Spatiotemporal evolution and driving factors of the coupling coordination between county land urbanization and grain production: the case of Jiangsu province, China

How to ensure a coordinated development between land urbanization and grain production has been a key issue that needs to be urgently addressed to achieve sustainable development in China. Taking Jiangsu province as an example, this paper measures the coupling coordination degree (CCD) between county land urbanization and grain production from 2010 to 2020 based on the coupling coordination degree model (CCDM). In addition, the exploratory spatial data analysis method and the space Durbin model are combined to explore the spatial correlation and influencing factors of the CCD between land urbanization and grain production. The main conclusions are as follows: (1) From a temporal perspective, the CCD between county land urbanization and grain production in Jiangsu is dominated by basically coordinated, with an overall stable rising trend and a distribution pattern of Northern Jiangsu > Central Jiangsu > Southern Jiangsu. (2) From the perspective of spatial distribution, the CCD between the two is dominated by basically coordinated in the Southern, Central and Northern Jiangsu regions. The spatial clustering characteristics are significant, and the distribution of counties with basically coordinated shows concentrated and contiguous characteristics. (3) From the perspective of spatial correlation, the CCD between the two shows a low level of positive spatial autocorrelation. The state of agglomeration is significant in Northern Jiangsu, while spatial agglomeration is sporadic in Southern Jiangsu and insignificant in Central Jiangsu. (4) The factors affecting the CCD between county land urbanization and grain production in Jiangsu province are determined by many factors together. Based on a driver perspective, Per capita GDP and chemical fertilizer application intensity have a negative effect on it. Highway network density and mobile internet penetration rate have a positive effect on it. Population density, advanced industrial structure, per capita grain planting area and agro-industrial agglomeration are not significant. This study offers useful insights for promoting the coupled and coordinated development of county urbanization and grain production in China

TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise

Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks. However, what about small models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant fundamentals, chain of thought, and common mistakes for most NLP samples, which makes annotation more than just an answer, thus allowing other models to learn "why" instead of just "what". The TeacherLM-7.1B model achieved a zero-shot score of 52.3 on MMLU, surpassing most models with over 100B parameters. Even more remarkable is its data augmentation ability. Based on TeacherLM-7.1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting. The experimental results indicate that the data augmentation provided by TeacherLM has brought significant benefits. We will release the TeacherLM series of models and augmented datasets as open-source.Comment: 5 figures, 15 page

Spatial and temporal evolution and driving factors of county solid waste harmless disposal capacity in China

Currently, China mainly adopts the waste treatment model of “household sorting, village collection, town transfer and county disposal.” Determining the spatial and temporal distribution of China’s county solid waste harmless disposal capacity and formulating strategies according to local conditions are of great significance in promoting the construction of beautiful villages in China and realizing the Beautiful China strategy. This paper explores the spatial and temporal evolution characteristics of county solid waste harmless disposal capacity by selecting relevant data from 27 provinces in China from 2006 to 2020, and adopts the Dagum Gini coefficient method to measure the spatial gap of it. In addition, this paper empirically analyses the drivers affecting county solid waste harmless disposal capacity using the spatial Durbin model (SDM). The main conclusions are as follows: 1) In terms of time, county solid waste harmless disposal capacity in China as a whole shows a year-by-year increasing trend, especially after 2018 when the growth rate is faster. 2) In terms of spatial patterns, the solid waste harmless disposal capacity of coastal areas is generally higher than that of inland areas, and the distribution of provinces with low and middle levels of solid waste harmless disposal capacity is characterized by concentrated contiguity. From the perspective of spatial agglomeration, the characteristics of spatial agglomeration in the north are gradually becoming more pronounced, while those in the south are not significant. From the trajectory of the evolution of the spatial center of gravity, the center of gravity of county solid waste harmless disposal capacity as a whole shows a northeast, then northwest, then northeast movement, and the speed of “northward expansion” is greater than the speed of “eastward expansion”. 3) The results of the Dagum Gini coefficient and its decomposition show that the northeast has the smallest average annual rate of change in the Gini coefficient. The reduction of the within-group gap is an important driver towards equilibrium. The contribution of hypervariable density is decreasing year by year. 4) The number of harmless disposal plants, GDP per person, population urbanization, the number of township waste transfer stations and county waste disposal fixed asset investment are important drivers of county waste harmless disposal capacity. Findings provide helpful insights into optimizing rural habitat and promoting the comprehensive transformation of China’s county development

VideoLLM: Modeling Video Sequence with Large Language Models

With the exponential growth of video data, there is an urgent need for automated technology to analyze and comprehend video content. However, existing video understanding models are often task-specific and lack a comprehensive capability of handling diverse tasks. The success of large language models (LLMs) like GPT has demonstrated their impressive abilities in sequence causal reasoning. Building upon this insight, we propose a novel framework called VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs from natural language processing (NLP) for video sequence understanding. VideoLLM incorporates a carefully designed Modality Encoder and Semantic Translator, which convert inputs from various modalities into a unified token sequence. This token sequence is then fed into a decoder-only LLM. Subsequently, with the aid of a simple task head, our VideoLLM yields an effective unified framework for different kinds of video understanding tasks. To evaluate the efficacy of VideoLLM, we conduct extensive experiments using multiple LLMs and fine-tuning methods. We evaluate our VideoLLM on eight tasks sourced from four different datasets. The experimental results demonstrate that the understanding and reasoning capabilities of LLMs can be effectively transferred to video understanding tasks. We release the code at https://github.com/cg1177/VideoLLM.Comment: Technical Repor

Oral vinorelbine and continuous low doses of cyclophosphamide in pediatric rhabdomyosarcoma: a real-world study

Introduction: Metronomic maintenance therapy (MMT) has significantly improved the survival of patients with high-risk rhabdomyosarcoma in clinical trials. However, there remains a lack of relevant data on its effectiveness in real-world situations.Methods: We retrospectively retrieved data of 459 patients < 18 years of age diagnosed with rhabdomyosarcoma at Sun Yat-sen University Cancer Center from January 2011 to July 2020 from our database. The MMT regimen was oral vinorelbine 25–40 mg/m2 for twelve 4-week cycles on days 1, 8, and 15, and oral cyclophosphamide 25–50 mg/m2 daily for 48 consecutive weeks.Results: A total of 57 patients who underwent MMT were included in the analysis. The median follow-up time was 27.8 (range: 2.9–117.5) months. From MMT to the end of follow-up, the 3-year PFS and OS rates were 40.6% ± 6.8% and 58.3% ± 7.2%, respectively. The 3-year PFS was 43.6% ± 11.3% in patients who were initially diagnosed as low- and intermediate-risk but relapsed after comprehensive treatment (20/57), compared with 27.8% ± 10.4% in high-risk patients (20/57) and 52.8% ± 13.3% in intermediate-risk patients who did not relapse (17/57). The corresponding 3-year OS for these three groups was 65.8% ± 11.4%, 50.1% ± 12.9%, and 55.6% ± 13.6%, respectively.Conclusion: We present a novel study of MMT with oral vinorelbine and continuous low doses of cyclophosphamide in real-world pediatric patients with RMS. Our findings showed that the MMT strategy significantly improved patient outcomes and may be an effective treatment for high-risk and relapsed patients

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure