Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings

Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

The global pandemic of novel coronavirus that started in 2019 has ser-iously affected daily lives and placed everyone in a panic condition.Widespread coronavirus led to the adoption of social distancing and people avoiding unneces-sary physical contact with each other.The present situation advocates the require-ment of a contactless biometric system that could be used in future authentication systems which makesfingerprint-based person identification ineffective.Periocu-lar biometric is the solution because it does not require physical contact and is able to identify people wearing face masks.However,the periocular biometric region is a small area,and extraction of the required feature is the point of con-cern.This paper has proposed adopted multiple features and emphasis on the periocular region.In the proposed approach,combination of local binary pattern(LBP),color histogram and features in frequency domain have been used with deep learning algorithms for classification.Hence,we extract three types of fea-tures for the classification of periocular regions for biometric.The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel.In order to extract the frequency domain fea-tures,the wavelet transformation is obtained.By learning from these features,a convolutional neural network(CNN)becomes able to discriminate the features and can provide better recognition results.The proposed approach achieved the highest accuracy rates with the lowest false person identification.

Genome-Wide Discovery and Expression Profiling of the SWEET Sugar Transporter Gene Family in Woodland Strawberry (Fragaria vesca) under Developmental and Stress Conditions: Structural and Evolutionary Analysis

The SWEET(sugar will eventually be exported transporter)family proteins are a recently identified class of sugar transporters that are essential for various physiological processes.Although the functions of the SWEET proteins have been identified in a number of species,to date,there have been no reports of the functions of the SWEET genes in woodland strawberries(Fragaria vesca).In this study,we identified 15 genes that were highly homolo-gous to the A.thaliana AtSWEET genes and designated them as FvSWEET1–FvSWEET15.We then conducted a structural and evolutionary analysis of these 15 FvSWEET genes.The phylogenetic analysis enabled us to categor-ize the predicted 15 SWEET proteins into four distinct groups.We observed slight variations in the exon‒intron structures of these genes,while the motifs and domain structures remained highly conserved.Additionally,the developmental and biological stress expression profiles of the 15 FvSWEET genes were extracted and analyzed.Finally,WGCNA coexpression network analysis was run to search for possible interacting genes of FvSWEET genes.The results showed that the FvSWEET10 genes interacted with 20 other genes,playing roles in response to bacterial and fungal infections.The outcomes of this study provide insights into the further study of FvSWEET genes and may also aid in the functional characterization of the FvSWEET genes in woodland strawberries.

Genome-Wide Exploration of the Grape GLR Gene Family and Differential Responses of VvGLR3.1 and VvGLR3.2 to Low Temperature and Salt Stress

Grapes,one of the oldest tree species globally,are rich in vitamins.However,environmental conditions such as low temperature and soil salinization significantly affect grape yield and quality.The glutamate receptor(GLR)family,comprising highly conserved ligand-gated ion channels,regulates plant growth and development in response to stress.In this study,11 members of the VvGLR gene family in grapes were identified using whole-genome sequence analysis.Bioinformatic methods were employed to analyze the basic physical and chemical properties,phylogenetic trees,conserved domains,motifs,expression patterns,and evolutionary relationships.Phylogenetic and collinear analyses revealed that the VvGLRs were divided into three subgroups,showing the high conservation of the grape GLR family.These members exhibited 2 glutamate receptor binding regions(GABAb and GluR)and 3-4 transmembrane regions(M1,M2,M3,and M4).Real-time quantitative PCR analysis demonstrated the sensitivity of all VvGLRs to low temperature and salt stress.Subsequent localization studies in Nicotiana tabacum verified that VvGLR3.1 and VvGLR3.2 proteins were located on the cell membrane and cell nucleus.Additionally,yeast transformation experiments confirmed the functionality of VvGLR3.1 and VvGLR3.2 in response to low temperature and salt stress.Thesefindings highlight the significant role of the GLR family,a highly conserved group of ion channels,in enhancing grape stress resistance.This study offers new insights into the grape GLR gene family,providing fundamental knowledge for further functional analysis and breeding of stress-resistant grapevines.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Individual automatic detection and identification of big cats with the combination of different body parts

The development of facial recognition technology has become an increasingly powerful tool in wild animal indi-vidual recognition.In this paper,we develop an automatic detection and recognition method with the combinations of body features of big cats based on the deep convolutional neural network(CNN).We collected dataset including 12244 images from 47 individual Amur tigers(Panthera tigris altaica)at the Siberian Tiger Park by mobile phones and digital camera and 1940 images and videos of 12 individual wild Amur leopard(Panthera pardus orientalis)by infrared cameras.First,the single shot multibox detector algorithm is used to perform the automatic detection process of feature regions in each image.For the different feature regions of the image,like face stripe or spots,CNNs and multi-layer perceptron models were applied to automatically identify tiger and leopard individuals,in-dependently.Our results show that the identification accuracy of Amur tiger can reach up to 93.27%for face front,93.33%for right body stripe,and 93.46%for left body stripe.Furthermore,the combination of right face,left body stripe,and right body stripe achieves the highest accuracy rate,up to 95.55%.Consequently,the combination of different body parts can improve the individual identification accuracy.However,it is not the higher the number of body parts,the higher the accuracy rate.The combination model with 3 body parts has the highest accuracy.The identification accuracy of Amur leopard can reach up to 86.90%for face front,89.13%for left body spots,and 88.33%for right body spots.The accuracy of different body parts combination is lower than the independent part.For wild Amur leopard,the combination of face with body spot part is not helpful for the improvement of identification accuracy.The most effective identification part is still the independent left or right body spot part.It can be applied in long-term monitoring of big cats,including big data analysis for animal behavior,and be helpful for the individual identification of other wildlife species.

A Non-Singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.

海洋来源青霉Penicillium sp.的化学成分研究

海洋微生物因生存环境特殊,常能形成结构特异、活性良好的代谢产物。本文对1株海洋沉积物中分离得到的青霉Penicillium sp.进行化学成分研究和抗菌活性评价。结果分离和鉴定了4个化合物,分别为Nuatigenin(1)、吲哚-3-乙酸(2)、胸苷(3)和3-苯基乳酸(4),其中化合物1对金黄色葡萄球菌和白色念珠菌均有一定的抑菌活性,化合物4对白色念珠菌抑菌效果良好,其余化合物对不同的菌均无抑制作用。

一株猪细小病毒7群的鉴定和分离

猪细小病毒7群(Porcine parvovirus 7,PPV-7)是2016年首次从美国猪群发现和鉴定的新的猪细小病毒。为弄清我国猪群中是否存在PPV-7,本文用PPV-7特异性的实时荧光定量PCR和常规PCR方法对采集的10份猪流产胎儿和10份保育仔猪病料分别进行检测,结果发现其中1份保育仔猪样品的常规PCR和实时荧光定量PCR检测结果均为阳性,常规PCR扩增出的246 bp特异性条带测序和分子遗传演化时发现,该序列与PPV-7参考毒株KU5637332和KY996757的同源性分别为99.6%和98.0%,表明该样品中含有PPV-7,进一步用PK-15细胞分离病毒,连续传代5次后PK-15细胞都没有出现典型的细胞病变,但其上清液用实时荧光定量PCR方法都可以检测到该病毒。本研究结果证实我国猪群中存在PPV-7的感染,且检测到的PPV-7毒株能在PK15细胞中增殖。

参数识别问题混合有限元解的最大模误差估计

研究了参数识别问题混合有限元解的最大模误差估计.利用1阶Raviart-Thomas混合有限元离散状态和对偶状态变量,利用分片线性函数逼近控制变量,获得了状态变量和控制变量的最大模误差估计,这里控制变量的收敛阶是h^2,状态变量的收敛阶是h3/2|lnh|1/2.最后利用数值算例验证了理论结果.

茶叶挥发性成分中关键呈香成分研究进展

香气是决定茶叶品质的重要因子之一。研究表明,茶叶挥发性成分中存在少量关键呈香成分,对茶叶的香气品质具有重要影响,具有重要的研究价值和研究意义。本文综述了近年来茶叶挥发性成分中这些关键呈香成分的研究进展,包括关键呈香成分的分析鉴定方法和主要茶类(绿茶、红茶、乌龙茶、黑茶)以及其他再加工茶(主要为花茶)中已经鉴定出的关键呈香成分的汇总分析,并探讨茶叶中关键呈香成分未来的研究方向。这些研究结果丰富了茶叶风味品质化学基础理论,可为提升现代茶叶加工技术和开展茶叶香气品质定向调控等提供科学依据。

禽偏肺病毒研究进展

禽偏肺病毒(avian metapneumovirus,aMPV)可感染火鸡、鸡、鸭以及一些野生禽类,传染性强、传播迅速,易造成继发感染,导致严重的上呼吸道感染和产蛋率下降等症状。该病在世界范围内广泛流行,并呈地方性流行,给养禽业造成严重经济损失。本文以国内外对aMPV在禽类中的流行病学调查和基因分析研究报道为基础,从病原学、流行病学、病毒分离鉴定和防治的角度,对其在禽类中的感染情况和引起的相关疾病进行简要概述;比较多种分子生物学检测方法的优缺点和实用性,为建立快速、简便、实用的aMPV检测方法提供参考。

对虾急性肝胰腺坏死综合征不同检测方法结果分析

急性肝胰腺坏死综合征(acute hepatopancreas necrosis disease,AHPND)是一种危害大、新出现的对虾疫病。目前认为该病由一种副溶血性弧菌(Vibrio parahaemolyticus,VP)引起。本试验通过生化鉴定、血清学分型、MALDI-TOF-MS鉴定以及文献建立的PCR方法,在福建省南美白对虾养殖场,对分离于AHPND病虾肝胰腺的VP进行了检测。通过VITEK-2鉴定出81株VP分离株。从中选取9株分别进行MALDI-TOF-MS鉴定、PCR检测和血清学分型,发现这9株分离菌株均与MALDI-TOF-MS数据库中的VP匹配。其中:4株PCR检测为阳性,被鉴定为AHPND VP,其血清型包括O1:KUT和O1:K68;另5株PCR检测为阴性,血清型包括O1:KUT、O3:K6和O1:K68。试验表明:AHPND VP分离株存在不同血清型,可通过MALDI-TOF-MS进行菌种鉴定;MALDI-TOF-MS与PCR结合使用,可以准确、快速鉴定AHPND VP,这有利于开展AHPND的病原学分析、流行病学调查以及诊断和监测。

恩施市酸豇豆中乳酸菌多样性解析及其分离株发酵特性评价

对恩施市酸豇豆中的乳酸菌进行了分离鉴定,同时使用电子鼻和电子舌技术对分离株纯种发酵制备酸豇豆的品质进行了评价。结果表明:分离出的23株乳酸菌菌株,共鉴定为Lactobacillus rhamnosus(鼠李糖乳杆菌),L. fermentum(发酵乳杆菌)和L. plantarum(植物乳杆菌)3个种,其中21株为L. plantarum。通过电子鼻分析发现,传感器W1C、W3C和W5C对多数L. plantarum纯种发酵的酸豇豆响应值明显偏高。通过电子舌分析发现,L. plantarum纯种发酵可明显提升酸豇豆的酸味。通过主成分分析发现,L. plantarum HBUAS51009、L. plantarum HBUAS51016和L. plantarum HBUAS51027纯种发酵的酸豇豆具有较好的品质。由此可见,恩施市酸豇豆中乳酸菌以L. plantarum为主,且添加L. plantarum进行纯种发酵可提升酸豇豆的品质。

一种鲁棒的离线笔迹鉴别方法

离线笔迹鉴别在司法鉴定与历史文档分析中有重要作用.当前的主要离线笔迹鉴别都是基于局部特征提取的方法,其在笔迹检索中严重依赖于数据增强和全局编码,在笔迹识别中需要较多的笔迹信息.针对这一问题,本文提出一种基于统计的文档行分割与深度卷积神经网络相结合的离线笔迹鉴别方法(DLS-CNN).首先,使用基于统计的文档行分割方法将笔迹材料分割成小的像素块;然后,用优化后的残差神经网络作为识别模型;最后,对局部特征使用取均值法进行编码.在ICDAR2013和CVL这两个标准数据集上的实验结果表明,该方法能有效获得鲁棒的局部特征,从而仅需要少量的笔迹信息就能取得较高的识别率,而且不需依赖于数据增强和全局编码就能取得较好的检索效果.实验代码地址:https://github.com/shiming-chen/DLS-CNN.

Damage Identification of Truss Structures Based on Force Method

An computationally efficient damage identification technique for the planar and space truss structures is presented based on the force method and the micro ge-netic algorithm.For this purpose,the general equilibrium equations and the kinematic relations in which the reaction forces and the displacements at nodes are take into ac-count,respectively,are formulated.The compatibility equations in terms of forces are explicitly presented using the singular value decomposition(SVD)technique.Then governing equations with unknown reaction forces and initial elongations are derived.Next,the micro genetic algorithm(MGA)is used to properly identify the site and ex-tent of multiple damage cases in truss structures.In order to verify the accuracy and the superiority of the proposed damage detection technique,the numerical solutions are presented for the planar and space truss models.The numerical results indicate that the combination of the force method and the MGA can provide a reliable tool to accurately and efficiently identify the multiple damages of the truss structures.

Identification of Legionella species by the composition of cellular fatty acids

One hundred andfifty-three isolates from the environment and 36 reference strains of the Legionella were studied with regards to their composition of cellular fatty acids as determined by gas chromatography,and then were classified into 41 groups by numerical analysis.Most reference strains formed only a single group,except L.micdadei,L.jamestowniensis,L.parisiensis,L.jorda-nis,L.feeleii and L.longbeachae,which were clustered into two or three groups.Even serological types of L.pneumophila could be clearly identified.Therefore,in this study,numerical analysis of cellular fatty acid composition is an effective method for identifying Legionella species.

Dynamic parameter identification and nonlinear friction compensation method for safety perception of heavy explosion-proof robots

This article focuses on the problem of how to accurately calculate the joint control torques when the explosion-proof robot performs collision detection without sensors and gives a complete solution.Nonlinear joint frictions are incorporated into the dynamic model of a robotic manip-ulator to improve calculation accuracy.A genetic algorithm is used to optimise the excitation trajectories to fully stimulate the robot dynamic characteristics.Effective and applicable data filtering and smoothing methods are proposed and the Iteratively Reweighted Least-Squares method based on the error term is applied to identify the robot dynamic parameters.Compared with Ordinary Least-Squares method,the proposed algorithm improves the accuracy of joint control torques estimation.

Identification of the Genetic Cause for Childhood Disintegrative Disorder by Whole-Exome Sequencing

Dear Editor,Childhood Disintegrative Disorder（CDD）,also known as Heller’s syndrome and disintegrative psychosis,is a rare progressive neurological disorder,characterized by a late onset（[2 years of age）and regression of language,social

浅谈产品系统设计与品牌塑造

当今社会,设计与品牌密不可分。产品系统设计作为当今产品设计的重要理念与品牌塑造的关系是非常密切的。产品系统设计承担着塑造品牌内涵的重任,而品牌塑造对产品系统设计有重要的引导作用。发现并善用它们的共同点对于达成企业与社会、消费者之间的共识会起到事半功倍的作用。