Numerical simulation on the impact dynamics of a novel rotation air hammer and experimental research

Novel rotation air hammer (NRAH) is a rock-breaking tool in the gas drilling. The rock-breaking ability of the NRAH is mainly from the collision between piston and drill bit in it. The collision makes the piston and the drill bit suffer from a high instantaneous impact stress, so that they are prone to failure. Thus, both of them are not only the most crucial parts of the NRAH, but also the easily damaged parts. So it is necessary to analyze the impact stress in them and optimize their structure to improve the security and reliability. First of all, we analyzed the working mechanism of the NRAH to understand motion and structure of the piston and the drill bit. Then we used the LS-DYNA program to analyze impact dynamics problem of the piston and the drill bit to obtain their stress change rule in the impact process. According to the structure optimization, the security coefficient of the piston and the drill bit has been obviously improved. Moreover, their energy conversion regulations were studied in the impact process of the NRAH and the last impacting velocity of the piston was computed, which can provide helpful for effective application of this tool in the field. Finally, based on the experimental study on the NRAH after the optimization, we found that its function had satisfied the design requirements as well as overall performance was improved

Curcumin Blocks Small Cell Lung Cancer Cells Migration, Invasion, Angiogenesis, Cell Cycle and Neoplasia through Janus Kinase-STAT3 Signalling Pathway

Curcumin, the active component of turmeric, has been shown to protect against carcinogenesis and prevent tumor development. However, little is known about its anti-tumor mechanism in small cell lung cancer (SCLC). In this study, we found that curcumin can inhibit SCLC cell proliferation, cell cycle, migration, invasion and angiogenesis through suppression of the STAT3. SCLC cells were treated with curcumin (15 µmol/L) and the results showed that curcumin was effective in inhibiting STAT3 phosphorylation to downregulate of an array of STAT3 downstream targets ,which contributed to suppression of cell proliferation, loss of colony formation, depression of cell migration and invasion. Curcumin also suppressed the expression of proliferative proteins (Survivin, Bcl-XL and Cyclin B1), and invasive proteins (VEGF, MMP-2, MMP-7 and ICAM-1).Knockdown of STAT3 expression by siRNA was able to induce anti-invasive effects in vitro. In contrast, activation of STAT3 upstream of interleukin 6 (IL-6) leads to the increased cell proliferation ,cell survival, angiogenesis, invasion, migration and tumor growth. Our findings illustrate the biologic significance of IL-6/JAK/STAT3 signaling in SCLC progression and providenovel evidence that the pathway may be a new potential target for therapy of SCLC. It was concluded that curcumin is a potent agent in the inhibition of STAT3 with favorable pharmacological activity,and curcumin may have translational potential as an effective cancer therapeutic or preventive agent for SCLC

1-Benzyl-4-chloro­indoline-2,3-dione

There are two independent mol­ecules in the asymmetric unit of the title compound, C15H10ClNO2, which differ in the dihedral angles between the mean planes of the phenyl ring and the 4-chloro­indoline-2,3-dione ring system [59.48 (9) and 79.0 (1)°]. In the crystal, mol­ecules are linked through C—H⋯O hydrogen bonds, forming polymeric chains in [100]

DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning

Model-based reinforcement learning (RL), which learns environment model from offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in offline RL. Due to the gap between the learned and actual environment, conservatism should be incorporated into the algorithm to balance accurate offline data and imprecise model data. The conservatism of current algorithms mostly relies on model uncertainty estimation. However, uncertainty estimation is unreliable and leads to poor performance in certain scenarios, and the previous methods ignore differences between the model data, which brings great conservatism. Therefore, this paper proposes a milDly cOnservative Model-bAsed offlINe RL algorithm (DOMAIN) without estimating model uncertainty to address the above issues. DOMAIN introduces adaptive sampling distribution of model samples, which can adaptively adjust the model data penalty. In this paper, we theoretically demonstrate that the Q value learned by the DOMAIN outside the region is a lower bound of the true Q value, the DOMAIN is less conservative than previous model-based offline RL algorithms and has the guarantee of security policy improvement. The results of extensive experiments show that DOMAIN outperforms prior RL algorithms on the D4RL dataset benchmark, and achieves better performance than other RL algorithms on tasks that require generalization.Comment: 13 pages, 6 figure

Risk factors associated with the severity of diabetic retinopathy in Qingdao

AIM:To investigate and analyse the prevalence and risk factors associated with diabetic retinopathy severity in Qingdao.METHODS: This survey consisted of the 2 following parts: 2859 community residents aged >60 years old and 4275 patients with T2DM who were older than 30 years old in Qingdao. Ophthalmic examinations were performed on all patients. A questionnaire was used to obtain the patient's age and gender, the duration of diabetes mellitus(DM), glycaemic control and their knowledge of diabetic retinopathy(DR). Blood pressure and haemoglobin levels were recorded. All included patients underwent a comprehensive ophthalmic examination that included a fundus examination and retinal photographs and that assigned a grade for the severity of retinopathy according to the Early Treatment Diabetic Retinopathy Study(ETDRS)severity scale. Patients with severe non-proliferative or proliferative diabetic retinopathy and clinically significant macular edema(CSME)required ophthalmic therapy were assigned to the need-treatment group, while the remaining patients with DR were assigned to the need-observation group. Correlation and regression analyses were performed to determine the required-treatment rate and risk factors for DR. Logistic regression models were used to estimate odds ratios(OR)and 95% confidence intervals(CI)after adjustment for age, gender and the duration of diabetes.RESULTS: DR was present in 334(11.68%)of the 2859 community residents aged >60 years old and 1097(25.66%)of the 4275 hospital patients with T2DM, and 48(14.81%)of the residents and 172(15.68%)of the hospital patients required ophthalmic therapy. In univariate and multivariate logistic analyses, factors including the age of the patients(51-60 years old: OR, 1.68; 95%CI, 1.21-1.72; 61-70 years old: OR, 1.55; 95%CI, 1.38-1.76), the duration of diabetes(11-15 years: OR, 2.61; 95%CI, 1.51-4.72; >15 years: OR, 4.15; 95%CI, 2.32-5.77), glycaemic control(medium: OR, 2.51; 95%CI, 1.98-3.92; poor: OR, 4.69; 95%CI, 3.39-6.95), and knowledge of DR(did not understand: OR, 1.45; 95%CI, 1.21-1.95)were significantly associated with the required-treatment rate in DR, while gender, low and advanced age(31-50 years old and >70 years old), duration of disease(CONCLUSION: The prevalence rate and the required-treatment rate in DR in Qingdao are relatively high. Being aged 51-70 years old and having a duration of diabetes >10y, poor glycaemic control and a lack of knowledge of DR were found to be potential risk factors that increased the rate of required ophthalmic therapy in patients with DR. In patients with T2DM who were aged 51-70 years old, we found that focusing on using science and education to strengthen the patients' knowledge of DR, establishing specifications for a community DR screening system, and effectively implementing early intervention in the community of DR-affected individuals were particularly important for preventing and controlling the high DR prevalence and the high rate of DR-associated blindnes

CROP: Conservative Reward for Model-based Offline Policy Optimization

Offline reinforcement learning (RL) aims to optimize policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing offline RL challenges due to their capability to mitigate the limitations of offline data through data generation using models. Prior research has demonstrated that introducing conservatism into the model or Q-function during policy optimization can effectively alleviate the prevalent distribution drift problem in offline RL. However, the investigation into the impacts of conservatism in reward estimation is still lacking. This paper proposes a novel model-based offline RL algorithm, Conservative Reward for model-based Offline Policy optimization (CROP), which conservatively estimates the reward in model training. To achieve a conservative reward estimation, CROP simultaneously minimizes the estimation error and the reward of random actions. Theoretical analysis shows that this conservative reward mechanism leads to a conservative policy evaluation and helps mitigate distribution drift. Experiments on D4RL benchmarks showcase that the performance of CROP is comparable to the state-of-the-art baselines. Notably, CROP establishes an innovative connection between offline and online RL, highlighting that offline RL problems can be tackled by adopting online RL techniques to the empirical Markov decision process trained with a conservative reward. The source code is available with https://github.com/G0K0URURI/CROP.git

Charge separation: From the topology of molecular electronic transitions to the dye/semiconductor interfacial energetics and kinetics

Charge separation properties, that is the ability of a chromophore, or a chromophore/semiconductor interface, to separate charges upon light absorption, are crucial characteristics for an efficient photovoltaic device. Starting from this concept, we devote the first part of this book chapter to the topological analysis of molecular electronic transitions induced by photon capture. Such analysis can be either qualitative or quantitative, and is presented here in the framework of the reduced density matrix theory applied to single-reference, multiconfigurational excited states. The qualitative strategies are separated into density-based and wave function-based approaches, while the quantitative methods reported here for analysing the photoinduced charge transfer nature are either fragment-based, global or statistical. In the second part of this chapter we extend the analysis to dye-sensitized metal oxide surface models, discussing interfacial charge separation, energetics and electron injection kinetics from the dye excited state to the semiconductor conduction band states