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.

Electronic structure,bonding characteristics,and mechanical behaviors of a new family of Si-containing damage-tolerant MAB phases M_(5)SiB_(2)(M=IVB-VIB transition metals)

MAB phases are layered ternary compounds with alternative stacking of transition metal boride layers and group A element layers.Until now,most of the investigated MAB phases are concentrated on compounds with Al as the A element layers.In this work,the family of M_(5)SiB_(2)(M=IVB-VIB transition metals)compounds with silicon as interlayers were investigated by density functional theory(DFT)methods as potential MAB phases for high-temperature applications.Starting from the known Mo5SiB2,the electronic structure,bonding characteristics,and mechanical behaviors were systematically investigated and discussed.Although the composition of M_(5)SiB_(2) does not follow the general formula of experimentally reported(MB)_(2z)A_(x)(MB_(2))_(y)(z=1,2;x=1,2;y=0,1,2),their layered structure and anisotropic bonding characteristics are similar to other known MAB phases,which justifies their classification as new members of this material class.As a result of the higher bulk modulus and lower shear modulus,Mo_(5)SiB_(2) has a Pugh’s ratio of 0.53,which is much lower than the common MAB phases.It was found that the stability and mechanical properties of M_(5)SiB_(2) compounds depend on their valence electron concentrations(VECs),and an optimum VEC exists as the criteria for stability.The hypothesized Zr and Hf containing compounds,i.e.,Zr_(5)SiB_(2) and Hf_(5)SiB_(2),which are more interesting in terms of high-temperature oxidation/ablation resistance,were found to be unfortunately unstable.To cope with this problem,a new stable solid solution(Zr_(0.6)Mo_(0.4))_(5)SiB_(2) was designed based on VEC tuning to demonstrate a promising approach for developing new MAB phases with desirable compositions.