International code for ships operating in polar waters: challenges to polar shipping safety rules in China

With the sea-ice diminishing steadily in the polar regions, there has been growing interest in new transit routes through polar waters using cost-effective transportation. Among the international regulators over polar shipping, the International Maritime Organization (IMO) is the leading body concerned with drafting marine safety and environmental protection rules. The mandatory Polar Code (International Code for Ships Operating in Polar Waters) adopted by the IMO signals the consensus among maritime states to apply compulsory rules to vessels operating in Arctic and Antarctic waters. As the standing member of the IMO and a major global shipping power, China is preparing to adopt national regulatory standards to develop an adequate vessel infrastructure and crew training system. Proceeding in parallel with the developing polar shipping industry, China will also move ahead in comprehensive collaboration with the Nordic states regarding polar issues

MELA: Multilingual Evaluation of Linguistic Acceptability

Recent benchmarks for Large Language Models (LLMs) have mostly focused on application-driven tasks such as complex reasoning and code generation, and this has led to a scarcity in purely linguistic evaluation of LLMs. Against this background, we introduce Multilingual Evaluation of Linguistic Acceptability -- MELA, the first multilingual benchmark on linguistic acceptability with 48K samples covering 10 languages from a diverse set of language families. We establish baselines of commonly used LLMs along with supervised models, and conduct cross-lingual transfer and multi-task learning experiments with XLM-R. In pursuit of multilingual interpretability, we analyze the weights of fine-tuned XLM-R to explore the possibility of identifying transfer difficulty between languages. Our results show that ChatGPT benefits much from in-context examples but still lags behind fine-tuned XLM-R, while the performance of GPT-4 is on par with fine-tuned XLM-R even in zero-shot setting. Cross-lingual and multi-task learning experiments show that unlike semantic tasks, in-language training data is crucial in acceptability judgements. Results in layerwise probing indicate that the upper layers of XLM-R become a task-specific but language-agnostic region for multilingual acceptability judgment. We also introduce the concept of conflicting weight, which could be a potential indicator for the difficulty of cross-lingual transfer between languages. Our data will be available at https://github.com/sjtu-compling/MELA.Comment: Work in progres

Revisiting Acceptability Judgements

In this work, we revisit linguistic acceptability in the context of large language models. We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale acceptability dataset for a non-Indo-European language. It is verified by native speakers and is the first acceptability dataset that comes with two sets of labels: a linguist label and a crowd label. Our experiments show that even the largest InstructGPT model performs only at chance level on CoLAC, while ChatGPT's performance (48.30 MCC) is also much below supervised models (59.03 MCC) and human (65.11 MCC). Through cross-lingual transfer experiments and fine-grained linguistic analysis, we provide detailed analysis of the model predictions and demonstrate for the first time that knowledge of linguistic acceptability can be transferred across typologically distinct languages, as well as be traced back to pre-training. Our dataset is publicly available at \url{https://github.com/huhailinguist/CoLAC}

Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases. Therefore, it is important to develop an efficient heart segmentation algorithm.Methods: A total of 275 nuclear magnetic resonance imaging (MRI) heart scans were collected, analyzed, and preprocessed from Huaqiao University Affiliated Strait Hospital, and the data were used in our improved deep learning model, which was designed based on the U-net network. The training set included 80% of the images, and the remaining 20% was the test set. Based on five time phases from end-diastole (ED) to end-systole (ES), the segmentation findings showed that it is possible to achieve improved segmentation accuracy and computational complexity by segmenting the left ventricle (LV), right ventricle (RV), and myocardium (myo).Results: We improved the Dice index of the LV to 0.965 and 0.921, and the Hausdorff index decreased to 5.4 and 6.9 in the ED and ES phases, respectively; RV Dice increased to 0.938 and 0.860, and the Hausdorff index decreased to 11.7 and 12.6 in the ED and ES, respectively; myo Dice increased to 0.889 and 0.901, and the Hausdorff index decreased to 8.3 and 9.2 in the ED and ES, respectively.Conclusion: The model obtained in the final experiment provided more accurate segmentation of the left and right ventricles, as well as the myocardium, from cardiac MRI. The data from this model facilitate the prediction of cardiovascular disease in real-time, thereby providing potential clinical utility

Understanding Regulatory Mechanisms of Brain Function and Disease through 3D Genome Organization

The human genome has a complex and dynamic three-dimensional (3D) organization, which plays a critical role for gene regulation and genome function. The importance of 3D genome organization in brain development and function has been well characterized in a region- and cell-type-specific fashion. Recent technological advances in chromosome conformation capture (3C)-based techniques, imaging approaches, and ligation-free methods, along with computational methods to analyze the data generated, have revealed 3D genome features at different scales in the brain that contribute to our understanding of genetic mechanisms underlying neuropsychiatric diseases and other brain-related traits. In this review, we discuss how these advances aid in the genetic dissection of brain-related traits

Evaluation and improvement of workplace vertical violence of nursing interns based on the Importance-Performance Analysis method

PurposeTo analyze the key factors related to workplace vertical violence among nursing interns in China and to propose strategies to improve the nursing practice environment.MethodsA cross-sectional study was conducted using the Importance-Performance Analysis (IPA) method to analyze the key factors and significance of workplace vertical violence for nursing interns. The data were obtained by administering a workplace vertical violence survey, designed specifically for this study, to 120 nursing interns at a tertiary general hospital in Zhejiang Province, China.ResultsThe results demonstrated that the variables “I was ordered to do something beyond my ability and lacked guidance (C3),” “Errors in work have been repeatedly emphasized, spread, or exaggerated (C8),” “I was unjustly criticized (C9),” “I was withheld or blocked information purposefully (C1),” and “I was belittled at work (C2)” were the most crucial variables for determining the presence of workplace vertical violence of nursing interns. Moreover, they are priority improvement variables.ConclusionManagers must prioritize the use of relevant resources during internships to minimize false reinforcement and unfair criticism. Efforts should focus on improving information sharing, emphasizing the role of nursing interns in clinical work, providing better guidance when arranging for nursing interns to do work that exceeds their capacity, reducing workplace vertical violence, and improving nursing intern practice environments

Dissimilatory nitrate reduction processes in surface sediments of shrimp ponds during the culture period

Intensive aquaculture in estuaries and coasts has resulted in several ecological and environmental problems. Among various nitrogen transformation pathway, dissimilatory nitrate (NO3-) reduction is considered to be highly important in regulating reactive nitrogen. However, there are relatively few studies on the processes and contribution of NOx- reduction in sediment during the shrimp pond culture period. Three sediment NO3- reduction processes, denitrification (DNF), anaerobic ammonium oxidation (ANA), and dissimilatory NO3- reduction to ammonium (DNRA), were surveyed in eight shrimp ponds across three subtropical estuaries using 15N isotope tracing experiments. The rates of DNF, ANA and DNRA ranged from 2.87–18.11, 0.10–1.92, and 0.21–1.25 nmol N g -1 h -1, respectively. DNF was responsible for 64.2–91.6% of the total NO3- reduction. Regarding environmental factors, C and N substrates, as well as salinity, significantly affected NO3- reduction. In general, the N losses were approximately 32.43–131.64 g N m-2 yr-1 for DNF and 2.38–15.85 g N m-2 yr-1 for ANA in this study, indicating that coastal reclamation is a nonnegligible way to remove nitrogen. Our results provide a scientific foundation for understanding the mechanism of nitrogen cycling in the artificial aquatic environment of shrimp ponds

Identification of Amino Acids Essential for Estrone-3-Sulfate Transport within Transmembrane Domain 2 of Organic Anion Transporting Polypeptide 1B1

As an important structure in membrane proteins, transmembrane domains have been found to be crucial for properly targeting the protein to cell membrane as well as carrying out transport functions in transporters. Computer analysis of OATP sequences revealed transmembrane domain 2 (TM2) is among those transmembrane domains that have high amino acid identities within different family members. In the present study, we identify four amino acids (Asp70, Phe73, Glu74, and Gly76) that are essential for the transport function of OATP1B1, an OATP member that is specifically expressed in the human liver. A substitution of these four amino acids with alanine resulted in significantly reduced transport activity. Further mutagenesis showed the charged property of Asp70 and Glu74 is critical for proper function of the transporter protein. Comparison of the kinetic parameters indicated that Asp70 is likely to interact with the substrate while Glu74 may be involved in stabilizing the binding site through formation of a salt-bridge. The aromatic ring structure of Phe73 seems to play an important role because substitution of Phe73 with tyrosine, another amino acid with a similar structure, led to partially restored transport function. On the other hand, replacement of Gly76 with either alanine or valine could not recover the function of the transporter. Considering the nature of a transmembrane helix, we proposed that Gly76 may be important for maintaining the proper structure of the protein. Interestingly, when subjected to transport function analysis of higher concentration of esteone-3-sulfate (50 µM) that corresponds to the low affinity binding site of OATP1B1, mutants of Phe73, Glu74, and Gly76 all showed a transport function that is comparable to that of the wild-type, suggesting these amino acids may have less impact on the low affinity component of esteone-3-sulfate within OATP1B1, while Asp 70 seems to be involved in the interaction of both sites

A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method

In order to reduce the air pollution impacts by aircraft operations around airports, a fast and accurate prediction of air quality related to aircraft operations is an essential prerequisite. This article proposes a new framework with a combination of the standard assessment procedure and machine learning methods for fast and accurate prediction of air quality in airports. Instead of taking some specific pollutant as concerned metric, we introduce the air quality index (AQI) for the first time to evaluate the air quality in airports. Then, following the standard assessment procedure proposed by International Civil Aviation Organization (ICAO), the airports AQIs in different scenarios are classified with consideration of the airport configuration, actual flight operations, aircraft performance, and related meteorological data. Taking the AQI classification results as sample data, several popular supervised learning methods are investigated for accurately predicting air quality in airports. The numerical tests implicate that the accuracy rate of prediction could reach more than 95% with only 0.022 sec; the proposed framework and the results could be used as the foundation for improving air quality impacts around airports

Thermo-mechanical Numerical Analysis for Distortions Introduced in Titanium Alloy Blades by Post-forge Cooling

AbstractThis paper presents a numerical method to predict the distortions of alloy blades, caused by uneven temperature distribution and its thermal stress in cooling phase. A simplified thermo-mechanical model is presented, considering the actual production conditions. A simulation model of TC11Titanium Alloy Blade is established and performed to evaluate the transient temperature field and deformation by using ANSYS. The simulation results also are compared to the experiment results at the end