Abstract: Passive rotational stiffness of the osseo-ligamentous spine is an important input parameter for estimating in-vivo spinal loading using musculoskeletal models. These data are typically acquired from cadaveric testing. Increasingly, they are also estimated from subject-specific imaging-based finite element (FE) models, which are typically built from CT/MR data obtained in supine position and employ pure rotation kinematics. We explored the sensitivity of FE-based lumbar passive rotational stiffness to two aspects of functional in-vivo kinematics: (a) passive strain changes from supine to upright standing position, and (b) in-vivo coupled translation-rotation kinematics. We developed subject-specific FE models of four subjects' L4L5 segments from supine CT images. Sagittally symmetric flexion was simulated in two ways: (i) pure flexion up to 12 degrees under a 500 N follower load directly from the supine pose. (ii) First, a displacement-based approach was implemented to attain the upright pose, as measured using Dynamic Stereo X-ray (DSX) imaging. We then simulated in-vivo flexion using DSX imaging-derived kinematics. Datasets from weight-bearing motion with three different external weights [(4.5 kg), (9.1 kg), (13.6 kg)] were used. Accounting for supine-upright motion generated compressive pre-loads approximate to 468 N (+/- 188 N) and a “pre-torque” approximate to 2.5 Nm (+/- 2.2 Nm), corresponding to 25% of the reaction moment at 10 degrees flexion (case (i)). Rotational stiffness estimates from DSX-based coupled translation-rotation kinematics were substantially higher compared to pure flexion. Reaction Moments were almost 90% and 60% higher at 5 degrees and 10 degrees of L4L5 flexion, respectively. Within-subject differences in rotational stiffness based on external weight were small, although between-subject variations were large. (C) 2020 Elsevier Ltd. All rights reserved.

Abstract: Second-to-second renewable power fluctuations can severely hinder the frequency regulation performance of modern isolated microgrids, as these typically have a low inertia and significant renewable energy integration. In this context, the present paper studies the coordinated control of Thermostatically Controlled Loads (TCLs) for managing short-term power imbalances, and their integration in microgrid operations through the use of aggregate TCL models. In particular, two computationally efficient and accurate aggregate TCL models are developed: a virtual battery model representing the aggregate flexibility of TCLs considering solar irradiance heat gains and wall/floor heat transfers, and a frequency transient model representing the aggregate dynamics of a TCL collection considering communication delays and the presence of model uncertainty and time-variability. The proposed aggregate TCL models are then used to design a practical Energy Management System (EMS) integrating TCL flexibility, and study the impact of TCL integration on microgrid operation and frequency control. Computational experiments using detailed frequency transient and thermal dynamic models are presented, demonstrating the accuracy of the proposed aggregate TCL models, as well as the economic and reliability benefits resulting from using these aggregate models to integrate TCLs in microgrid operations.

Abstract: This paper proposes a framework to calculate the bounds on failure probability of linear structural systems whose performance is affected by both random variables and interval variables. This kind of problems is known to be very challenging, as it demands coping with aleatoric and epistemic uncertainty explicitly. Inspired by the framework of the operator norm theorem, it is proposed to consider the maximum standard deviation of the structural response as a proxy for detecting the crisp values of the interval parameters, which yield the bounds of the failure probability. The scope of application of the proposed approach comprises linear structural systems, whose properties may be affected by both aleatoric and epistemic uncertainty and that are subjected to (possibly imprecise) Gaussian loading. Numerical examples indicate that the application of such proxy leads to substantial numerical advantages when compared to a traditional double-loop approach for coping with imprecise failure probabilities. In fact, the proposed framework allows to decouple the propagation of aleatoric and epistemic uncertainty.

Abstract: Increased bone formation resulting from mechanical loading is well documented; however, the interactions of the mechanotransduction pathways are less well understood. Endothelin-1, a ubiquitous autocrine/paracrine signaling molecule promotes osteogenesis in metastatic disease. In the present study, it was hypothesized that exposure to big endothelin-1 (big ET1) and/or mechanical loading would promote osteogenesis in ex vivo trabecular bone cores. In a 2 x 2 factorial trial of daily mechanical loading (-2000 μepsilon,120 cycles daily, “jump” waveform) and big ET1 (25 ng/mL), 48 bovine sternal trabecular bone cores were maintained in bioreactor chambers for 23 days. The bone cores' response to the treatment stimuli was assessed with percent change in core apparent elastic modulus (Delta E-app), static and dynamic histomorphometry, and prostaglandin E2 (PGE2) secretion. Two-way ANOVA with a post hoc Fisher's LSD test found no significant treatment effects on Delta E-app (p = 0.25 and 0.51 for load and big ET1, respectively). The Delta E-app in the “no load + big ET1” (CE, 13 +/- 12.2%, p = 0.56), “load + no big ET1” (LC, 17 +/- 3.9%, p = 0.14) and “load + big ETI” (LE, 19 +/- 4.2%, p = 0.13) treatment groups were not statistically different than the control group (CC, 3.3% +/- 8.6%). Mineralizing surface (MS/BS), mineral apposition (MAR) and bone formation rates (BFR/BS) were significantly greater in LE than CC (p = 0.037, 0.0040 and 0.019, respectively). While the histological bone formation markers in LC trended to be greater than CC (p = 0.055, 0.11 and 0.074, respectively) there was no difference between CE and CC (p = 0.61, 0.50 and 0.72, respectively). Cores in LE and LC had more than 50% greater MS/BS (p = 0.037, p = 0.055 respectively) and MAR (p = 0.0040, p = 0.11 respectively) than CC. The BFR/BS was more than two times greater in LE (p = 0.019) and LC (p = 0.074) than CC. The PGE2 levels were elevated at 8 days post-osteotomy in all groups and the treatment groups remained elevated compared to the CC group on days 15,19 and 23. The data suggest that combined exposure to big ET1 and mechanical loading results in increased osteogenesis as measured in biomechanical, histomorphometric and biochemical responses. (C) 2016 Elsevier Inc. All rights reserved.

Abstract: In this paper, we consider a parallel machine scheduling problem in which machines have a limited workload capacity and jobs have deadlines and release dates. The problem is motivated by the operation of energy storage management systems for microgrids under emergency conditions and generalizes some problems that have already been studied in the literature for their theoretical value. In this work, we propose heuristic and exact algorithms to solve the problem. The heuristics are adaptations of classical bin packing heuristics in which additional conditions on the feasibility of a solution are imposed, whereas the exact method is a branch-and-price approach. The results show that the branch-andprice approach is able to optimally solve random instances with up to 250 jobs within a time limit of one hour, while the heuristic procedures provide near optimal solution within reduced running times. Finally, we also provide additional complexity results for a special case of the problem. (C) 2019 Elsevier Inc. All rights reserved.

Abstract: Dynamic Parallelism (DP) is a GPU programming abstraction that can make parallel computation more efficient for problems that exhibit heterogeneous workloads. With DP, GPU threads can launch kernels with more threads, recursively, producing a subdivision effect where resources are focused on the regions that exhibit more parallel work. Doing an optimal subdivision process is not trivial, as the combination of different parameters play a relevant role in the final performance of DP. Also, the current programming abstraction of DP relies on kernel recursion, which has performance overhead. This work presents a new subdivision cost model for problems that exhibit self similar density (SSD) workloads, useful for finding efficient subdivision schemes. Also, a new subdivision implementation free of recursion overhead is presented, named Adaptive Serial Kernels (ASK). Using the Mandelbrot set as a case study, the cost model shows that optimal performance is achieved when using {g -32, r -2, B -32} for the initial subdivision, recurrent subdivision and stopping size, respectively. Experimental results agree with the theoretical parameters, confirming the usability of the cost model. In terms of performance, the ASK approach runs up to -60% faster than DP in the Mandelbrot set, and up to 12x faster than a basic exhaustive implementation, whereas DP is up to 7.5x faster. In terms of energy efficiency, ASK is up to -2x and -20x more energy efficient than DP and the exhaustive approach, respectively. These results put the subdivision cost model and the ASK approach as useful tools for analyzing the potential improvement of subdivision based approaches and for developing more efficient GPU-based libraries or fine-tune specific codes in research teams.

Abstract: A low-complexity decision model for a Home Energy Management System is proposed to follow demand trajectory sets received from a Demand Side Response aggregator. This model is designed to reduce its computational complexity and being solved by low performance processors using available Single-Board Computers as a proof of concept. To decrease the computational complexity is proposed a two-stage model, where the first stage evaluates the hourly appliance scheduling using a relaxed set of restrictions, and the second stage evaluates a reduced set of appliances in a intra-hourly interval with a detailed characterization of the scheduled appliance properties. Simulations results show the effectiveness of the proposed algorithm to follow trajectories for different sets of home appliances and operational conditions. For the studied cases, the model presents deviations in the demand for the 3.2% of the cases in the first-stage and a 12% for the second-stage model. Results show that the proposed model can schedule available appliances according to the demand aggregator requirements in a limited solving time with diverse hardware.

Abstract: Wildfires can pose a major threat to the secure operation of power networks. Chile, California, and Australia have suffered from recent wildfires that have induced considerable power supply cuts. Further, as power systems move to a significant integration of variable renewable energy sources, successfully managing the impact of wildfires on the power supply can become even more challenging due to the joint uncertainty in wildfire trajectories and the power injections from wind and solar farms. Motivated by this, this paper develops a practical decision-support approach that concatenates a stochastic wildfire simulation method with an attacker-defender model that aims to find a worst-case realization for (i) transmission line and generator contingencies, out of those that can potentially be affected by a given wildfire scenario, and for (ii) wind and solar power trajectories, based on a max-min structure where the inner min problem represents a best adaptive response on generator dispatch actions. Further, this paper proposes an evaluation framework to assess the power supply security of various power system topology configurations, under the assumption of limited transmission switching capabilities, and based on the simulation of several wildfire evolution scenarios. Extensive computational experiments are carried out on two representations of the Chilean power network with up to 278 buses, showing the practical effectiveness of the proposed approach for enhancing wildfire resilience in highly renewable power systems.