Large mass-independent sulphur isotope anomalies link stratospheric volcanism to the Late Ordovician mass extinction

Volcanic eruptions are thought to be a key driver of rapid climate perturbations over geological time, such as global cooling, global warming, and changes in ocean chemistry. However, identification of stratospheric volcanic eruptions in the geological record and their causal link to the mass extinction events during the past 540 million years remains challenging. Here we report unexpected, large mass-independent sulphur isotopic compositions of pyrite with Δ33S of up to 0.91‰ in Late Ordovician sedimentary rocks from South China. The magnitude of the Δ33S is similar to that discovered in ice core sulphate originating from stratospheric volcanism. The coincidence between the large Δ33S and the first pulse of the Late Ordovician mass extinction about 445 million years ago suggests that stratospheric volcanic eruptions may have contributed to synergetic environmental deteriorations such as prolonged climatic perturbations and oceanic anoxia, related to the mass extinction

Large mass-independent sulphur isotope anomalies link stratospheric volcanism to the Late Ordovician mass extinction

Abstract: Volcanic eruptions are thought to be a key driver of rapid climate perturbations over geological time, such as global cooling, global warming, and changes in ocean chemistry. However, identification of stratospheric volcanic eruptions in the geological record and their causal link to the mass extinction events during the past 540 million years remains challenging. Here we report unexpected, large mass-independent sulphur isotopic compositions of pyrite with Δ33S of up to 0.91‰ in Late Ordovician sedimentary rocks from South China. The magnitude of the Δ33S is similar to that discovered in ice core sulphate originating from stratospheric volcanism. The coincidence between the large Δ33S and the first pulse of the Late Ordovician mass extinction about 445 million years ago suggests that stratospheric volcanic eruptions may have contributed to synergetic environmental deteriorations such as prolonged climatic perturbations and oceanic anoxia, related to the mass extinction

Pulsed laser-deposited n-Si/NiO_x photoanodes for stable and efficient photoelectrochemical water splitting

An electrocatalytic and stable nickel oxide (NiO_x) thin layer was successfully deposited on an n-Si (100) substrate by pulsed laser deposition (PLD), acting as a photoanode for efficient photo-oxidation of water under solar illumination. It was revealed that the formed n-Si/NiO_x heterojunction with good Schottky contact could improve photogenerated charge separation, and thus n-Si photoanodes deposited with a 105 nm-thick NiO_x electrocatalytic layer exhibited a photovoltage of ∼350 mV, leading to greatly improved photoelectrochemical performances for water oxidation. The stability of the photoanode was significantly enhanced with the increasing thickness of NiO_x protective layers. This study demonstrates a simple and effective method to enable the use of planar n-Si (100) substrates as efficient and durable photoanodes for practical solar water oxidation

Electrochemical mechanical micromachining based on confined etchant layer technique

National Science Foundation of China [91023006, 91023047, 91023043, 21061120456, 21021002]; Natural Science Foundation of Fujian Province of China [2012J06004]; Fundamental Research Funds for the Central Universities [2010121022]; Scientific Research Foundation for the Returned Overseas Chinese Scholars (State Education Ministry)The confined etchant layer technique (CELT) has been proved an effective electrochemical microfabrication method since its first publication at Faraday Discussions in 1992. Recently, we have developed CELT as an electrochemical mechanical micromachining (ECMM) method by replacing the cutting tool used in conventional mechanical machining with an electrode, which can perform lathing, planing and polishing. Through the coupling between the electrochemically induced chemical etching processes and mechanical motion, ECMM can also obtain a regular surface in one step. Taking advantage of CELT, machining tolerance and surface roughness can reach micro-or nano-meter scale

Rapid Identification of Waste Cooking Oil with Near Infrared Spectroscopy Based on Support Vector Machine

International audienceThe qualitative model for rapidly discriminating the waste oil and four normal edible vegetable oils is developed using near infrared spectroscopy combined with support vector machine (SVM). Principal component analysis (PCA) has been carried out on the base of the combination of spectral pretreatment of vector normalization, first derivation and nine point smoothing, and seven principal components are selected. The radial basis function (RBF) is used as the kernel function; the penalty parameter C and kernel function parameter γ are optimized by K-fold Cross Validation (K-CV), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), respectively. The result shows that the best classification model is developed by GA optimization when the parameters C = 911.33, γ= 2.91. The recognition rate of the model for 208 samples in training set and 85 samples in prediction set is 100% and 90.59%, respectively. By comparison with K-means and Linear Discriminant Analysis (LDA), the result indicates that the SVM recognition rate is higher, well generalization, can quickly and accurately identify the waste cooking oil and normal edible vegetable oils

Tighter Constraints of Multi-Qubit Entanglement in Terms of Nonconvex Entanglement Measures LCREN and LCRENoA

The monogamy property of entanglement is an intriguing feature of multipartite quantum entanglement. Most entanglement measures satisfying the monogamy inequality have turned out to be convex. Whether nonconvex entanglement measures obey the monogamy inequalities remains less known at present. As a well-known measure of entanglement, the logarithmic negativity is not convex. We elucidate the constraints of multi-qubit entanglement based on the logarithmic convex-roof extended negativity (LCREN) and the logarithmic convex-roof extended negativity of assistance (LCRENoA). Using the Hamming weight derived from the binary vector associated with the distribution of subsystems, we establish monogamy inequalities for multi-qubit entanglement in terms of the αth-power (α≥4ln2) of LCREN, and polygamy inequalities utilizing the αth-power (0≤α≤2) of LCRENoA. We demonstrate that these inequalities give rise to tighter constraints than the existing ones. Furthermore, our monogamy inequalities are shown to remain valid for the high-dimensional states that violate the CKW monogamy inequality. Detailed examples are presented to illustrate the effectiveness of our results in characterizing the multipartite entanglement distributions

Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study

BackgroundWith the prevalence of online consultation, many patient-doctor dialogues have accumulated, which, in an authentic language environment, are of significant value to the research and development of intelligent question answering and automated triage in recent natural language processing studies. ObjectiveThe purpose of this study was to design a front-end task module for the network inquiry of intelligent medical services. Through the study of automatic labeling of real doctor-patient dialogue text on the internet, a method of identifying the negative and positive entities of dialogues with higher accuracy has been explored. MethodsThe data set used for this study was from the Spring Rain Doctor internet online consultation, which was downloaded from the official data set of Alibaba Tianchi Lab. We proposed a composite abutting joint model, which was able to automatically classify the types of clinical finding entities into the following 4 attributes: positive, negative, other, and empty. We adapted a downstream architecture in Chinese Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) with whole word masking (WWM) extended (RoBERTa-WWM-ext) combining a text convolutional neural network (CNN). We used RoBERTa-WWM-ext to express sentence semantics as a text vector and then extracted the local features of the sentence through the CNN, which was our new fusion model. To verify its knowledge learning ability, we chose Enhanced Representation through Knowledge Integration (ERNIE), original Bidirectional Encoder Representations from Transformers (BERT), and Chinese BERT with WWM to perform the same task, and then compared the results. Precision, recall, and macro-F1 were used to evaluate the performance of the methods. ResultsWe found that the ERNIE model, which was trained with a large Chinese corpus, had a total score (macro-F1) of 65.78290014, while BERT and BERT-WWM had scores of 53.18247117 and 69.2795315, respectively. Our composite abutting joint model (RoBERTa-WWM-ext + CNN) had a macro-F1 value of 70.55936311, showing that our model outperformed the other models in the task. ConclusionsThe accuracy of the original model can be greatly improved by giving priority to WWM and replacing the word-based mask with unit to classify and label medical entities. Better results can be obtained by effectively optimizing the downstream tasks of the model and the integration of multiple models later on. The study findings contribute to the translation of online consultation information into machine-readable information