Oriented self-assembly of anisotropic layered double hydroxides (LDHs) with 2D-on-3D hierarchical structure

Layered double hydroxides (LDHs) have been the subject of increasing research due to their unique 2D or 3D structures and promising applications. However, achieving precise control over their morphology and architecture has proven to be a significant challenge. In this work, we present an oriented self-assembly strategy for the synthesis of ultrathin 2D-on-3D CoNi-LDHs nanoflowers (NFs) at ambient temperature. Ex situ and in situ characterization techniques were employed to elucidate the formation process of the 2D-on-3D CoNi-LDHs hierarchical structure. The 2D nanosheets are composed of CoNi(OH)2 seeds that undergo rapid nucleation and growth. Under the influence of oriented attachment and Ostwald ripening, the 2D nanosheets continue to crystallize along the axial and radial directions, resulting in the formation of 2D-on-3D CoNi-LDH NFs. This unique 2D-on-3D LDHs structure possesses an ultrathin thickness of approximately 1.5 nm, nanopores with a diameter of approximately 3.8 nm, and a large surface area of approximately 154 m2/g. These properties manifest excellent energy-storage performance in supercapacitors. Our approach provides important insights into the precise synthesis of LDHs with a 2D-on-3D hierarchical structure. The synthesis of LDHs with well-defined structures is a significant challenge in materials science. Our work contributes to the advancement of this field and has the potential to facilitate the development of new, high-performance energy-storage devices.publishedVersio

Integrating the extended theory of planned behavior with the stages of change to predict exercise among Chinese people with type 2 diabetes

Background: There have been very limited prospective studies examining social-cognitive models within stages of behavior change in the exercise domain. Purpose: We examined the utility of the theory of planned behavior (TPB), incorporating self-identity and descriptive norm constructs, to predict exercise behavior across the stages of change, in individuals with type 2 diabetes. Methods: Data were obtained from a longitudinal study. Multi-group structural equation modeling was used to estimate the association between extended TPB constructs and exercise within different stages groups. Results: 647 individuals completed a self-report questionnaire at baseline and at 3 months follow-up. The extended TPB model explained 8–15% variance of exercise behavior and 42–81% variance of exercise intention within three stages groups in the cross-sectional design. The extended TPB model explained 4%-13% variance of exercise behavior and 42–66% variance of exercise intention in the longitudinal design. Intention was significantly related to exercise behavior in the pre-action and action stages. Self-identity, perceived behavioral control and descriptive norms were stronger predictors of intention in different stages. Conclusion: Discontinuity patterns in the extended theory of planned behavior for the different stages groups were found. Intention was a significant predictor of exercise in the pre-action and action stages at 3 months

BatchEval: Towards Human-like Text Evaluation

Significant progress has been made in automatic text evaluation with the introduction of large language models (LLMs) as evaluators. However, current sample-wise evaluation paradigm suffers from the following issues: (1) Sensitive to prompt design; (2) Poor resistance to noise; (3) Inferior ensemble performance with static reference. Inspired by the fact that humans treat both criterion definition and inter sample comparison as references for evaluation, we propose BatchEval, a paradigm that conducts batch-wise evaluation iteratively to alleviate the above problems. We explore variants under this paradigm and confirm the optimal settings are two stage procedure with heterogeneous batch composition strategy and decimal scoring format. Comprehensive experiments across 3 LLMs on 4 text evaluation tasks demonstrate that BatchEval outperforms state-of-the-art methods by 10.5% on Pearson correlations with only 64% API cost on average. Further analyses have been conducted to verify the robustness, generalization, and working mechanism of BatchEval.Comment: 19 pages, 9 figure

Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data

Large Language Models (LLMs) have performed well on various reasoning tasks, but their inaccessibility and numerous parameters hinder wide application in practice. One promising way is distilling the reasoning ability from LLMs to small models by the generated chain-of-thought reasoning paths. In some cases, however, LLMs may produce incorrect reasoning chains, especially when facing complex mathematical problems. Previous studies only transfer knowledge from positive samples and drop the synthesized data with wrong answers. In this work, we illustrate the merit of negative data and propose a model specialization framework to distill LLMs with negative samples besides positive ones. The framework consists of three progressive steps, covering from training to inference stages, to absorb knowledge from negative data. We conduct extensive experiments across arithmetic reasoning tasks to demonstrate the role of negative data in distillation from LLM.Comment: AAAI 202

Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning

Self-consistency (SC) has been a widely used decoding strategy for chain-of-thought reasoning. Despite bringing significant performance improvements across a variety of multi-step reasoning tasks, it is a high-cost method that requires multiple sampling with the preset size. In this paper, we propose a simple and scalable sampling process, \textbf{E}arly-Stopping \textbf{S}elf-\textbf{C}onsistency (ESC), to greatly reduce the cost of SC without sacrificing performance. On this basis, one control scheme for ESC is further derivated to dynamically choose the performance-cost balance for different tasks and models. To demonstrate ESC's effectiveness, we conducted extensive experiments on three popular categories of reasoning tasks: arithmetic, commonsense and symbolic reasoning over language models with varying scales. The empirical results show that ESC reduces the average number of sampling of chain-of-thought reasoning by a significant margin on six benchmarks, including MATH (-33.8%), GSM8K (-80.1%), StrategyQA (-76.8%), CommonsenseQA (-78.5%), Coin Flip (-84.2%) and Last Letters (-67.4%), while attaining comparable performances.Comment: ICLR 202

BjTT: A Large-scale Multimodal Dataset for Traffic Prediction

Traffic prediction is one of the most significant foundations in Intelligent Transportation Systems (ITS). Traditional traffic prediction methods rely only on historical traffic data to predict traffic trends and face two main challenges. 1) insensitivity to unusual events. 2) limited performance in long-term prediction. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation, and name the task Text-to-Traffic Generation (TTG). The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations. To this end, we propose ChatTraffic, the first diffusion model for text-to-traffic generation. To guarantee the consistency between synthetic and real data, we augment a diffusion model with the Graph Convolutional Network (GCN) to extract spatial correlations of traffic data. In addition, we construct a large dataset containing text-traffic pairs for the TTG task. We benchmarked our model qualitatively and quantitatively on the released dataset. The experimental results indicate that ChatTraffic can generate realistic traffic situations from the text. Our code and dataset are available at https://github.com/ChyaZhang/ChatTraffic

Generative Dense Retrieval: Memory Can Be a Burden

Generative Retrieval (GR), autoregressively decoding relevant document identifiers given a query, has been shown to perform well under the setting of small-scale corpora. By memorizing the document corpus with model parameters, GR implicitly achieves deep interaction between query and document. However, such a memorizing mechanism faces three drawbacks: (1) Poor memory accuracy for fine-grained features of documents; (2) Memory confusion gets worse as the corpus size increases; (3) Huge memory update costs for new documents. To alleviate these problems, we propose the Generative Dense Retrieval (GDR) paradigm. Specifically, GDR first uses the limited memory volume to achieve inter-cluster matching from query to relevant document clusters. Memorizing-free matching mechanism from Dense Retrieval (DR) is then introduced to conduct fine-grained intra-cluster matching from clusters to relevant documents. The coarse-to-fine process maximizes the advantages of GR's deep interaction and DR's scalability. Besides, we design a cluster identifier constructing strategy to facilitate corpus memory and a cluster-adaptive negative sampling strategy to enhance the intra-cluster mapping ability. Empirical results show that GDR obtains an average of 3.0 R@100 improvement on NQ dataset under multiple settings and has better scalability.Comment: EACL 2024 mai

Research on the Floor Rockburst of Panel Entry under the Mining Influence: A Case Study

AbstractThe stability of the entries of longwall panels is the key to ensure efficient and safe production of coal mines. In order to solve the common problems of floor heave of panel entry in western China, based on a case study, this paper studies the rockburst instability mechanism of entry floor-induced mining by considering the results from a laboratory test, numerical simulations, and field practice. After testing, the coal and rock of the entry are hard and brittle. In particular under the action of impact dynamic load, its dynamic strength is higher and has a positive correlation with the impact pressure, which provides a mechanical premise for subsequent rockburst. Numerical simulation results show that with the mining of the panel, the vertical stress and the maximum principal stress of the floor are mainly concentrated in the coal pillar along the entry, and the area and degree of concentration continue to increase. The horizontal stress is mainly concentrated in the entry floor, which is distributed in the advanced range of the panel. The deformation rate of the entry roof and the ribs is stable, while the floor shows a “mutation” characteristic of not deforming when the panel is far away and suddenly rising when it is closer to the panel. The range of the plastic zone of the roof and floor remains unchanged, the ribs are further deepened, and the mechanical properties of the coal and rock mass are further weakened. The results of this study contribute to providing a reference for the control of surrounding rock of panel entry under similar geological and geotechnical circumstances

Serum Uric Acid and Long-term Prognosis in Patients with Acute Myocardial Infarction

BackgroundIt is still controversial whether or not serum uric acid, a key risk for coronary heart disease, is significantly associated with prognosis of acute myocardial infarction (AMI) . And there are rare large-scale and multicenter studies on serum uric acid and long prognosis of AMI in China.ObjectiveTo investigate the relationship between serum uric acid and long-term prognosis in AMI patients.MethodsOne thousand and ninety-eight AMI patients from 9 hospitals (Chengdu First People's Hospital, Chengdu Second People's Hospital, the Third People's Hospital of Chengdu, the First Affiliated Hospital of Chengdu Medical College, Dujiangyan Medical Center, Pidu District People's Hospital, Chengdu, Shuangliu District First People's Hospital, Jintang First People's Hospital, the People's Hospital of Pengzhou) in Chengdu during September 2016 to July 2019 were consecutively reSScruited. Baseline data were collected via the electronic medical record system of each hospital by trained professionals, including: (1) demographic data: age, gender, prevalence of smoking; (2) clinical complications and related information: hypertension, diabetes, blood pressure, heart rate, Killip class, AMI type (NSTEMI or STEMI) , prevalence of percutaneous coronary intervention (PCI) ; (3) laboratory parameters: serum SScreatinine (Scr) , uric acid (UA) , triglyceride (TG) , total cholesterol (TC) , low-density lipoprotein cholesterol (LDL-C) , high-density lipoprotein cholesterol (HDL-C) , estimated glomerular filtration rate (eGFR) ; (4) post-discharge medication: aspirin, clopidogrel/tigrelol, statins, Beta-blockers, ACEI/ARB, diuretics. Baseline data were compared between patients with and without major adverse cardiovascular and cerebrovascular events (MACCE) during post-discharge follow-up. Then, prognosis was compared aSScross UA tertile subgroups〔A: UA<420 μmol/L; B: 420 ≤UA<480 μmol/L; C: UA≥480 μmol/L〕 stratified by the diagnostic SScriteria for hyperuricemia in Guideline for the Diagnosis and Management of Hyperuricemia and Gout in China (2019) .ResultsThe median follow-up time for all participants was 14.5 (9.2, 20.7) months. Of all cases, 173 were found with MACCE, and 366 with hyperuricemia. Compared with those without MACCE, patients with MACCE had greater average age, Scr and UA, and heart rate, and higher female ratio, higher prevalence of hypertension, diabetes, use of diuretics, and Killip class≥3, but lower prevalence of PCI treatment (P<0.05) . Subgroup A had much lower incidence of MACCE, all-cause death and cardiac death than subgroup B or C (P<0.01) . Kaplan-Meier survival analysis indicated that the cumulative incidence of MACCE, all-cause death and cardiac death either in subgroup B or C was higher than that in subgroup A (P<0.01) . Cox regression analysis showed that Killip class ≥3〔HR=1.812, 95%CI (1.215, 2.700) 〕, older age〔HR=1.045, 95%CI (1.031, 1.059) 〕 and higher UA level〔 (≥420 μmol/L but<480 μmol/L: HR=1.614, 95%CI (1.062, 2.455) ; ≥480 μmol/L: HR=1.949, 95%CI (1.327, 2.862) 〕 were independent risk factors for long-term MACCE events in patients with AMI (P<0.05) . Serum UA had an AUC (95%CI) of 0.578 (0.548, 0.607) with 0.387 sensitivity, and 0.779 specificity in predicting long-term incidence of MACCE, an AUC (95%CI) of 0.645 (0.616, 0.674) with 0.598 sensitivity, and 0.670 specificity in predicting long-term incidence of all-cause death, and an AUC (95% CI) of 0.653 (0.624, 0.681) with 0.534 sensitivity, and 0.761 specificity in predicting long-term incidence of cardiac death.ConclusionElevated serum UA was associated with higher risk of long-term adverse events in AMI patients. Serum UA may be used as a predictor for long-term MACCE events in such patients

Metabolic engineering of Escherichia coli for the biosynthesis of alpha-pinene

Background: alpha-Pinene is an important natural product that is widely used in flavorings, fragrances, medicines, fine chemicals and high-density renewable fuels. Currently, alpha-Pinene used in industry is mainly produced either by tapping trees (gum turpentine) or as a byproduct of paper pulping (crude sulfate turpentine, CST). However, the extraction of it from trees is tedious and inefficient and requires substantial expenditure of natural resources. Therefore, it is necessary to seek sustainable technologies for alpha-pinene production