Reinforcement Learning Based Gasoline Blending Optimization: Achieving More Efficient Nonlinear Online Blending of Fuels

The online optimization of gasoline blending benefits refinery economies. However, the nonlinear blending mechanism, the oil property fluctuations, and the blending model mismatch bring difficulties to the optimization. To solve the above issues, this paper proposes a novel online optimization method based on deep reinforcement learning algorithm (DRL). The Markov decision process (MDP) expression are given considering a practical gasoline blending system. Then, the environment simulator of gasoline blending process is established based on the MDP expression and the one-year measurement data of a real-world refinery. The soft actor-critic (SAC) DRL algorithm is applied to improve the DRL agent policy by using the data obtained from the interaction between DRL agent and environment simulator. Compared with a traditional method, the proposed method has better economic performance. Meanwhile, it is more robust under property fluctuations and component oil switching. Furthermore, the proposed method maintains performance by automatically adapting to system drift.Comment: 30 pages,13 figure

Generation of rotational ground state HD+^+ ions in an ion trap using a resonance-enhanced threshold photoionization process

We report a method for producing ultracold HD+ molecular ions populated in a rotational ground state in an ion trap based on [2+1'] resonance-enhanced threshold photoionization (RETPI) and sympathetic cooling with the laser-cooled Be$^+$ ions. The effect of electric field of the ion trap on the RETPI process of neutral HD molecules and the blackbody radiation (BBR) on the population evolution of rotational states of the generated polar HD+ ions have been studied. The initial rotational ground state population of HD$^+$ ions is 0.93(12). After the cumulation time of 5 s, the rotational ground state population is reduced to 0.77(8) due to the BBR coupling. This method of generating ultracold state-selected HD$^+$ ions is beneficial for the studies in precision rovibrational spectroscopy, state-controlled cold chemical reaction, and quantum logic spectroscopy.Comment: accepted by PRA, 7 pages, 7 figure

Vasohibin-1 suppresses colon cancer

Vasohibin-1 (VASH1) is an endogenous angiogenesis inhibitor. However, the clinical relevance of VASH1 in colon cancer and its regulations on cancer angiogenesis and cancer cell biological characteristics are still unknown. Here we showed that stromal VASH1 levels were negatively correlated with tumor size, advanced clinical stage and distant metastases in colon cancer patients. Overexpression of VASH1 in colon cancer cells induced apoptosis and senescence, inhibiting cancer cell growth and colony formation in vitro and tumor growth in vivo. In addition, knockdown of VASH1 in cancer cells promoted cell growth, adhesion and migration in vitro, and enhanced tumorigenesis and metastasis in vivo

META-ANALYSIS: THERAPEUTIC EFFECT OF TRANSCATHETER ARTERIAL CHEMOEMBOLIZATION COMBINED WITH COMPOUND KUSHEN INJECTION IN HEPATOCELLULAR CARCINOMA

Compound Kushen Injection (CKI) is Sophora Flavescens and Heterosmilacis Japonicae extract. Meta-analysis confirmed that CKI plus transcatheter arterial chemoembolization (TACE) is more superior to TACE alone for unresectable hepatocellular carcinoma (UHCC) patients

Landslide susceptibility assessment using the certainty factor and deep neural network

Areas with vulnerable ecological environments often breed many geological disasters, especially landslides, which pose a severe threat to the safety of people’s lives and property in these areas. To aid in landslide prevention and mitigation, an approach combining the coefficient of determination method (CF) and a deep neural network (DNN) were proposed in this study for landslide susceptibility evaluation. The deep neural network can excavate the deep features of samples and improve the accuracy of the susceptibility model. In addition, the logistic regression model (LRM) and support vector machine (SVM) were selected to create landslide susceptibility maps for comparison, which also involved the coefficient of determination method (CF). Based on landslide remote sensing interpretation and field investigations, a spatial database of mudstone landslides in the Xining area was established. Eight different conditional factors, including the elevation, slope, slope aspect, undulation, curvature, watershed, distance from a fault, and distance from a road, in the study area were selected as the evaluation factors to evaluate the susceptibility. The results revealed that four factors (i.e., the ground elevation, curvature, distance from a fault, and distance from a road) had relatively significant influences on the landslide susceptibility in the study area. Finally, the confusion matrix was used to evaluate the accuracy of the results obtained using the three methods, and the optimal result was selected to evaluate the landslide susceptibility in the study area. It was found that the combined CF-DNN method was more suitable for evaluating the landslide susceptibility in this area. Landslide susceptibility zoning was conducted to divide the study area into four sensitivity levels: low (32.65%), medium (35.12%), high (22.44%), and extremely high (9.79%) susceptibility. The high-risk areas were primarily distributed in the high-elevation areas along the eastern edge of the Huangshui Basin

Enhancing the diversity of self-replicating structures using active self-adapting mechanisms

Numerous varieties of life forms have filled the earth throughout evolution. Evolution consists of two processes: self-replication and interaction with the physical environment and other living things around it. Initiated by von Neumann et al. studies on self-replication in cellular automata have attracted much attention, which aim to explore the logical mechanism underlying the replication of living things. In nature, competition is a common and spontaneous resource to drive self-replications, whereas most cellular-automaton-based models merely focus on some self-protection mechanisms that may deprive the rights of other artificial life (loops) to live. Especially, Huang et al. designed a self-adaptive, self-replicating model using a greedy selection mechanism, which can increase the ability of loops to survive through an occasionally abandoning part of their own structural information, for the sake of adapting to the restricted environment. Though this passive adaptation can improve diversity, it is always limited by the loop’s original structure and is unable to evolve or mutate new genes in a way that is consistent with the adaptive evolution of natural life. Furthermore, it is essential to implement more complex self-adaptive evolutionary mechanisms not at the cost of increasing the complexity of cellular automata. To this end, this article proposes new self-adaptive mechanisms, which can change the information of structural genes and actively adapt to the environment when the arm of a self-replicating loop encounters obstacles, thereby increasing the chance of replication. Meanwhile, our mechanisms can also actively add a proper orientation to the current construction arm for the sake of breaking through the deadlock situation. Our new mechanisms enable active self-adaptations in comparison with the passive mechanism in the work of Huang et al. which is achieved by including a few rules without increasing the number of cell states as compared to the latter. Experiments demonstrate that this active self-adaptability can bring more diversity than the previous mechanism, whereby it may facilitate the emergence of various levels in self-replicating structures

Efficacy and safety of sintilimab plus doxorubicin in advanced soft tissue sarcoma: A single-arm, phase II trial

Background: Chemoimmunotherapy is safe and efficacious in treating many types of malignant tumors. However, clinical data demonstrating the effect of this combination treatment in patients with metastatic soft tissue sarcoma (STS) are currently limited. This study evaluated the safety and efficacy of a programmed cell death protein 1 (PD-1) inhibitor plus doxorubicin in patients with advanced STS who failed previous systemic therapy.Methods: This was a single-center, single-arm, open-label phase II trial. Patients with unresectable or metastatic STS who had previously failed systemic therapy were enrolled. Patients received up to six cycles of doxorubicin and sintilimab (a PD-1 inhibitor), while sintilimab treatment continued for up to 2 years. Primary outcomes were objective response rate (ORR) and safety. Univariate Cox proportional hazards model was used to analyze the relationship between clinicopathological parameters and progression-free survival (PFS).Results: A total of 38 patients (20 men and 18 women) were enrolled in this study. The overall ORR was 39.5%, disease control rate was 71.1%, and the median PFS was 4.5 months [95% confidence interval (CI), 3.0–8.5 months]. The adverse events (AEs) associated with the combined treatment were mild, manageable, and well-tolerated. The most common grade 3 or higher AEs were hematologic, including leukopenia (21.1%), anemia (18.4%), and thrombocytopenia (18.4%). Patients with undifferentiated pleomorphic sarcoma (UPS) or dedifferentiated liposarcoma had a significantly longer PFS than those with other pathological subtypes [hazard ratio (HR) = 0.42, 95% CI 0.21–0.83; p = 0.013]. There was no significant difference in the median PFS between patients who had previously received anthracycline-based chemotherapy and those who had not (HR = 0.74, 95% CI 0.34–1.58, p = 0.43).Conclusion: Sintilimab plus doxorubicin is a safe and promising treatment for patients with advanced STS who have failed previous systemic therapy (including anthracycline-based chemotherapy). The efficacy of this combination therapy in UPS and dedifferentiated liposarcoma is superior to that in other sarcomas.Clinical Trial Registration:https://www.chictr.org.cn, registration number: ChiCTR1900027009