RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

Multi-UAV Collaborative Mission Planning Method for Self-Organized Sensor Data Acquisition

In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered animals.Deploying sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method.The current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection.With regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme optimization.The improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation scheme.The combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline model.In order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and the results show that the present method has a huge advantage in terms of space and time complexity,and at the same time,the accuracy for data extraction is relatively improved by 10.46%and 12.71%.Finally,by eliminating the clustering process and the subtask assignment process,the AoI performance decreases by 3.46×and 4.45×,and the energy performance decreases by 3.52×and 4.47×.This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations.

Jurassic integrative stratigraphy,biotas,and paleogeographical evolution of the Qinghai-Tibetan Plateau and its surrounding areas

The Qinghai-Tibetan Plateau experienced a unique geological evolution during the Jurassic,driven by the termination of the Palaeotethys and the reduction of the Neotethys.The Indian Plate separated from the northern margin of Gondwana and drifted northward from the Southern Hemisphere.Given that the timing of strata serves as the basis for reconstructing geological history,the present work aimed to develop a new multiple stratigraphic and chronologic framework for the Jurassic strata of the Qinghai-Tibetan Plateau region via a synthesis of the material on lithostratigraphy,palaeontology,iso-radiometric dating,magnetostratigraphy,and other techniques with an emphasis on recent progress and findings.The new framework included the Jurassic System from the four major subdivisions of the plateau:the Baryan Har,Qiangtang,Lhasa-Gandise,and Southern Xizang(Himalaya).Ultimately,a more complete,refined biostratigraphic sequence was proposed,comprising the most common fossils in the plateau and those that are stratigraphically significant for the Jurassic stratigraphy,including ammonites,bivalves,brachiopods,foraminifera,radiolarians,and dinoflagellate cysts for the marine strata,and pollen and spores,and charophytes for the terrestrial sediments.This biostratigraphic framework was correlated with the Jurassic international standard zonation of the Geological Time Scale 2020 via standard or representative species or genera of ammonites.Based on this framework,we constructed a lateral correlation of the Jurassic strata between different basins of the plateau.The palaeontologic correlation in the present work shows that the Lhasa-Gandise Block had a closer relationship with the Qiangtang Block than with the Southern Xizang Himalaya during the Jurassic Period.Meanwhile,the Lhasa-Gandise Block and Qiangtang Block shared similar marine fauna features of the north marginal East Tethys.This contrasts the opinion suggesting that the Yarlung Zangbo Tethys was a small back-arc basin.A combination of stratigraphical,palaeontological,and sedimentological analyses implies that the Bangong Co-Nujiang Tethys may have begun rifting in the Late Triassic,evolving to the birth at the late Early Jurassic with the formation of ocean crust.However,this resulted in failure after it grew into the climax at the end of the Middle Jurassic when the Qiangtang Block began subducting under the Lhasa-Gandise Block.In the Early Cretaceous,the two blocks finally merged.

Designing ABC-6 family small pore zeolites by epitaxial growth approach

Aluminosilicate small pore zeolites belonging to ABC-6 family play crucially important roles in the high methanol conversion with the high selectivity of light olefins,gas separation and storage,and selective catalytic reduction of NO_(x).In this work,we report a general method,called the epitaxial growth approach,for designing ABC-6 family small pore zeolites.It is mainly realized through the epitaxial growth on the nonporous SOD-type zeolite in the presence of inorganic cations(Na^(+)and K^(+))combined with a variety of organic structure directing agents(OSDAs).In this case,a series of ABC-6 family small pore zeolites such as ERI-,SWY-,LEV-,AFX-,and PTT-type zeolites have been successfully synthesized within a few hours.More importantly,the advanced focused ion beam(FIB)and the low-dose high-resolution transmission electron microscopy(HRTEM)imaging technique have been utilized for unraveling the zeolite heterojunction at the atomic level during the epitaxial growth process.It turns out(222)crystallographic planes of the SOD-type zeolite substrate provide unique pre-building units,which facilitate the growth of targeted ABC-6 family small pore zeolites along its c-axis.Moreover,the morphologies of ERI-type zeolite can also be tuned through the epitaxial growth approach,achieving a longer lifetime in the methanol conversion.

木霉菌合成银纳米粒子条件的优化及其对甜瓜尖孢镰刀菌抑制作用

随着绿色环保观念的普及,生物合成金属纳米粒子的方法备受青睐。纳米银(Silver nanoparticles,AgNPs)由于其抗菌活性强且不易产生抗药性等特点在农业病害防治中越来越受到关注。文中利用橘绿木霉Trichoderma citrinoviride和毛簇木霉Trichoderma velutinous研究了AgNPs的最适合成条件和AgNPs对尖孢镰刀菌抑菌活性。结果表明,所有合成的AgNPs均在400–500 nm处有吸收峰,两种木霉生物合成AgNPs的最适合成条件为CL法(菌丝滤液)静置光照培养,底物AgNO3浓度为2.0mmol/L,pH值为7,反应温度为45℃。橘绿木霉和毛簇木霉合成的AgNPs均对尖孢镰刀菌有抑制作用,抑菌效果随浓度的增加而增大,AgNPs在浓度为200 mg/L时,抑菌率分别达到33.745%和36.083%。