Automatic Truss Design with Reinforcement Learning

Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training. In this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. AutoTruss first adopts Monte Carlo tree search to discover a diverse collection of valid layouts. Then RL is applied to iteratively refine the valid solutions. We conduct experiments and ablation studies in popular truss layout design test cases in both 2D and 3D settings. AutoTruss outperforms the best-reported layouts by 25.1% in the most challenging 3D test cases, resulting in the first effective deep-RL-based approach in the truss layout design literature.Comment: IJCAI2023. The codes are available at https://github.com/StigLidu/AutoTrus

DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue

Tagged magnetic resonance imaging (MRI) has been used for decades to observe and quantify the detailed motion of deforming tissue. However, this technique faces several challenges such as tag fading, large motion, long computation times, and difficulties in obtaining diffeomorphic incompressible flow fields. To address these issues, this paper presents a novel unsupervised phase-based 3D motion estimation technique for tagged MRI. We introduce two key innovations. First, we apply a sinusoidal transformation to the harmonic phase input, which enables end-to-end training and avoids the need for phase interpolation. Second, we propose a Jacobian determinant-based learning objective to encourage incompressible flow fields for deforming biological tissues. Our method efficiently estimates 3D motion fields that are accurate, dense, and approximately diffeomorphic and incompressible. The efficacy of the method is assessed using human tongue motion during speech, and includes both healthy controls and patients that have undergone glossectomy. We show that the method outperforms existing approaches, and also exhibits improvements in speed, robustness to tag fading, and large tongue motion.Comment: Accepted to MIDL 2023 (full paper

Methylprednisolone as Adjunct to Endovascular Thrombectomy for Large-Vessel Occlusion Stroke

Importance It is uncertain whether intravenous methylprednisolone improves outcomes for patients with acute ischemic stroke due to large-vessel occlusion (LVO) undergoing endovascular thrombectomy. Objective To assess the efficacy and adverse events of adjunctive intravenous low-dose methylprednisolone to endovascular thrombectomy for acute ischemic stroke secondary to LVO. Design, Setting, and Participants This investigator-initiated, randomized, double-blind, placebo-controlled trial was implemented at 82 hospitals in China, enrolling 1680 patients with stroke and proximal intracranial LVO presenting within 24 hours of time last known to be well. Recruitment took place between February 9, 2022, and June 30, 2023, with a final follow-up on September 30, 2023.InterventionsEligible patients were randomly assigned to intravenous methylprednisolone (n = 839) at 2 mg/kg/d or placebo (n = 841) for 3 days adjunctive to endovascular thrombectomy. Main Outcomes and Measures The primary efficacy outcome was disability level at 90 days as measured by the overall distribution of the modified Rankin Scale scores (range, 0 [no symptoms] to 6 [death]). The primary safety outcomes included mortality at 90 days and the incidence of symptomatic intracranial hemorrhage within 48 hours. Results Among 1680 patients randomized (median age, 69 years; 727 female [43.3%]), 1673 (99.6%) completed the trial. The median 90-day modified Rankin Scale score was 3 (IQR, 1-5) in the methylprednisolone group vs 3 (IQR, 1-6) in the placebo group (adjusted generalized odds ratio for a lower level of disability, 1.10 [95% CI, 0.96-1.25]; P = .17). In the methylprednisolone group, there was a lower mortality rate (23.2% vs 28.5%; adjusted risk ratio, 0.84 [95% CI, 0.71-0.98]; P = .03) and a lower rate of symptomatic intracranial hemorrhage (8.6% vs 11.7%; adjusted risk ratio, 0.74 [95% CI, 0.55-0.99]; P = .04) compared with placebo. Conclusions and Relevance Among patients with acute ischemic stroke due to LVO undergoing endovascular thrombectomy, adjunctive methylprednisolone added to endovascular thrombectomy did not significantly improve the degree of overall disability.Trial RegistrationChiCTR.org.cn Identifier: ChiCTR210005172

Guidelines for Optimal Selection of Subcritical Low-Temperature Geothermal Organic Rankine Cycle Configuration Considering Reinjection Temperature Limits

General guidelines are proposed to select the optimal subcritical organic Rankine cycle configuration considering reinjection temperature limits for a low-temperature geothermal brine power plant. Saturated/superheated, non-regenerative/regenerative cycles are investigated. Evaporating temperature and overheating degree at the turbine inlet are selected as design variables, and highest plant exergy efficiency is pursued for current optimizations. Through theoretical analysis of mathematical modelling and typical case studies, a simple optimization approach is presented. The new approach consists of up to three judgements on reinjection temperature and evaporating temperature in comparison two optimization calculations along the saturated line and along the given reinjection temperature line. The potential optimal cycle configurations are saturated non-regenerative cycle, saturated regenerative cycle and superheated regenerative cycle. Then, this new optimization approach is applied to obtain optimal cycle configuration and relevant working condition. The working fluids investigated are R245fa, R1234ze(Z), isopentane, and isobutane. The saturated non-regenerative cycle is the optimal cycle when the reinjection temperature limit is equal or less than the optimal reinjection temperature with no reinjection constraint. Otherwise, the reinjection temperature limit influences not only the optimal cycle configuration but also the optimal working condition. Working fluid isobutane always achieves highest plant exergy efficiency for optimal cycles with either reinjection temperature limit

Achieving near-infrared-light-mediated switchable friction regulation on MXene-based double network hydrogels

Abstract MXene possesses great potential in enriching the functionalities of hydrogels due to its unique metallic conductivity, high aspect ratio, near-infrared light (NIR light) responsiveness, and wide tunability, however, the poor compatibility of MXene with hydrogels limits further applications. In this work, we report a uniformly dispersed MXene-functionalized poly-N-isopropylacrylamide (PNIPAM)/poly-2-acrylamido-2-methyl-1-propanesulfonic acid (PAMPS) double network hydrogel (M—DN hydrogel) that can achieve switchable friction regulation by using the NIR light. The dispersity of MXene in hydrogels was significantly improved by incorporating the chitosan (CS) polymer. This M—DN hydrogel showed much low coefficient of friction (COF) at 25 °C due to the presence of hydration layer on hydrogel surface. After illuminating with the NIR light, M—DN hydrogel with good photothermal effect rapidly raised the temperature to above the lower critical solution temperature (LCST), which led to an obvious increase of surface COF owing to the destruction of the hydration layer. In addition, M—DN friction control hydrogel showed good recyclability and controllability by tuning “on-off” of the NIR light. This work highlights the construction of functional MXene hydrogels for intelligent lubrication, which provides insight for interface sensing, controlled transmission, and flexible robotic arms

Fast Prediction Method of Combustion Chamber Parameters Based on Artificial Neural Network

Gas turbines are widely used in industry, and the combustion chamber, compressor, and turbine are known as their three important components. In the design process of the combustion chamber, computational fluid dynamics simulation takes up a lot of time. In order to accelerate the design speed of the combustion chamber, this article proposes a combustion chamber design method that combines an artificial neural network (ANN) and computational fluid dynamics (CFD). CFD results are used as raw data to establish a fast prediction model using ANN and eXtreme Gradient Boosting (XGBoost). The results show that the mean squared error (MSE) of the ANN is 0.0019, and the MSE of XGBoost is 0.0021, so the ANN’s prediction performance is slightly better. This fast prediction method combines CFD and the ANN, which can greatly shorten CFD calculation time, improve the efficiency of gas turbine combustion chamber design, and provide the possibility of achieving digital twins of gas turbine combustion chambers

Analysis of dynamic characteristics of bubble rise under a free surface

The rising process of a bubble occurs in several natural and industrial apparatuses. This process is computationally studied using the front tracking method for a moving interface whose surface properties are solved in terms of an immersed-boundary method. The results show that the free interface does not influence the bubble before the centroid velocity of the bubble reaches the terminal velocity, which reaches a stable value or fluctuates at it, with the distance h (between the centroid of the bubble and the free surface) reaching a certain value. When the Reynolds number increases, the time to reach terminal velocity will decrease, and the influence of the viscous factor on the terminal velocity is also weakened. The dramatic interaction between a bubble and free surface is beneficial to accelerate film draining out. It is also shown that the shape of the bubble gradually becomes an ellipse as the Weber number (We) decreases, and it is beneficial to reduce the resistance of the bubble. The free surface could accelerate the bubble breaking at high We values.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Research Progress of Biomarkers of Sepsis-Associated Encephalopathy

Abstract Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, raise the mortality rate with an incidence of up to 71%. Pathological neuroinflammation after sepsis leads to acute brain dysfunction, survivors may remain long-term cognitive impairment. At present, the evaluation of SAE severity and prognosis mainly depends on clinical manifestations and imaging features, but lack of effectiveness and timeliness. Biomarkers of nerve injuries nowadays, have shown good application value and perspectives in the diagnosis and evaluation of SAE. This article will review the current biomarkers for accurate diagnosis and evaluation, basing on the possible pathophysiological mechanism of different stages of SAE