A nonlocal curve flow in centro-affine geometry

In this paper, the isoperimetric inequality in centro-affine plane geometry is obtained. We also investigate the long-term behavior of an invariant plane curve flow, whose evolution process can be expressed as a second-order nonlinear parabolic equation with respect to centro-affine curvature. The forward and backward limits in time are discussed, which shows that a closed convex embedded curve may converge to an ellipse when evolving according to this flow

Effect of the Dispersibility of Nano-CuO Catalyst on Heat Releasing of AP/HTPB Propellant

Kneading time is adjusted to change the dispersibility of nano-CuO in AP/HTPB (Ammonia Perchlorate/Hydroxyl-Terminated Polybutadiene) composite propellants. Nano-CuO/AP is prepared to serve as the other dispersing method of nano-CuO, named predispersing procedure. Several kinds of heat releasing, thermal decomposition by DSC, combustion heat in oxygen environment, and explosion heat in nitrogen environment, are characterized to learn the effect of dispersibility of nano-CuO catalyst on heat releasing of propellants. With pre-dispersing procedures, thermal decomposition temperature of nano-CuO/AP and its propellant are about 25∘C and 8.6∘C lower than that of AP simple mixed with nano-CuO and its propellant, respectively. Comparing propellant with simple mixed nano-CuO kneading 3 hours, combustion heat and explosion heat of propellant with nano-CuO/AP increase about 1.4% and 1.7%, respectively. However, because of the breaking of nano-CuO/AP structure during kneading procedure, combustion heat and explosion heat of all the samples are decreased with the increase of kneading time after 3 hours

The roles of radiative, structural and physiological information of sun-induced chlorophyll fluorescence in predicting gross primary production of a corn crop at various temporal scales

Extensive research suggests that sun-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) have a near-linear relationship, providing a promising avenue for estimating the carbon uptake of ecosystems. However, the factors influencing the relationship are not yet clear. This study examines the roles of SIF's radiative, structural, and physiological information in predicting GPP, based on four years of field observations of a corn canopy at various temporal scales. We quantified SIF's radiative component by measuring the intensity of incident photosynthetically active radiation (iPAR), and separated the structural and physiological components from SIF observations using the fluorescence correction vegetation index (FCVI). Our results show that the R2 values between SIF and GPP, as estimated by linear models, increased from 0.66 at a half-hour resolution to 0.86 at a one-month resolution. In comparison, the product of FCVI and iPAR, representing the non-physiological information of SIF, performed consistently well in predicting GPP with R2&gt;0.84 at various temporal scales, suggesting a limited contribution of the physiological information of SIF for GPP estimation. The results further reveal that SIF's radiative and structural components positively impacted the SIF-GPP linearity, while the physiological component had a negative impact on the linearity for most cases, changing from 0.6 % to -27.5 %. As for the temporal dependency, the controls of the SIF-GPP relationship moved from radiation at diurnal scales to structure at the seasonal scales. The structural contribution changed from 14.8 % at a half-hour resolution to 92.4 % at a one-month resolution, while the radiative contribution decreased from 118.0 % at a half-hour resolution to 11.7 % at a one-month resolution. This study contributes to enhancing our understanding of the physiological information conveyed by SIF and the factors influencing the temporal dependency of the SIF-GPP relationship.</p

Association between Polymorphisms of ERCC1 and Response in Patients with Advanced Non-small Cell Lung Cancer Receiving Cisplatin-based Chemotherapy

Background and objective Results of studies on genetic polymorphisms of ERCC1 gene in DNA repair pathway which may affect response to platinum-based chemotherapy and survival in patients with non-small cell lung cancer are conflicting. The aim of this study is to prospectively assess the association between single nucleotide polymorphisms of C8092A and codon118 in ERCC1 and drug response in 90 patients with advanced non-small cell lung cancer treated with cisplatin-based chemotherapy. Methods All patients were treated with cisplatin-based chemotherapy. Genotypes of ERCC1 C8092A and codon118 were examined by sequencing, and the association between genotypes and response was evaluated. Results Genotype frequencies of ERCC1 C8092A were CC 40.0% (36/90), CA 48.9% (44/90) and AA 11.1% (10/90), frequencies of codon118 were CC 58.9% (53/90), CT 34.4% (31/90) and TT 6.7% (6/90). There was no significant difference in response rate of patients carrying with CC, compared with CA plus AA in C8092A (33.3% vs 29.6%, P=0.71). Response rate of patients carrying with CC in ERCC1 118 was 32.1%, 24.3% with CT plus CC (P=0.43). There was no difference in progression free survival between patients carrying with CC and CT plus TT in C8092A (5.2 months vs 5.4 months, P=0.62). There was no difference in progression free survival between patients carrying with CC and CA plus AA (5.5 months vs 5.3 months, P=0.59). Conclusion The results suggest that there is no association between polymorphisms in ERCC1 C8092A and codon118 and response in patients with advanced non-small cell lung cancer receiving cisplatin-based chemotherapy

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model

Most biomedical pretrained language models are monolingual and cannot handle the growing cross-lingual requirements. The scarcity of non-English domain corpora, not to mention parallel data, poses a significant hurdle in training multilingual biomedical models. Since knowledge forms the core of domain-specific corpora and can be translated into various languages accurately, we propose a model called KBioXLM, which transforms the multilingual pretrained model XLM-R into the biomedical domain using a knowledge-anchored approach. We achieve a biomedical multilingual corpus by incorporating three granularity knowledge alignments (entity, fact, and passage levels) into monolingual corpora. Then we design three corresponding training tasks (entity masking, relation masking, and passage relation prediction) and continue training on top of the XLM-R model to enhance its domain cross-lingual ability. To validate the effectiveness of our model, we translate the English benchmarks of multiple tasks into Chinese. Experimental results demonstrate that our model significantly outperforms monolingual and multilingual pretrained models in cross-lingual zero-shot and few-shot scenarios, achieving improvements of up to 10+ points. Our code is publicly available at https://github.com/ngwlh-gl/KBioXLM

Tank container operators’ profit maximization through dynamic operations planning integrated with the quotation-booking process under multiple uncertainties

Tank Container Operators (TCOs) are striving to maximize profit through the integration of their global Tank Container (TC) operations with the job quotation-booking process. However, TCOs face a set of unique challenges not faced by general shipping container operators, including the process uncertainties arising from TC cleaning and the use of Freight Forwarders (FFs). In this paper, a simulation-based two-stage optimization model is developed to address these challenges. The first stage focuses on tactical decisions of setting inventory levels and control policy for empty container repositioning. The second stage integrates the dynamic job acceptance/rejection decisions in the quotation-booking processes with container operations decisions in the planning and execution processes, such as job fulfilment, container leasing terms, choice of FFs considering cost and reliability, and empty tank container repositioning. The solution procedure is based on the simulation model combined with heuristic algorithms including an adjusted Genetic Algorithm, mathematical programming, and heuristic rules. Numerical examples based on a real case study are provided to illustrate the effectiveness of the model.Tank Container Operators (TCOs) are striving to maximize profit through the integration of their global Tank Container (TC) operations with the job quotation-booking process. However, TCOs face a set of unique challenges not faced by general shipping container operators, including the process uncertainties arising from TC cleaning and the use of Freight Forwarders (FFs). In this paper, a simulation-based two-stage optimization model is developed to address these challenges. The first stage focuses on tactical decisions of setting inventory levels and control policy for empty container repositioning. The second stage integrates the dynamic job acceptance/rejection decisions in the quotation-booking processes with container operations decisions in the planning and execution processes, such as job fulfillment, container leasing terms, choice of FFs considering cost and reliability, and empty tank container repositioning. The solution procedure is based on the simulation model combined with heuristic algorithms including an adjusted Genetic Algorithm, mathematical programming, and heuristic rules. Numerical examples based on a real case study are provided to illustrate the effectiveness of the model