Emerging New Phylogenetic Groups of Rabies Virus in Chinese Ferret Badgers

Chinese ferret badger（FB）-transmitted rabies is a serious threat to public health in southeast China. Although mostly associated with dogs, the rabies virus（RABV） presents genetic diversity and has a significantly wide host range in China. Instead of the dog-and wildlife-associated China ⅠI lineage in the past decades, the China Ⅰ lineage has become the main epidemic group hosted and transmitted by dogs. In this study, four new lineages, including 43 RABVs from FBs, have been classified within the dog-dominated China Ⅰ lineage since 2014. FBRABVs have been previously categorized in the China Ⅱ lineage. Moreover, FB-hosted viruses seem to have become the main independent FB-associated clade in the phylogenetic tree. This claim suggests that the increasing genetic diversity of RABVs in FBs is a result of the selective pressure from coexisting dog rabies. FB transmission has become complicated and serious with the coexistence of dog rabies. Therefore, apart from targeting FB rabies, priority should be provided by the appropriate state agencies to perform mass immunization of dog against rabies.

Epidemic and Maintenance of Rabies in Chinese Ferret Badgers (Melogale moschata) indicated by Epidemiology and the Molecular Signatures of Rabies Viruses

An epidemic of Chinese ferret badger-associated human rabies was investigated in Wuyuan county, Jiangxi province and rabies viruses isolates from ferret badgers in different districts in Jiangxi and Zhejiang provinces were sequenced with their nucleotides and amino acids and aligned for epidemiological analysis. The results showed that the human rabies in Wuyuan are only associated with ferret badger bites; the rabies virus can be isolated in a high percentage of ferret badgers in the epidemic areas in Jiangxi and Zhejiang provinces; the isolates share the same molecular features in nucleotides and have characteristic amino acid signatures, i.e., 2 sites in the nucleoprotein and 3 sites in the glycoprotein, that are distinct from virus isolates from dogs in the same region. We conclude that rabies in Chinese ferret badgers has formed an independent transmission cycle and ferret badgers may serve as another important rabies reservoir independent of dog rabies in China.

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.

FULHAM BADGERS—ENJOYING THE GAME

富勒姆足球俱乐部（Fulham Football Club）成立于1879年，到现在已有130多年的历史了。主场位于可以容纳30500名观众的克拉文农场球场（Craven Cottage）。在上世纪60年代，他们大多数都在旧甲组联赛（超级联赛的前身）参赛，但并没有赢得任何主要的锦标赛冠军。1975年，他们打进足总杯决赛，但这也是他们历史上唯一的一次。他们于2009-2010赛季获得了欧洲联赛的亚军。

An Improved Honey Badger Algorithm through Fusing Multi-Strategies

TheHoney Badger Algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers.The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA,which has been applied in photovoltaic systems and optimization problems effectively.However,HBA tends to suffer from the local optimum and low convergence.To alleviate these challenges,an improved HBA(IHBA)through fusing multi-strategies is presented in the paper.It introduces Tent chaotic mapping and composite mutation factors to HBA,meanwhile,the random control parameter is improved,moreover,a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation.IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems.The Wilcoxon Rank-sum Test,Friedman Test and Mann-WhitneyU Test are conducted after emulation.The results indicate the competitiveness and merits of the IHBA,which has better solution quality and convergence traits.The source code is currently available from:https://github.com/zhaotao789/IHBA.

Optimal zero-crossing group selection method of the absolute gravimeter based on improved auto-regressive moving average model

An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.

An Efficient Honey Badger Optimization Based Solar MPPT Under Partial Shading Conditions

Due to the enormous utilization of solar energy,the photovoltaic(PV)system is used.The PV system is functioned based on a maximum power point(MPP).Due to the climatic change,the Partial shading conditions have occurred under non-uniform irradiance conditions.In the PV system,the global maximum power point(GMPP)is complex to track in the P-V curve due to the Partial shad-ing.Therefore,several tracking processes are performed using various methods like perturb and observe(P&O),hill climbing(HC),incremental conductance(INC),Fuzzy Logic,Whale Optimization Algorithm(WOA),Grey Wolf Optimi-zation(GWO)and Flying Squirrel Search Optimization(FSSO)etc.Though,the MPPT is not so efficient when the partial shading is increased.To increase the efficiency and convergences in MMPT,the Honey Badger optimization(HBO)algorithm is presented.This HBO model is motivated by the excellent foraging behaviour of honey badgers.This HBO model is used to achieve the best solution in GMPP tracking and speed convergence.The HBO methodology is also com-pared with prior P&O,WOA and FSSO methods using MATLAB.Therefore,the experiment shows that the HBO method is performed a higher tracking than all prior methods.

Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

Every day,websites and personal archives create more and more photos.The size of these archives is immeasurable.The comfort of use of these huge digital image gatherings donates to their admiration.However,not all of these folders deliver relevant indexing information.From the outcomes,it is dif-ficult to discover data that the user can be absorbed in.Therefore,in order to determine the significance of the data,it is important to identify the contents in an informative manner.Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.Hence,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image annotation.Initially,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class map.The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation(JSEG).Thefinal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classifier,the unlabeled images are labelled.The proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_Measure.With the assistance of the pro-posed methodology,the unlabeled images are labelled.

EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

Globally,depression is perceived as the most recurrent and risky disor-der among young people and adults under the age of 60.Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media.With the help of Natural Language Proces-sing(NLP)and Machine Learning(ML)techniques,the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts.The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory network(LSTM)model.The feature extraction process combines a novel feature selection method called Elite Term Score(ETS)and Word2Vec to extract the syntactic and semantic information respectively.First,the ETS method leverages the document level,class level,and corpus level prob-abilities for computing the weightage/score of the terms.Then,the ideal and per-tinent set of features with a high ETS score is selected,and the Word2vec model is trained to generate the intense feature vector representation for the set of selected terms.Finally,the resultant word vector obtained is called EliteVec,which is fed to the hybrid LSTM model based on Honey Badger optimizer with population reduction technique(PHB)which predicts whether the input textual content is depressive or not.The PHB algorithm is integrated to explore and exploit the opti-mal hyperparameters for strengthening the performance of the LSTM network.The comprehensive experiments are carried out with two different Twitter depres-sion corpus based on accuracy and Root Mean Square Error(RMSE)metrics.The results demonstrated that the proposed EliteVec+LSTM+PHB model outperforms the state-of-art models with 98.1%accuracy and 0.0559 RMSE.

Hybrid Chameleon and Honey Badger Optimization Algorithm for QoS-Based Cloud Service Composition Problem

Cloud computing facilitates the great potentiality of storing and managing remote access to services in terms of software as a service(SaaS).Several organizations have moved towards outsourcing over the cloud to reduce the burden on local resources.In this context,the metaheuristic optimization method is determined to be highly suitable for selecting appropriate services that comply with the requirements of the client’s requests,as the services stored over the cloud are too complex and scalable.To achieve better service composition,the parameters of Quality of Service(QoS)related to each service considered to be the best resource need to be selected and optimized for attaining potential services over the cloud.Thus,the cloud service composition needs to concentrate on the selection and integration of services over the cloud to satisfy the client’s requests.In this paper,a Hybrid Chameleon and Honey Badger Optimization Algorithm(HCHBOA)-based cloud service composition scheme is presented for achieving efficient services with satisfying the requirements ofQoS over the cloud.This proposed HCHBOA integrated the merits of the Chameleon Search Algorithm(CSA)and Honey Badger Optimization Algorithm(HBOA)for balancing the tradeoff between the rate of exploration and exploitation.It specifically used HBOA for tuning the parameters of CSA automatically so that CSA could adapt its performance depending on its incorporated tuning factors.The experimental results of the proposed HCHBOA with experimental datasets exhibited its predominance by improving the response time by 21.38%,availability by 20.93%and reliability by 19.31%with a minimized execution time of 23.18%,compared to the baseline cloud service composition schemes used for investigation.

Badger和Lummus苯乙烯工艺能耗对比分析

从代表当今苯乙烯主流技术的两家专利商贝杰尔和鲁姆斯技术的能耗对比分析入手,对这两种生产苯乙烯产品的工艺技术特点进行论述,从能耗角度分析为潜在的苯乙烯生产商选择工艺路线提供技术参考。装置物耗由于采用不同厂家的催化剂会出现较大差别,在此不进行论述。

异丙苯生产技术进展

对国外最新开发的用于合成异丙苯的Dow/Kollogg、Mobil/Badger、CD Tech、UOP Q-max、EniChem工艺作了比较和评述、提出加快我国异异丙苯科研开发的必要性。

苯乙烯装置分离单元流程模拟与优化

研究围绕某公司苯乙烯装置生产线展开,苯乙烯装置分离单元流程模拟采用Aspen Plus流程模拟软件实现,以此为流程的优化提供依据。通过优化苯乙烯分离单元工艺参数,各塔再沸器和冷凝器负荷均出现一定下降,苯乙烯产品质量分数也实现一定程度提升,研究的现实意义可见一斑。

韩国LG化学公司在大山建采用Badger技术生产异丙苯和双酚A新装置

石油化学新报（日），2011（4569）：5 韩国LG化学公司计划采用美国NBadgerLicensing公司的生产技术（Badger技术）在大山工厂建200kt／a的异丙苯装置和120kt／a的双酚A（BPA）装置。预计这两套装置将在2012年内建成并投产。LG化学公司目前在丽水工厂运转的一套异丙苯装置和两套BPA装置也都采用的是Badger技术。

中国石化三井40万t／a苯酚丙酮装置投产

2014年11月29日，上海中石化三井化工有限公司40万t／a苯酚丙酮装置顺利打通全部流程，产出第一批苯酚、丙酮产品，圆满实现开车一次成功，标志着目前国内单套规模第二大、工艺最先进的苯酚丙酮装置全面建成投产。2011年11月18日，40万t／a苯酚丙酮合资项目在上海化学工业区正式动工奠基。该项目主要建设25万t／a年苯酚、15万t／a丙酮的生产装置，总投资约20亿元人民币。装置异丙苯生产采用美国Badger分子筛烃化技术，苯酚丙酮生产采用日本三井工艺技术。

欧盟研发隧道挖掘机器人同时具备3D打印功能

近日，欧盟正在开发一个名为BADGER的机器人。这种机器人被设计来用于施工现场，带有导航设备，能在地下挖沟渠和隧道。此外，

沙特将建造苯酚-BPA联合装置

沙特阿拉伯Al Jubail的项目管理开发公司宣布已经选择了KBR和Badger公司的技术建设一套苯酚-双酚A的联合装置。KBR将提供苯酚屑酮的生产技术，Badger则提供异丙苯和双酚A的技术。装置的公称年生产能力将为272kt／a异丙苯，201kt／a苯酚，124kt／a丙酮和240kt／a的BPA，作为原先已经宣布的总值为35亿美元的Al Jubail石化项目的一个组成部分，计划在2008年投入运行。

獾和出版社Badger＆Press——“凿壁偷光”

獾和出版社成立于2010年，创办者是国内著名的平面设计师广煜。广煜毕业于中央美院．多年以来一直从事着众多关于书籍和海报类别的设计工作。获奖无数的他在工作之中繁复的思索着一个问题“我们都应该看些什么书”。而这个思索的结果最终变成了一个略微偏重于理想化的现实——獾和出版社（Badger＆Press）。