Evaluation on the Efficiency of Crop Insurance in China's Major Grain-Producing Area

AbstractIn China, crop insurance is just a pilot program characterized by material cost-based coverage level and government-subsidized premium. To identify the efficiency of the crop insurance, we use the nonparametric density function model and estimate the probability of yield loss rate at 3 proposed levels for grain crop, wheat, corn, rice and cotton respectively from 13 provinces in the Major Grain-Producing Area. Besides, we point out some unfavorable factors for crop insurance management based on the Second National Agricultural Census data (2006). Our finding is: the coverage level is on average no larger than 50% of the per hectare crop production value while the probability of yield loss for each crop approaches to zero if the proposed yield loss rate is larger than 40%, so the yield damage compensations are not necessary unless the huge catastrophes occur with the yield loss rate over 50%. Farmers could buy crop insurance to avoid big crop failure other than to maximize their returns. Therefore, the current crop insurance coverage level under normal years could not create an effective inducement for farmers to purchase insurance contracts. To expand crop insurance participation, we consider that it is necessary to carry out positive and conditional forced insurance, provide a larger portion of premium subsidy to the Major Grain-Producing Area by central government and improve the basic agricultural production conditions

Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph

The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeological knowledge. To achieve this, we first collect a large-scale image dataset of bronze dings, which contains richer attribute information than other existing fine-grained datasets. Second, we introduce a multihead classifier and a knowledge-guided relation graph to mine the relationship between attributes and the ding era. Third, we conduct comparison experiments with various existing methods, the results of which show that our dating method achieves a state-of-the-art performance. We hope that our data and applied networks will enrich fine-grained classification research relevant to other interdisciplinary areas of expertise. The dataset and source code used are included in our supplementary materials, and will be open after submission owing to the anonymity policy. Source codes and data are available at: https://github.com/zhourixin/bronze-Ding.Comment: CVPR2023 accepte

Discovery of new cellulases from the metagenome by a metagenomics-guided strategy.

Background Energy shortage has become a global problem. Production of biofuels from renewable biomass resources is an inevitable trend of sustainable development. Cellulose is the most abundant and renewable resource in nature. Lack of new cellulases with unique properties has become the bottleneck of the efficient utilization of cellulose. Environmental metagenomes are regarded as huge reservoirs for a variety of cellulases. However, new cellulases cannot be obtained easily by functional screening of metagenomic libraries. Results In this work, a metagenomics-guided strategy for obtaining new cellulases from the metagenome was proposed. Metagenomic sequences of DNA extracted from the anaerobic beer lees converting consortium enriched at thermophilic conditions were assembled, and 23 glycoside hydrolase (GH) sequences affiliated with the GH family 5 were identified. Among the 23 GH sequences, three target sequences (designated as cel7482, cel3623 and cel36) showing low identity with those known GHs were chosen as the putative cellulase genes to be functionally expressed in Escherichia coli after PCR cloning. The three cellulases were classified into endo-β-1,4-glucanases by product pattern analysis. The recombinant cellulases were more active at pH 5.5 and within a temperature range of 60–70 °C. Computer-assisted 3D structure modeling indicated that the active residues in the active site of the recombinant cellulases were more similar to each other compared with non-active site residues. The recombinant cel7482 was extremely tolerant to 2 M NaCl, suggesting that cel7482 may be a halotolerant cellulase. Moreover, the recombinant cel7482 was shown to have an ability to resist three ionic liquids (ILs), which are widely used for cellulose pretreatment. Furthermore, active cel7482 was secreted by the twin-arginine translocation (Tat) pathway of Bacillus subtilis 168 into the culture medium, which facilitates the subsequent purification and reduces the formation of inclusion body in the context of overexpression. Conclusions This study demonstrated a simple and efficient method for direct cloning of new cellulase genes from environmental metagenomes. In the future, the metagenomics-guided strategy may be applied to the high-throughput screening of new cellulases from environmental metagenomes.published_or_final_versio

AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation

We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks. These tasks often involve complex multi-step reasoning, presenting significant challenges due to the limited paired data connecting human instructions (e.g., making a smiley face) and robot actions (e.g., end-effector movement). Existing approaches relieve this challenge by adopting an open-loop paradigm decomposing high-level instructions into simple sub-task plans, and executing them step-by-step using low-level control models. However, these approaches are short of instant observations in multi-step reasoning, leading to sub-optimal results. To address this issue, we propose to automatically collect a cognitive robot dataset by Large Language Models (LLMs). The resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of multi-step text plans and paired observation sequences. To enable efficient data acquisition, we employ elaborated multi-round prompt designs that effectively reduce the burden of extensive human involvement. We further propose a closed-loop multi-modal embodied planning model that autoregressively generates plans by taking image observations as input. To facilitate effective learning, we leverage MiniGPT-4 with a frozen visual encoder and LLM, and finetune additional vision adapter and Q-former to enable fine-grained spatial perception for manipulation tasks. We conduct experiments to verify the superiority over existing open and closed-loop methods, and achieve a significant increase in success rate by 21.4% and 14.5% over ChatGPT and GPT-4 based robot tasks. Real-world demos are shown in https://www.youtube.com/watch?v=ayAzID1_qQk

Pave the Way to Grasp Anything: Transferring Foundation Models for Universal Pick-Place Robots

Improving the generalization capabilities of general-purpose robotic agents has long been a significant challenge actively pursued by research communities. Existing approaches often rely on collecting large-scale real-world robotic data, such as the RT-1 dataset. However, these approaches typically suffer from low efficiency, limiting their capability in open-domain scenarios with new objects, and diverse backgrounds. In this paper, we propose a novel paradigm that effectively leverages language-grounded segmentation masks generated by state-of-the-art foundation models, to address a wide range of pick-and-place robot manipulation tasks in everyday scenarios. By integrating precise semantics and geometries conveyed from masks into our multi-view policy model, our approach can perceive accurate object poses and enable sample-efficient learning. Besides, such design facilitates effective generalization for grasping new objects with similar shapes observed during training. Our approach consists of two distinct steps. First, we introduce a series of foundation models to accurately ground natural language demands across multiple tasks. Second, we develop a Multi-modal Multi-view Policy Model that incorporates inputs such as RGB images, semantic masks, and robot proprioception states to jointly predict precise and executable robot actions. Extensive real-world experiments conducted on a Franka Emika robot arm validate the effectiveness of our proposed paradigm. Real-world demos are shown in YouTube (https://www.youtube.com/watch?v=1m9wNzfp_4E ) and Bilibili (https://www.bilibili.com/video/BV178411Z7H2/ )

Anxiety and depression in dry eye patients during the COVID-19 pandemic: Mental state investigation and influencing factor analysis

ObjectiveInvestigate the anxiety and depression states among dry eye (DE) patients during the COVID-19 outbreak and analyze their influence factors.MethodsThe study was conducted in a tertiary eye hospital in Tianjin, China from March–April 2021. Four hundred twenty-eight DE patients were tested with the Ocular Surface Disease Index, Short Healthy Anxiety Inventory, Hospital Anxiety and Depression Scale, and Pittsburgh Sleep Quality Index. Descriptive statistics was used to assess the difference between DE with depression or anxiety among different groups. And multiple linear regression was used to explore factors that influence anxiety and depression in DE patients.ResultsThe incidence rates of anxiety and depression among DE patients during COVID-19 were 27.34 and 26.87%, respectively. The proportion with comorbid anxiety and depression was 24.30%. Patients' education level (t = −3.001, P < 0.05; t = −3.631, P < 0.05), course of disease (t = 2.341, P < 0.05; t = 2.444, P < 0.05), health anxiety (t = 3.015, P < 0.05; t = 2.731, P < 0.05), and subjective sleep quality (t = 3.610, P < 0.05; t = 4.203, P < 0.05) had certain influences on anxiety and depression.ConclusionThe results showed that subjective symptoms of DE patients were related to depression and anxiety. Higher education, shorter disease duration, lower health anxiety levels, and better subjective sleep quality were associated with the reduced depressive and anxiety symptoms in DE patients. These findings could be deemed beneficial to the treatment and prevention of DE during the COVID-19 epidemic

Non-linear associations of body mass index with impaired fasting glucose, β-cell dysfunction, and insulin resistance in nondiabetic Chinese individuals: a cross-sectional study

Introduction: Identifying and managing patients with prediabetes is important. The study aims to investigate the association of body mass index (BMI) with impaired fasting glucose (IFG), β-cell dysfunction, and insulin resistance in nondiabetic Chinese individuals. Methods: This was a cross-sectional study of consecutive nondiabetic individuals enrolled between January 2014 and January 2015, divided into the NFG (normal fasting glucose, fasting blood glucose [FBG] <5.6 mmol/L) and IFG (n=450; FBG ≥5.6 mmol/L) groups. Restricted cubic splines and piecewise-regression were used to model the association of IFG, impaired β-cell function, and insulin resistance with BMI. Stratified analyses were performed across sex and age. Results: A total of 900 NFG and 450 IFG individuals were enrolled, with a median age of 41 (30-49) years and 1076 males (79.7%). After adjusting for age and sex, the restricted cubic splines showed that the risk of IFG was increasing rapidly until around 27.96 kg/m2 of BMI and then started to flat afterward (P for non-linearity=0.010), which was similar in males and ≤45 years individuals (P for non-linearity<0.001 and =0.007, respectively). The risk of insulin resistance increased and β-cell dysfunction decreased as the BMI increased in all participants (both P for non-linearity>0.05), consistent with the results in males, females, and ≤45 and >45 years individuals. Conclusions: The risk of IFG is not rising linearly as the BMI increases, and higher BMI seems to decelerate the rise of the risk

Optical characterization of isothermal spin state switching in an Fe(II) spin crossover molecular and polymer ferroelectric bilayer

Using optical characterization, it is evident that the spin state of the spin crossover molecular complex [Fe{H2B(pz)2}2(bipy)] (pz = tris(pyrazol-1-1y)-borohydride, bipy = 2,2′-bipyridine) depends on the electric polarization of the adjacent polymer ferroelectric polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) thin film. The role of the PVDF-HFP thin film is significant but complex. The UV–Vis spectroscopy measurements reveals that room temperature switching of the electronic structure of [Fe{H2B(pz)2}2(bipy)] molecules in bilayers of PVDF-HFP/[Fe{H2B(pz)2}2(bipy)] occurs as a function of ferroelectric polarization. The retention of voltage-controlled nonvolatile changes to the electronic structure in bilayers of PVDF-HFP/[Fe{H2B(pz)2}2(bipy)] strongly depends on the thickness of the PVDF-HFP layer. The PVDF-HFP/[Fe{H2B(pz)2}2(bipy)] interface may affect PVDF-HFP ferroelectric polarization retention in the thin film limit