predominantly moves as a rigid body in low-frequency modes and the isolation layer plays a minor role in all other modes, this study proposes updating parameters in stages: first, the linear superstructure parameters are updated so that its natural frequencies and mode shapes match those identified via a subspace system identification of the experimental building responses to low-level random excitations; then, the isolation-layer device linear parameters are updated so that the natural frequencies, damping ratios and mode shapes of the three isolation modes match. These two stages break a large-scale linear model updating problem into two smaller problems, thereby reducing the search space for the to-be-updated parameters, which generally reduces computational costs regardless of what optimization algorithm is adopted. Due to the limited instrumentation, the identified modes constitute only a subset of all the modes; to match each identified mode with a FEM mode, a procedure is proposed to compare each identified mode with a candidate set of FEM modes and to select the best match, which is a second contribution. Further, nonlinear isolation-layer device models are proposed, updated and validated with experimental data. Finally, combining the isolation-layer devices' nonlinear models with the updated superstructure linear FEM, the final result is a data-calibrated nonlinear numerical model that will be used for further studies of controllable damping and validation of new design methodologies, and is being made available for use by the research community, alleviating the dearth of experimentally-calibrated numerical models of full-scale base-isolated buildings with lateral-torsional coupling effects. 

Increasing demand of using everyday clothing in wearable sensing and display has synergistically advanced the field of electronic textiles, or e-textiles. A variety of types of e-textiles have been formed into stretchy fabrics in a manner that can maintain their intrinsic properties of stretchability, breathability, and wearability to fit comfortably across different sizes and shapes of the human body. These unique features have been leveraged to ensure accuracy in capturing physical, chemical, and electrophysiological signals from the skin under ambulatory conditions, while also displaying the sensing data or other immediate information in daily life. Here, we review the emerging trends and recent advances in e-textiles in wearable sensing and display, with a focus on their materials, constructions, and implementations. We also describe perspectives on the remaining challenges of e-textiles to guide future research directions toward wider adoption in practice. 

In this paper, we introduce the design and implementation of a low-cost, small-scale autonomous vehicle equipped with an onboard computer, a camera, a Lidar, and some other accessories. We implement various autonomous driving-related modules including mapping and localization, object detection, obstacle avoidance, and path planning. In order to better test the system, we focus on the autonomous parking scenario. In this scenario, the vehicle is able to move from an appointed start point to the desired parking lot autonomously by following a path planned by the hybrid A* algorithm. The vehicle is able to detect objects and avoid obstacles on its path and achieve autonomous parking. 

Abstract Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited. Here, we report ECNet (evolutionary context-integrated neural network), a deep-learning algorithm that exploits evolutionary contexts to predict functional fitness for protein engineering. This algorithm integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe. As such, it enables accurate mapping from sequence to function and provides generalization from low-order mutants to higher-order mutants. We show that ECNet predicts the sequence-function relationship more accurately as compared to existing machine learning algorithms by using ~50 deep mutational scanning and random mutagenesis datasets. Moreover, we used ECNet to guide the engineering of TEM-1 β-lactamase and identified variants with improved ampicillin resistance with high success rates.