Transition Metal‐Catalyzed and MAO‐Assisted Olefin Polymerization. Cyclic Isomers of Sinn’s Dimer Are Excellent Ligands in Iron Complexes and Great Methylating Reagents

Methylaluminoxane (MAO) is the most commonly used co‐catalyst for transition metalcatalyzed olefin polymerization, but the structures of MAO species and their catalytic functions remain topics of intensive study. We are interested in MAO‐assisted polymerization with catalysts L(R2)FeCl2 (L = tridentate pyridine‐2,6‐diyldimethanimine; imine‐R = Me, Ph). It is our hypothesis that the MAO species is not merely enabling Fe−Me bond formation but functions as an integral part of the active catalyst, a MAO adduct of the Fe‐precatalyst [L(R2)FeCl]+. In this paper, we explored the possible structures of acyclic and cyclic MAO species and their complexation with pre‐catalysts [L(R2)FeCl]+ using quantum chemical approaches (MP2 and DFT). We report absolute and relative oxophilicities associated with the Fe←O(MAO) adduct formation and provide compelling evidence that oxygen of an acyclic MAO species (i.e., O(AlMe2)2, 4) cannot compete with the O‐donor in cyclic MAO species (i.e., (MeAlO)2, 7; MeAl(OAlMe2)2, cyclic 5). Significantly, our work demonstrates that intramolecular O→Al dative bonding results in cyclic isomers of MAO species (i.e., cyclic 5) with high oxophilicities. The stabilities of the [L(R2)FeClax(MAO)eq]+ species demonstrate that 5 provides for the ligating benefits of the cyclic MAO species 4 without the thermodynamically costly elimination of TMA. Mechanistic implications are discussed for the involvement of such Fe−O−Al bridged catalyst in olefin polymerization

Privacy-Preserving Adaptive Traffic Signal Control in a Connected Vehicle Environment

Although Connected Vehicles (CVs) have demonstrated tremendous potential to enhance traffic operations, they can impose privacy risks on individual travelers, e.g., leaking sensitive information about their frequently visited places, routing behavior, etc. Despite the large body of literature that devises various algorithms to exploit CV information, research on privacy-preserving traffic control is still in its infancy. In this paper, we aim to fill this research gap and propose a privacy-preserving adaptive traffic signal control method using CV data. Specifically, we leverage secure Multi-Party Computation and differential privacy to devise a privacy-preserving CV data aggregation mechanism, which can calculate key traffic quantities without any CVs having to reveal their private data. We further develop a linear optimization model for adaptive signal control based on the traffic variables obtained via the data aggregation mechanism. The proposed linear programming problem is further extended to a stochastic programming problem to explicitly handle the noises added by the differentially private mechanism. Evaluation results show that the linear optimization model preserves privacy with a marginal impact on control performance, and the stochastic programming model can significantly reduce residual queues compared to the linear programming model, with almost no increase in vehicle delay. Overall, our methods demonstrate the feasibility of incorporating privacy-preserving mechanisms in CV-based traffic modeling and control, which guarantees both utility and privacy

Control-Aware Trajectory Predictions for Communication-Efficient Drone Swarm Coordination in Cluttered Environments

Swarms of Unmanned Aerial Vehicles (UAV) have demonstrated enormous potential in many industrial and commercial applications. However, before deploying UAVs in the real world, it is essential to ensure they can operate safely in complex environments, especially with limited communication capabilities. To address this challenge, we propose a control-aware learning-based trajectory prediction algorithm that can enable communication-efficient UAV swarm control in a cluttered environment. Specifically, our proposed algorithm can enable each UAV to predict the planned trajectories of its neighbors in scenarios with various levels of communication capabilities. The predicted planned trajectories will serve as input to a distributed model predictive control (DMPC) approach. The proposed algorithm combines (1) a trajectory compression and reconstruction model based on Variational Auto-Encoder, (2) a trajectory prediction model based on EvolveGCN, a graph convolutional network (GCN) that can handle dynamic graphs, and (3) a KKT-informed training approach that applies the Karush-Kuhn-Tucker (KKT) conditions in the training process to encode DMPC information into the trained neural network. We evaluate our proposed algorithm in a funnel-like environment. Results show that the proposed algorithm outperforms state-of-the-art benchmarks, providing close-to-optimal control performance and robustness to limited communication capabilities and measurement noises.Comment: 15 pages, 15 figures, submitted to IEEE Transactions on Intelligent Vehicle

NMR Study of CO2 Capture by Butylamine and Oligopeptide KDDE in Aqueous Solution: Capture Efficiency and Gibbs Free Energy of the Capture Reaction as a Function of PH**

We Have Been Interested in the Development of Rubisco-Based Biomimetic Systems for Reversible CO2 Capture from Air. Our Design of the Chemical CO2 Capture and Release (CCR) System is Informed by the Understanding of the Binding of the Activator CO2 (ACO2) in Rubisco (Ribulose-1,5-Bisphosphate Carboxylase/oxygenase). the Active Site Consists of the Tetrapeptide Sequence Lys-Asp-Asp-Glu (Or KDDE) and the Lys Sidechain Amine is Responsible for the CO2 Capture Reaction. We Are Studying the Structural Chemistry and the Thermodynamics of CO2 Capture based on the Tetrapeptide CH3CO−KDDE−NH2 ( KDDE ) in Aqueous Solution to Develop Rubisco Mimetic CCR Systems. Here, We Report the Results of 1H NMR and 13C NMR Analyses of CO2 Capture by Butylamine and by KDDE. the Carbamylation of Butylamine Was Studied to Develop the NMR Method and with the Protocol Established, We Were Able to Quantify the Oligopeptide Carbamylation at Much Lower Concentration. We Performed a PH Profile in the Multi Equilibrium System and Measured Amine Species and Carbamic Acid/carbamate Species by the Integration of 1H NMR Signals as a Function of PH in the Range 8≤pH≤11. the Determination of ΔG1(R) for the Reaction R−NH2+CO2 (Formula Presented.) R−NH−COOH Requires the Solution of a Multi-Equilibrium Equation System, Which Accounts for the Dissociation Constants K2 and K3 Controlling Carbonate and Bicarbonate Concentrations, the Acid Dissociation Constant K4 of the Conjugated Acid of the Amine, and the Acid Dissociation Constant K5 of the Alkylcarbamic Acid. We Show How the Multi-Equilibrium Equation System Can Be Solved with the Measurements of the Daughter/parent Ratio X, the Knowledge of the PH Values, and the Initial Concentrations [HCO3−]0 and [R-NH2]0. for the Reaction Energies of the Carbamylations of Butylamine and KDDE, Our Best Values Are ΔG1(Bu)=−1.57 Kcal/mol and ΔG1(KDDE)=−1.17 Kcal/mol. Both CO2 Capture Reactions Are Modestly Exergonic and Thereby Ensure Reversibility in an Energy-Efficient Manner. These Results Validate the Hypothesis that KDDE-Type Oligopeptides May Serve as Reversible CCR Systems in Aqueous Solution and Guide Designs for their Improvement

Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances

This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations. Even though reinforcement learning (RL) is capable of optimizing energy efficiency in intricate environments, it is challenged by safety requirements during the training process. The lack of safety guarantees is the other concern when deploying a trained policy in real-world application. Compared with RL, model predicted control (MPC) can handle constrained dynamics systems, ensuring safe driving. However, the major challenges lie in complicated eco-driving tasks and the presence of disturbances, which respectively challenge the MPC design and the satisfaction of constraints. To address these limitations, the proposed framework incorporates the tube-based enhanced MPC (RMPC) to ensure the safe execution of the RL policy under disturbances, thereby improving the control robustness. RL not only optimizes the energy efficiency of the connected and automated vehicle in mixed traffic but also handles more uncertain scenarios, in which the energy consumption of the human-driven vehicle and its diverse and stochastic driving behaviors are considered in the optimization framework. Simulation results demonstrate that the proposed algorithm, compared with RMPC technique, shows an average improvement of 10.88% in holistic energy efficiency, while compared with RL algorithm, it effectively prevents inter-vehicle collisions

A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature

This study presents a novel approach that synergizes community detection algorithms with various Graph Neural Network (GNN) models to bolster link prediction in scientific literature networks. By integrating the Louvain community detection algorithm into our GNN frameworks, we consistently enhance performance across all models tested. For example, integrating Louvain with the GAT model resulted in an AUC score increase from 0.777 to 0.823, exemplifying the typical improvements observed. Similar gains are noted when Louvain is paired with other GNN architectures, confirming the robustness and effectiveness of incorporating community-level insights. This consistent uplift in performance reflected in our extensive experimentation on bipartite graphs of scientific collaborations and citations highlights the synergistic potential of combining community detection with GNNs to overcome common link prediction challenges such as scalability and resolution limits. Our findings advocate for the integration of community structures as a significant step forward in the predictive accuracy of network science models, offering a comprehensive understanding of scientific collaboration patterns through the lens of advanced machine learning techniques

Time-to-Green predictions for fully-actuated signal control systems with supervised learning

Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications

Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators

We show that DNN accelerator micro-architectures and their program mappings represent specific choices of loop order and hardware parallelism for computing the seven nested loops of DNNs, which enables us to create a formal taxonomy of all existing dense DNN accelerators. Surprisingly, the loop transformations needed to create these hardware variants can be precisely and concisely represented by Halide's scheduling language. By modifying the Halide compiler to generate hardware, we create a system that can fairly compare these prior accelerators. As long as proper loop blocking schemes are used, and the hardware can support mapping replicated loops, many different hardware dataflows yield similar energy efficiency with good performance. This is because the loop blocking can ensure that most data references stay on-chip with good locality and the processing units have high resource utilization. How resources are allocated, especially in the memory system, has a large impact on energy and performance. By optimizing hardware resource allocation while keeping throughput constant, we achieve up to 4.2X energy improvement for Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs), respectively.Comment: Published as a conference paper at ASPLOS 202