A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist Scheduling Problems

Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices. Although there are a few approaches proposed to target at the challenging problem, they generally cannot scale to large-scale scheduling problems. In this paper, we formulate the hoist scheduling problem as a new temporal planning problem in the form of adapted PDDL, and propose a novel hierarchical temporal planning approach to efficiently solve the scheduling problem. Additionally, we provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem. We exhibit that the proposed approach is able to efficiently find solutions of high quality for large-scale real-life benchmark instances, with comparison to state-of-the-art baselines

Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized and organically holistic, requiring comprehensive consideration of the patient's state and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patient status and only explore potential patterns between symptoms and prescriptions. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), a framework that treats the model as a sequential prescription-making problem by considering the dynamics of the patient's condition across multiple visits. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and the patient's condition. Experimental results on a real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance

Adhesive and Self-Healing Polyurethanes with Tunable Multifunctionality

Many polyurethanes (PUs) are blood-contacting materials due to their good mechanical properties, fatigue resistance, cytocompatibility, biosafety, and relatively good hemocompatibility. Further functionalization of the PUs using chemical synthetic methods is especially attractive for expanding their applications. Herein, a series of catechol functionalized PU (CPU-PTMEG) elastomers containing variable molecular weight of polytetramethylene ether glycol (PTMEG) soft segment are reported by stepwise polymerization and further introduction of catechol. Tailoring the molecular weight of PTMEG fragment enables a regulable catechol content, mobility of the chain segment, hydrogen bond and microphase separation of the C-PU-PTMEG elastomers, thus offering tunability of mechanical strength (such as breaking strength from 1.3 MPa to 5.7 MPa), adhesion, self-healing efficiency (from 14.9% to 96.7% within 2 hours), anticoagulant, antioxidation, anti-inflammatory properties and cellular growth behavior. As cardiovascular stent coatings, the C-PU-PTMEGs demonstrate enough flexibility to withstand deformation during the balloon dilation procedure. Of special importance is that the C-PU-PTMEG-coated surfaces show the ability to rapidly scavenge free radicals to maintain normal growth of endothelial cells, inhibit smooth muscle cell proliferation, mediate inflammatory response, and reduce thrombus formation. With the universality of surface adhesion and tunable multifunctionality, these novel C-PU-PTMEG elastomers should find potential usage in artificial heart valves and surface engineering of stents

Phosphoproteomic Profiling of In Vivo Signaling in Liver by the Mammalian Target of Rapamycin Complex 1 (mTORC1)

Our understanding of signal transduction networks in the physiological context of an organism remains limited, partly due to the technical challenge of identifying serine/threonine phosphorylated peptides from complex tissue samples. In the present study, we focused on signaling through the mammalian target of rapamycin (mTOR) complex 1 (mTORC1), which is at the center of a nutrient- and growth factor-responsive cell signaling network. Though studied extensively, the mechanisms involved in many mTORC1 biological functions remain poorly understood.We developed a phosphoproteomic strategy to purify, enrich and identify phosphopeptides from rat liver homogenates. Using the anticancer drug rapamycin, the only known target of which is mTORC1, we characterized signaling in liver from rats in which the complex was maximally activated by refeeding following 48 hr of starvation. Using protein and peptide fractionation methods, TiO(2) affinity purification of phosphopeptides and mass spectrometry, we reproducibly identified and quantified over four thousand phosphopeptides. Along with 5 known rapamycin-sensitive phosphorylation events, we identified 62 new rapamycin-responsive candidate phosphorylation sites. Among these were PRAS40, gephyrin, and AMP kinase 2. We observed similar proportions of increased and reduced phosphorylation in response to rapamycin. Gene ontology analysis revealed over-representation of mTOR pathway components among rapamycin-sensitive phosphopeptide candidates.In addition to identifying potential new mTORC1-mediated phosphorylation events, and providing information relevant to the biology of this signaling network, our experimental and analytical approaches indicate the feasibility of large-scale phosphoproteomic profiling of tissue samples to study physiological signaling events in vivo

Creativity of AI: Automatic Symbolic Option Discovery for Facilitating Deep Reinforcement Learning

Despite of achieving great success in real life, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, which are data efficiency, lack of the interpretability and transferability. Recent research shows that embedding symbolic knowledge into DRL is promising in addressing those challenges. Inspired by this, we introduce a novel deep reinforcement learning framework with symbolic options. This framework features a loop training procedure, which enables guiding the improvement of policy by planning with action models and symbolic options learned from interactive trajectories automatically. The learned symbolic options alleviate the dense requirement of expert domain knowledge and provide inherent interpretability of policies. Moreover, the transferability and data efficiency can be further improved by planning with the action models. To validate the effectiveness of this framework, we conduct experiments on two domains, Montezuma's Revenge and Office World, respectively. The results demonstrate the comparable performance, improved data efficiency, interpretability and transferability

Retrosynthetic planning with experience-guided Monte Carlo tree search

Abstract In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule using simple building blocks leads to a combinatorial explosion of possibilities. Even experienced chemists often have difficulty to select the most promising transformations. The current approaches rely on human-defined or machine-trained score functions which have limited chemical knowledge or use expensive estimation methods for guiding. Here we propose an experience-guided Monte Carlo tree search (EG-MCTS) to deal with this problem. Instead of rollout, we build an experience guidance network to learn knowledge from synthetic experiences during the search. Experiments on benchmark USPTO datasets show that, EG-MCTS gains significant improvement over state-of-the-art approaches both in efficiency and effectiveness. In a comparative experiment with the literature, our computer-generated routes mostly matched the reported routes. Routes designed for real drug compounds exhibit the effectiveness of EG-MCTS on assisting chemists performing retrosynthetic analysis

Gradient-Based Mixed Planning with Discrete and Continuous Actions

Dealing with planning problems with both discrete logical relations and continuous numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex quadratic constraints on numeric variables, which harms the performance when solving the problem. In this paper, we propose a novel algorithm framework to solve the numeric planning problems mixed with discrete and continuous actions based on gradient descent. We cast the numeric planning with discrete and continuous actions as an optimization problem by integrating a heuristic function based on discrete effects. Specifically, we propose a gradient-based framework to simultaneously optimize continuous parameters and actions of candidate plans. The framework is combined with a heuristic module to estimate the best plan candidate to transit initial state to the goal based on relaxation. We repeatedly update numeric parameters and compute candidate plan until it converges to a valid plan to the planning problem. In the empirical study, we exhibit that our algorithm framework is both effective and efficient, especially when solving non-convex planning problems.Comment: 36 pages, 20 figure

Surface Modification of Cardiovascular Stent Material 316L SS with Estradiol-Loaded Poly (trimethylene carbonate) Film for Better Biocompatibility

A delay in the endothelialization process represents a bottleneck in the application of a drug-eluting stent (DES) during cardiovascular interventional therapy, which may lead to a high risk of late restenosis. In this study, we used a novel active drug, estradiol, which may contribute to surface endothelialization of a DES, and prepared an estradiol-loaded poly (trimethylene carbonate) film (PTMC-E5) on the surface of the DES material, 316L stainless steel (316L SS), in order to evaluate its function in improving surface endothelialization. All the in vitro and in vivo experiments indicated that the PTMC-E5 film significantly improved surface hemocompatibility and anti-hyperplasia, anti-inflammation and pro-endothelialization properties. This novel drug-delivery system may provide a breakthrough for the surface endothelialization of cardiovascular DES

A microfluidic system simulating physiological fluid environment for studying the degradation behaviors of magnesium-based materials

Magnesium (Mg)-based materials have excellent potential for application in biodegradable vascular stents. Before application, all these materials need to be screened and optimized, especially the screening of corrosion resistance, which is one of the key indicators for stent material screening. Based on the characteristics of the structure of the stent, we focus on the study of the corrosion and degradation behavior of the micron-scale stent struts in the simulated in vivo environment. The struts are simplified into Mg-based wires, and a microfluidic system is established to provide near physiological conditions. A flow-induced shear stress (FISS) of approximately 0.68 Pa close to the wall shear stress of the human coronary artery is applied to the sample surface relying on the microfluidic system. The degradation behaviors of Mg-based wire samples close to the size of struts are studied simultaneously parallel under FISS conditions using this microfluidic system. The immersion test and in vivo experiments demonstrated the feasibility of this microfluidic system for studies of the degradation behavior of Mg-based materials under simulated physiological conditions. In addition, it was also investigated that the effect of degradation products produced under dynamic conditions on vascular cell behavior. The results show that the degradation rate is significantly accelerated under the effect of FISS in the in vitro study, the degradation rate is obviously higher than that in vivo, and AZ31 has the fastest degradation rate compared with pure magnesium and Mg–Zn–Y–Nd alloys. Taken together, this microfluidic system can be used to evaluate and screen the corrosion resistance of Mg-based materials, providing a basis for the design and optimization of Mg-based cardiovascular stent materials