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.

Hyperparameter Optimization for Capsule Network Based Modified Hybrid Rice Optimization Algorithm

Hyperparameters play a vital impact in the performance of most machine learning algorithms.It is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance manually.Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a com-binatorial optimization problem.However,these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted.The inspiration for the hybrid rice optimization(HRO)algorithm is from the breeding technology of three-line hybrid rice in China,which has the advantages of easy implementation,less parameters and fast convergence.In the paper,genetic search is combined with the hybrid rice optimization algorithm(GHRO)and employed to obtain the optimal hyperparameter of the capsule network automatically,that is,a probability search technique and a hybridization strategy belong with the primary HRO.Thirteen benchmark functions are used to evaluate the performance of GHRO.Furthermore,the MNIST,Chest X-Ray(pneumonia),and Chest X-Ray(COVID-19&pneumonia)datasets are also utilized to evaluate the capsule network learnt by GHRO.The experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network,which is able to boost the performance of the capsule network on image classification.

Intake of tobacco nitrosamines of smokers in various provinces of China and their cancer risk:A meta-analysis

Nitrosamines are a class of carcinogens which have been detected widely in food,water,some pharmaceuticals as well as tobacco.The objectives of this paper include reviewing the basic information on tobacco consumption and nitrosamine contents,and assessing the health risks of tobacco nitrosamines exposure to Chinese smokers.We searched the publications in English from“Web of Science”and those in Chinese from the“China National Knowledge Infrastructure”in 2022 and collected 151 literatures with valid information.The content of main nitrosamines in tobacco,including 4-(methylnitrosoamino)-1-(3-pyridyl)-1-butanone(NNK),N-nitrosonornicotine(NNN),N-nitrosoanatabine(NAT),N-nitrosoanabasine(NAB),total tobacco-specific nitrosamines(TSNA),and N-nitrosodimethylamine(NDMA)were summarized.The information of daily tobacco consumption of smokers in 30 provinces of China was also collected.Then,the intakes of NNN,NNK,NAT,NAB,TSNAs,and NDMA via tobacco smoke were estimated as 1534ng/day,591 ng/day,685 ng/day,81 ng/day,2543 ng/day,and 484 ng/day by adult smokers in30 provinces,respectively.The cancer risk(CR)values for NNN and NNK inhalation intake were further calculated as 1.44×10^(-5)and 1.95×10^(-4).The CR value for NDMA intake via tobacco smoke(inhalation:1.66×10^(-4))indicates that NDMA is similarly dangerous in tobacco smoke when compared with the TSNAs.In China,the CR values caused by average nitrosamines intake via various exposures and their order can be estimated as the following:smoke(3.75×10^(-4))>food(1.74×10^(-4))>drinking water(1.38×10^(-5)).Smokers in China averagely suffer 200%of extra cancer risk caused by nitrosamines in tobacco when compared with non-smokers.

药品中N-亚硝胺类有害杂质

自从2018年“缬沙坦事件”以来,药品中的N-亚硝胺类有害杂质日益受到关注。N-亚硝胺类是一类强致癌物,已有十余种亚硝胺类被国际癌症研究所(International Agency for Research on Cancer, IARC)列入致癌物清单。其中,N-二甲基亚硝胺(N-nitrosodimethylamine, NDMA)、N-二乙基亚硝胺(N-nitrosodiethylamine, NDEA)被列为2A类致癌物。本文对N-亚硝胺物质的毒理学特性、致癌机理、检测方法进行了介绍,分析了缬沙坦、雷尼替丁、二甲双胍等常用药品中产生亚硝胺类杂质的原因,梳理了欧盟、美国和我国在药品中亚硝胺杂质方面的监管要求和措施。论文估算了召回前上述药品中亚硝胺杂质带来的致癌风险:缬沙坦中亚硝胺杂质浓度最高(NDMA含量为未检出~20.19μg/片,NDEA含量为未检出~1.31μg/片),导致额外致癌风险最高,其中位值为4.69×10-6,75百分位值则高达5.61×10-4,提示至少有25%的药品存在过高的致癌风险;雷尼替丁和二甲双胍中亚硝胺杂质则低得多,其致癌风险接近或低于10-6安全水平。不合格药品中亚硝胺杂质带来的致癌风险远高于来自食品和饮用水中的亚硝胺类污染物,甚至高于烟草中亚硝胺导致的致癌风险,因此,需要引起高度重视。加强监管后,2020年后我国原料药和成品药中未出现亚硝胺杂质超标的报道。本文可为药品生产、健康评价与研究和监管领域相关机构人员提供参考。

Adversarial Neural Collaborative Filtering with Embedding Dimension Correlations

Recently,convolutional neural networks(CNNs)have achieved excellent performance for the recommendation system by extracting deep features and building collaborative filtering models.However,CNNs have been verified susceptible to adversarial examples.This is because adversarial samples are subtle non-random disturbances,which indicates that machine learning models produce incorrect outputs.Therefore,we propose a novel model of Adversarial Neural Collaborative Filtering with Embedding Dimension Correlations,named ANCF in short,to address the adversarial problem of CNN-based recommendation system.In particular,the proposed ANCF model adopts the matrix factorization to train the adversarial personalized ranking in the prediction layer.This is because matrix factorization supposes that the linear interaction of the latent factors,which are captured between the user and the item,can describe the observable feedback,thus the proposed ANCF model can learn more complicated representation of their latent factors to improve the performance of recommendation.In addition,the ANCF model utilizes the outer product instead of the inner product or concatenation to learn explicitly pairwise embedding dimensional correlations and obtain the interaction map from which CNNs can utilize its strengths to learn high-order correlations.As a result,the proposed ANCF model can improve the robustness performance by the adversarial personalized ranking,and obtain more information by encoding correlations between different embedding layers.Experimental results carried out on three public datasets demonstrate that the ANCF model outperforms other existing recommendation models.

Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

恒产者恒心:“原罪”嫌疑、产权保护与民营企业绩效

本文从产权保护视角考察民营企业家"原罪"嫌疑对其所经营企业绩效的影响。研究发现:与创业型企业相比,国有转制型民营企业的绩效显著较差;这一现象在产权保护较差地区更为明显。在"郎顾之争"事件中,国有转制型企业的市场反应显著差于创业型企业;而当最高人民法院宣布再审三起重大产权案件时,其市场反应则显著更好;《物权法》施行后,两类企业绩效差异显著缩小;此外,国有转制型企业更多地通过关联交易转移资源且创新投入较少。

市场分割、投资攀比与企业投资本地联动性

企业投资除了存在行业层面的“投资潮涌”,还可能存在地区层面的联动性.本文利用我国上市公司1998—2016年的数据,发现所在省份上市公司的平均投资水平对企业投资支出具有重要影响,即企业投资存在本地联动性。进一步,企业投资的本地联动性在与相邻省份市场分割程度较大的相邻省份、投资攀比可能性较大的公司中更高。本文结合中国市场的特点揭示了导致企业投资本地联动性的新机制,在一定程度上表明了企业投资本地联动性是低效率的。本文的政策启示是强化国内市场的整合程度和企业投资的自主性有助于提高企业投资效率。

Indoor evacuation model based on visual-guidance artificial bee colony algorithm

Research on evacuation simulation and modeling is an important and urgent issue for emergency management.This paper presents an evacuation model based on cellular automata and social force to simulate the evacuation dynamics.Attractive force of target position,repulsive forces of individuals and obstacles,as well as congestion are considered in order to simulate the interaction among evacuees and the changing environment.A visual-guidance-based artificial bee colony algorithm is proposed to optimize the evacuation process.Each evacuee moves toward exits with the guidance of leading bee in his/her visual field.And leading bee is selected according to comprehensive factors including distance from the current individual,the number of obstacles and congestion,which avoids the randomness of roulette mechanism used by basic artificial bee colony algorithm.The experimental results indicate that the proposed model and algorithm can achieve effective performances for indoor evacuation problems with a large number of evacuees and obstacles,which accords with the actual evacuation situation.

Clickable rhodamine spirolactam based spontaneously blinking probe for super-resolution imaging

Spontaneously blinking probe, which switches between dark and bright state without UV or external additives, is extremely attractive in super resolution imaging of live cells. Herein, a clickable rhodamine spirolactam probe, Atto565-Tet, is rationally constructed for spontaneously blinking after biorthogonal labelling and successfully applied to super resolution imaging of mitochondria and lysosomes.

The mechanodonor-acceptor coupling (MDAC) approach for unidirectional multi-state fluorochromism

Uni-directional multi-state fluorochromic scaffolds are valuable photofunctional molecules and yet scarce. We report a general approach for their design, i.e., mechanodonor-acceptor coupling(MDAC). A photochromic molecule is a mechanodonor, due to its capability to convert photonic energy into mechanical force. Upon proper coupling, it can be used to drive a mechanochromic molecule for uni-directional multi-state fluorochromism. The embodiment of this approach is a rhodamine-dithienylethylene hydride(RDH), which has been successfully employed in super-resolution localization microscopy.

Review Quantum science: a review and current research trends

Quantum science is accelerating the transition from research to industrializedapplications and scenarios, and its potential disruptive power in thedevelopment of future technological transformation, operational modes, andeconomy is emerging. In this study, we describe the state of the art of quantumscience, and we attempt to provide an overview of quantum mechanics and itsrelevant prospects. On the other hand, we employ a certain tool (Biblioshinyfrom R Project) to analyze the relevant articles from Web of Science (WoS).The analysis shows that quantum science is an interdisciplinary field that isattracting more and more attention from both academia and practice. Theapplication of quantum computer needs more time to be realized, it is potentialto improve and change the whole society in many aspects.