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1. Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages

   https://doi.org/10.1111/risa.13343  

   Yu, Jin‐Zhu ; Baroud, Hiba ( July 2019 , Risk Analysis)

   The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross‐validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log‐likelihood and root mean squared error. The HBKM yields on average the highest out‐of‐sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.

    

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2. Experimental Analysis of Chiller Cooling Failure in a Small Size Data Center Environment Using Wireless Instrumentation

   https://doi.org/10.1115/IPACK2018-8334  

   Tradat, Mohammad ; Sammakia, Bahgat ; Alissa, Husam ; Nemati, Kourosh ( January 2018 , ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems)

   Given the vital rule of data center availability and since the inlet temperature of the IT equipment increase rapidly until reaching a certain threshold value after which IT starts throttling or shut down because of overheat during cooling system failure. Hence, it is especially important to understand failures and their effects. This study presented experimental investigation and analysis of a facility-level cooling system failure scenario in which chilled water interruption introduced to the data center. Quantitative instrumentation tools including wireless technology such as wireless temperature and pressure sensors were used to measure the discrete air inlet temperature and pressure differential though cold aisle enclosure, respectively. In addition, Intelligent Platform Management Interface (IPMI) and cooling system data during failure/recovery were reported. Furthermore, the IT equipment performance and response for opened and contained environments were simulated and compared. Finally, an experiment based analysis of the Ride Through Time (RTT) of servers during chilled water interruption of the cooling infrastructure presented as well. The results showed that for all three classes of servers tested during the cooling failure, CAC helped keep the server’s cooler for longer. The containment provided a barrier between the hot and cold air streams and caused slight negative pressure to build up, which allowed the servers to pull cold air from the underfloor plenum. In addition, the results show that the effect of CAC in containment solutions on the IT equipment performance and response could vary and depend on the server’s airflow, generation and hence types of servers deployed in cold aisle enclosure. Moreover, it was shown that when compared to the discrete sensors, the IPMI inlet temperature sensors underestimate the Ride Through Time (RTT) by 42% and 12% for the CAC and opened cases, respectively. 

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3. Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models

   https://doi.org/10.1175/AIES-D-22-0038.1  

   Weirich-Benet, Elizabeth ; Pyrina, Maria ; Jiménez-Esteve, Bernat ; Fraenkel, Ernest ; Cohen, Judah ; Domeisen, Daniela I. V. ( April 2023 , Artificial Intelligence for the Earth Systems)

   Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves.

   Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves.

    

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4. Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method

   https://doi.org/10.1002/jcc.27089  

   Sun, Yuchen ; He, Xibing ; Hou, Tingjun ; Cai, Lianjin ; Man, Viet Hoag ; Wang, Junmei ( February 2023 , Journal of Computational Chemistry)

   Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson‐Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1‐BCC‐GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1‐BCC (Austin Model 1‐bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types,on,oi,hn1,hn2,hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein‐ligand binding free energies using the MM‐PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM‐PBSA method.

    

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5. Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal Recovery

   https://doi.org/10.1109/TSP.2017.2691661  

   Gu, Renliang ; Dogandzic, Aleksandar ( July 2017 , IEEE Transactions on Signal Processing)

   We develop a projected Nesterov’s proximal-gradient (PNPG) approach for sparse signal reconstruction that combines adaptive step size with Nesterov’s momentum acceleration. The objective function that we wish to minimize is the sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a convex regularization term. We apply sparse signal regularization where the signal belongs to a closed convex set within the closure of the domain of the NLL; the convex-set constraint facilitates flexible NLL domains and accurate signal recovery. Signal sparsity is imposed using the ℓ₁-norm penalty on the signal’s linear transform coefficients. The PNPG approach employs a projected Nesterov’s acceleration step with restart and a duality-based inner iteration to compute the proximal mapping. We propose an adaptive step-size selection scheme to obtain a good local majorizing function of the NLL and reduce the time spent backtracking. Thanks to step-size adaptation, PNPG converges faster than the methods that do not adjust to the local curvature of the NLL. We present an integrated derivation of the momentum acceleration and proofs of O(k⁻²) objective function convergence rate and convergence of the iterates, which account for adaptive step size, inexactness of the iterative proximal mapping, and the convex-set constraint. The tuning of PNPG is largely application independent. Tomographic and compressed-sensing reconstruction experiments with Poisson generalized linear and Gaussian linear measurement models demonstrate the performance of the proposed approach. 

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