Emission charge and liner shipping network configuration ‐ an economic investigation of the Asia‐Europe route

This paper models shipping lines’ operational costs and CO2 emissions under alternative geographic network configurations when an emission charge is imposed on operations from Asia to Europe. Our modeling results suggest that shipping firms’ network configuration is influenced by emission charge, fuel price, port loading and unloading cost, and demand pattern of cargo transport across different markets. Total emission will be reduced by an EU emission charge scheme. However, if the charge is above a threshold, carriers will reconfigure shipping networks to minimize their costs including emission charge payments. This will offset part of the emission reduction achieved by the emission scheme. As a result, a higher charge does not always lead to a higher emission reduction. In addition, the performance of major ports along the Asia-Europe routes will be influenced in different ways, leading to conflicting views from regional governments. These findings reveal possible market distortions associated with regional emission systems, and highlight the complex effects of international environmental policies when market dynamics are considered

Contrastive Label Disambiguation for Self-Supervised Terrain Traversability Learning in Off-Road Environments

Discriminating the traversability of terrains is a crucial task for autonomous driving in off-road environments. However, it is challenging due to the diverse, ambiguous, and platform-specific nature of off-road traversability. In this paper, we propose a novel self-supervised terrain traversability learning framework, utilizing a contrastive label disambiguation mechanism. Firstly, weakly labeled training samples with pseudo labels are automatically generated by projecting actual driving experiences onto the terrain models constructed in real time. Subsequently, a prototype-based contrastive representation learning method is designed to learn distinguishable embeddings, facilitating the self-supervised updating of those pseudo labels. As the iterative interaction between representation learning and pseudo label updating, the ambiguities in those pseudo labels are gradually eliminated, enabling the learning of platform-specific and task-specific traversability without any human-provided annotations. Experimental results on the RELLIS-3D dataset and our Gobi Desert driving dataset demonstrate the effectiveness of the proposed method.Comment: 9 pages, 11 figure

A Fatty Acid Glycoside from a Marine-Derived Fungus Isolated from Mangrove Plant Scyphiphora hydrophyllacea

To study the antimicrobial components from the endophytic fungus A1 of mangrove plant Scyphiphora hydrophyllacea Gaertn. F., a new fatty acid glucoside was isolated by column chromatography from the broth of A1, and its structure was identified as R-3-hydroxyundecanoic acid methylester-3-O-α-l-rhamnopyranoside (1) by spectroscopic methods including 1D and 2D NMR (HMQC, 1H-1H COSY and HMBC) and chemical methods. Antimicrobial assay showed compound 1 possessed modest inhibitory effect on Saphylococcus aureus and methicillin-resistant S. aureus (MRSA) using the filter paper disc agar diffusion method

Decomposing Star Formation and Active Galactic Nucleus with Spitzer Mid-Infrared Spectra: Luminosity Functions and Co-Evolution

We present Spitzer 7-38um spectra for a 24um flux limited sample of galaxies at z~0.7 in the COSMOS field. The detailed high-quality spectra allow us to cleanly separate star formation (SF) and active galactic nucleus (AGN) in individual galaxies. We first decompose mid-infrared Luminosity Functions (LFs). We find that the SF 8um and 15um LFs are well described by Schechter functions. AGNs dominate the space density at high luminosities, which leads to the shallow bright-end slope of the overall mid-infrared LFs. The total infrared (8-1000um) LF from 70um selected galaxies shows a shallower bright-end slope than the bolometrically corrected SF 15um LF, owing to the intrinsic dispersion in the mid-to-far-infrared spectral energy distributions. We then study the contemporary growth of galaxies and their supermassive black holes (BHs). Seven of the 31 Luminous Infrared Galaxies with Spitzer spectra host luminous AGNs, implying an AGN duty cycle of 23+/-9%. The time-averaged ratio of BH accretion rate and SF rate matches the local M_BH-M_bulge relation and the M_BH-M_host relation at z ~ 1. These results favor co-evolution scenarios in which BH growth and intense SF happen in the same event but the former spans a shorter lifetime than the latter. Finally, we compare our mid-infrared spectroscopic selection with other AGN identification methods and discuss candidate Compton-thick AGNs in the sample. While only half of the mid-infrared spectroscopically selected AGNs are detected in X-ray, ~90% of them can be identified with their near-infrared spectral indices.Comment: ApJ Accepted. emulateapj style. 16 pages, 9 figures, 4 table

Identification and drug metabolic characterization of four new CYP2C9 variants CYP2C9*72-*75 in the Chinese Han population

Cytochrome 2C9 (CYP2C9), one of the most important drug metabolic enzymes in the human hepatic P450 superfamily, is required for the metabolism of 15% of clinical drugs. Similar to other CYP2C family members, CYP2C9 gene has a high genetic polymorphism which can cause significant racial and inter-individual differences in drug metabolic activity. To better understand the genetic distribution pattern of CYP2C9 in the Chinese Han population, 931 individuals were recruited and used for the genotyping in this study. As a result, seven synonymous and 14 non-synonymous variations were identified, of which 4 missense variants were designated as new alleles CYP2C9*72, *73, *74 and *75, resulting in the amino acid substitutions of A149V, R150C, Q214H and N418T, respectively. When expressed in insect cell microsomes, all four variants exhibited comparable protein expression levels to that of the wild-type CYP2C9 enzyme. However, drug metabolic activity analysis revealed that these variants exhibited significantly decreased catalytic activities toward three CYP2C9 specific probe drugs, as compared with that of the wild-type enzyme. These data indicate that the amino acid substitution in newly designated variants can cause reduced function of the enzyme and its clinical significance still needs further investigation in the future

H2S gas sensing performance and mechanisms using CuO-Al2O3 composite films based on both surface acoustic wave and chemiresistor techniques

Surface acoustic wave and chemiresistor based gas sensors integrated with a sensing layer of sol-gel CuO-Al2O3 composite film were fabricated and their performance and mechanisms for H2S sensing were characterized and compared. In the composite film, CuO nanoparticles provide active sites for adsorption and reaction of H2S molecules while Al2O3 nanoparticles help to form a uniform and mesoporous film structure, both of which enhance the sensitivity of the sensors by providing numerous active CuO surfaces. Through the comparative studies, the SAW based H2S sensor operated at room temperature showed a lower detection limit, higher sensitivity, better linearity and good selectivity to H2S gas with its concentration ranging from 5 ppb to 100 ppm, compared with those of the chemiresistor sensor, which are mainly attributed to the effective mass sensing properties of the SAW sensor, because a minor change in the mass of the film caused by adsorbed H2S molecules would lead to a significant and monotonous change of the resonant frequency of the SAW devices

Perceived stigma among discharged patients of COVID-19 in Wuhan, China: A latent profile analysis

BackgroundPerceived stigma has greatly influenced the life quality of the COVID-19 patients who recovered and were discharged (RD hereafter). It is essential to understand COVID-19 stigma of RD and its related risk factors. The current study aims to identify the characteristics of perceived COVID-19 stigma in RD using latent profile analysis (LPA), to explore its psycho-social influencing factors, and to determine the cut-off point of the stigma scale using receiver operating characteristic (ROC) analysis.MethodsA cross-sectional study was conducted among COVID-19 RD in 13 communities in Jianghan District, Wuhan City, Hubei Province, China from June 10 to July 25, 2021, enrolling total 1,297 participants. Data were collected on demographic characteristics, COVID-19 perceived stigma, post-traumatic stress disorder (PTSD), anxiety, depression, sleep disorder, fatigue, resilience, social support, and peace of mind. LPA was performed to identify different profiles of perceived COVID-19 stigma level. Univariate analysis and multinominal logistic regression analysis were conducted to explore the influencing factors in different profiles. ROC analyses was carried out to identify the cut-off value of perceived stigma.ResultsAmong the participants, three profiles of perceived stigma were identified: “low perceived COVID-19 stigma” (12.8%), “moderate perceived COVID-19 stigma” (51.1%), and “severe perceived COVID-19 stigma” (36.1%). Multinominal logistic regression analysis revealed that older age, living with other people, anxiety, and sleep disorder were positively associated with moderate perceived COVID-19 stigma, while higher educational level was negatively associated with moderate perceived COVID-19 stigma. Female, older age, living with other people, anxiety, and sleep disorder were positively associated with severe perceived COVID-19 stigma, while higher educational level, social support, and peace of mind were negatively associated with severe perceived COVID-19 stigma. ROC curve of the Short Version of COVID-19 Stigma Scale (CSS-S) for screening perceived COVID-19 stigma showed that the optimal cut-off value was ≥ 20.ConclusionThe study focuses on the issue of perceived COVID-19 stigma and its psycho-socio influencing factors. It provides evidence for implementing relevant psychological interventions to COVID-19 RD