Analysis on Genetic Diversity of 40 Flowering Cherry Cultivars and Construction of Molecular ID Based on SSR Markers

Studying on the genetic diversity and genetic relationship of flowering cherry cultivars is extremely important for germplasm conservation, cultivar identification and breeding. Flowering cherry is widely cultivated as an important woody ornamental plant in worldwide, especially Japan, China. However, owning to the morphological similarity, many cultivars are distinguished hardly in non-flowering season. Here, we evaluated the genetic diversity and genetic relationship of 40 flowering cherry cultivars, which are mainly cultivated in China. We selected 13 polymorphicprimers to amplify to allele fragments with fluorescent-labeled capillary electrophoresis technology. The population structure analysis results show that these cultivars could be divided into 4 subpopulations. At the population level, Na and Ne were 6.062, 4.326, respectively. Ho and He were 0.458 and 0.670, respectively. The Shannon’s information index (I) was 1.417. The Pop3, which originated from P. serrulata, had the highest Ho, He, and I among the 4 subpopulations. AMOVA showed that only 4% of genetic variation came from populations, the 39% variation came from individuals and 57% (p < 0.05) came from intra-individuals. 5 polymorphic SSR primers were selected to construct molecular ID code system of these cultivars. This analysis on the genetic diversity and relationship of the 40 flowering cherry cultivars will help to insight into the genetic background, relationship of these flowering cherry cultivars and promote to identify similar cultivars.

Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs

Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

ID.2 All 量产在即

在汽车工业发展不断创新并追求低碳环保的背景下,众多车企纷纷推出经济实用的电动车型,以满足不同消费者的需求。大众汽车推出的ID.系列纯电动汽车一直备受关注,ID.2 All概念车一经推出也同样如此。这款电动汽车预计将于2025年量产上市。

Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

Internet of Things(IoT)is the most widespread and fastest growing technology today.Due to the increasing of IoT devices connected to the Internet,the IoT is the most technology under security attacks.The IoT devices are not designed with security because they are resource constrained devices.Therefore,having an accurate IoT security system to detect security attacks is challenging.Intrusion Detection Systems(IDSs)using machine learning and deep learning techniques can detect security attacks accurately.This paper develops an IDS architecture based on Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)deep learning algorithms.We implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that cate-gorizes the network traffic into normal and attacks traffic.In this work,interpolation data preprocessing is used to compute the missing values.Also,the imbalanced data problem is solved using a synthetic data generation method.Extensive experiments have been implemented to compare the performance results of the proposed model(CNN+LSTM)with a basic model(CNN only)using both balanced and imbalanced dataset.Also,with some state-of-the-art machine learning classifiers(Decision Tree(DT)and Random Forest(RF))using both balanced and imbalanced dataset.The results proved the impact of the balancing technique.The proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy(92.10%)compared with the basic CNN model(89.90%)and the machine learning(DT 88.57%and RF 90.85%)models.Moreover,comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

某院PIVAS工作流程优化探究

静脉用药调配中心(PIVAS)是指在符合国际标准、依据药物特性设计的操作环境下,经过职业药师审核的处方由受过专门培训的药技人员严格按照标准操作程序进行全静脉营养、抗肿瘤性药物和抗生素等静脉药物调配,为临床提供优质产品和药学服务的机构。通过对潍坊市人民医院静脉用药调配科的发展历程进行总结讨论,分析前期工作流程中的不足,不断创新实践,从而优化PIVAS工作流程。处方审核作为PIVAS运行的核心部分,是体现药师价值的重要平台。

DL-Powered Anomaly Identification System for Enhanced IoT Data Security

In many commercial and public sectors,the Internet of Things(IoT)is deeply embedded.Cyber security threats aimed at compromising the security,reliability,or accessibility of data are a serious concern for the IoT.Due to the collection of data from several IoT devices,the IoT presents unique challenges for detecting anomalous behavior.It is the responsibility of an Intrusion Detection System(IDS)to ensure the security of a network by reporting any suspicious activity.By identifying failed and successful attacks,IDS provides a more comprehensive security capability.A reliable and efficient anomaly detection system is essential for IoT-driven decision-making.Using deep learning-based anomaly detection,this study proposes an IoT anomaly detection system capable of identifying relevant characteristics in a controlled environment.These factors are used by the classifier to improve its ability to identify fraudulent IoT data.For efficient outlier detection,the author proposed a Convolutional Neural Network(CNN)with Long Short Term Memory(LSTM)based Attention Mechanism(ACNN-LSTM).As part of the ACNN-LSTM model,CNN units are deployed with an attention mechanism to avoid memory loss and gradient dispersion.Using the N-BaIoT and IoT-23 datasets,the model is verified.According to the N-BaIoT dataset,the overall accuracy is 99%,and precision,recall,and F1-score are also 0.99.In addition,the IoT-23 dataset shows a commendable accuracy of 99%.In terms of accuracy and recall,it scored 0.99,while the F1-score was 0.98.The LSTM model with attention achieved an accuracy of 95%,while the CNN model achieved an accuracy of 88%.According to the loss graph,attention-based models had lower loss values,indicating that they were more effective at detecting anomalies.In both the N-BaIoT and IoT-23 datasets,the receiver operating characteristic and area under the curve(ROC-AUC)graphs demonstrated exceptional accuracy of 99%to 100%for the Attention-based CNN and LSTM models.This indicates that these models are capable of making precise predictions.

Critical Nodes Identification: A Non-Cooperative Method for Unknown Topology Information in Ad Hoc Networks

The foundation of ad hoc networks lies in the guarantee of continuous connectivity.However,critical nodes,whose failure can easily destroy network connectivity,will influence the ad hoc network connectivity significantly.To protect the network efficiently,critical nodes should be identified accurately and rapidly.Unlike existing critical node identification methods for unknown topology that identify critical nodes according to historical information,this paper develops a critical node identification method to relax the prior topology information condition about critical nodes.Specifically,we first deduce a theorem about the minimum communication range for a node through the number of nodes and deployment ranges,and prove the universality of the theorem in a realistic two-dimensional scenario.After that,we analyze the relationship between communication range and degree value for each node and prove that the greater number of nodes within the communication range of a node,the greater degree value of nodes with high probability.Moreover,we develop a novel strategy to improve the accuracy of critical node identification without topology information.Finally,simulation results indicate the proposed strategy can achieve high accuracy and low redundancy while ensuring low time consumption in the scenarios with unknown topology information in ad hoc networks.

PP-GAN:Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN

The objective of style transfer is to maintain the content of an image while transferring the style of another image.However,conventional methods face challenges in preserving facial features,especially in Korean portraits where elements like the“Gat”(a traditional Korean hat)are prevalent.This paper proposes a deep learning network designed to perform style transfer that includes the“Gat”while preserving the identity of the face.Unlike traditional style transfer techniques,the proposed method aims to preserve the texture,attire,and the“Gat”in the style image by employing image sharpening and face landmark,with the GAN.The color,texture,and intensity were extracted differently based on the characteristics of each block and layer of the pre-trained VGG-16,and only the necessary elements during training were preserved using a facial landmark mask.The head area was presented using the eyebrow area to transfer the“Gat”.Furthermore,the identity of the face was retained,and style correlation was considered based on the Gram matrix.To evaluate performance,we introduced a metric using PSNR and SSIM,with an emphasis on median values through new weightings for style transfer in Korean portraits.Additionally,we have conducted a survey that evaluated the content,style,and naturalness of the transferred results,and based on the assessment,we can confidently conclude that our method to maintain the integrity of content surpasses the previous research.Our approach,enriched by landmarks preservation and diverse loss functions,including those related to“Gat”,outperformed previous researches in facial identity preservation.

Classification Model for IDS Using Auto Cryptographic Denoising Technique

Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization algorithms.These classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem.Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion.These algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of threats.In this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam optimizer.IDS classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and accuracy.The neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 dataset.Explaining their power and limitations in the proposed methodology that could be used in future works in the IDS area.

基于ID3-CNN的旋转机械故障诊断研究

为解决旋转机械故障类型多、等级不均衡的故障诊断难题,构建了一种基于ID3决策树与卷积神经网络(ID3-CNN)的故障诊断模型。首先对原始信号进行人工时域特征提取,使用t-SNE降维可视化提取出特征混叠的故障,而后利用卷积运算对特征混叠的故障进行二次特征提取,提高模型的特征表达能力,最后使用ID3决策树和卷积神经网络对不同等级的故障进行分类。在轴承数据集上对模型进行了验证,结果表明,严重故障的诊断准确率达到100%,轻微故障的诊断准确率达到95%。与传统的支持向量机及二维卷积神经网络比较,提高了模型的诊断准确率及特征提取能力。

大众电动——ID.2ALL概念车

“3月16日,大众ID.2all概念车正式首发亮相。根据官方发布的参数,该车长4050mm、宽1812mm、高1530mm,轴距2600mm。与大众旗下其他小型车相比,车长与Polo相近,轴距却又与高尔夫接近,似乎是Polo与高尔夫的结合体。”

A Hybrid DNN-RBFNN Model for Intrusion Detection System

Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural Network (DNN-RBFNN) architecture to enhance the accuracy and efficiency of IDS. The hybrid model synergizes the strengths of both dense learning and radial basis function networks, aiming to address the limitations of traditional IDS techniques in classifying packets that could result in Remote-to-local (R2L), Denial of Service (Dos), and User-to-root (U2R) intrusions.

Methane Concentration Influence on Combustion in a Rapid Compression Machine under DFICE Relevant Conditions

In this experimental work,an optically accessible rapid compression machine is used to study the ignition and combustion process under engine relevant operation conditions for five different air-natural gas equivalence ratios(λ)ignited with either 12.2 mg or 6.7 mg of pilot diesel injected at 1,600 bar.Initial temperature of the ambient mixture,walls and injector was 333 K.Additionally,for the short(6.7 mg)diesel injection,the variation in the ID(ignition delay)for two higher ambient temperatures(343 K and 353 K)was measured.Pressure and piston displacement are recorded while two high-speed cameras simultaneously capture signals in the visible range spectrum and at 305 nm wavelength for OH^(*)chemiluminescence respectively.ID is measured both from OH^(*)and pressure rise.From the recorded data,the heat release ratio is estimated and compared with the visual signals.This gives an insight of the temporal and spatial evolution of the flame,as well as a qualitative perception of the transition from spray ignition into a premixed flame in the ambient fuel-air mixture.It was found that increasing the methane concertation delays the ignition,reduces the natural flame luminosity and enhances the OH^(*)chemiluminescence signal.

基于SAP及MES的发动机制造企业的刀具管理系统

通过分析汽车发动机制造企业对刀具管理的信息化需求、SAP及MES刀具管理模块功能的需求,详细阐述了刀具管理系统的构建及应用。刀具管理系统是基于业务流程优化及业务规则而构建,与企业OA、SAP及生产线MES系统集成,并将刀具管理系统应用至车间,实现刀具全生产周期管理、刀具实体ID化管理及刀具寿命管理等。系统上线应用后已取得较好成效。

本刊正式加入OSID开放科学计划

《山东医药》杂志从2019年3月起正式加入OSID(Open Science Identity)开放科学计划。通过在文章上添加开放科学二维标识码(OSID码),为读者和作者提供一个与业界同行和专家进行学术交流研究成果的途径,同时提供系列增值服务,提升论文的科研诚信。

本刊正式加入OSID开放科学计划

《山东医药》杂志从2019年3月起正式加入OSID(Open Science Identity)开放科学计划。通过在文章上添加开放科学二维标识码(OSID码),为读者和作者提供一个与业界同行和专家进行学术交流研究成果的途径,同时提供系列增值服务,提升论文的科研诚信。

本刊正式加入OSID开放科学计划

《山东医药》杂志从2019年3月起正式加入OSID(Open Science Identity)开放科学计划。通过在文章上添加开放科学二维标识码(OSID码),为读者和作者提供一个与业界同行和专家进行学术交流研究成果的途径,同时提供系列增值服务,提升论文的科研诚信。

国际全科前沿——2023年的秋季,COVID-19或许被遗忘但并未消失

2023年9月JAMA刊登了来自美国埃默里大学医学教授Carlos del Rio的文章:COVID-19 in the Fall of 2023-Forgotten but Not Gone,提出了COVID-19可能已被遗忘,但它并没有消失。医生和患者都应该把SARS-CoV-2列入引起重大呼吸系统疾病的清单中,且保护最脆弱的人群仍是重点。虽然COVID-19不再是一个公共卫生威胁,但感染的增加在可预见的将来或许还会发生。

本刊关于投稿时OSID注册及内容上传的相关事项

《肝胆胰外科杂志》自2019年1月起正式开展开放科学标识(OSID)业务,通过在稿件中添加OSID码,为作者提供一个与读者和业界同行进行学术交流的途径。拟投稿的作者,请登录我刊网站https://gdy.qk.wmu.edu.cn,在页面右侧栏“下载中心”查看“OSID码创建流程及投稿要求”,按照步骤生成OSID码,将此码附于稿件中,然后在网站进行相应的投稿。

本刊关于投稿时OSID注册及内容上传的相关事项

《肝胆胰外科杂志》自2019年1月起正式开展开放科学标识(OSID)业务,通过在稿件中添加OSID码,为作者提供一个与读者和业界同行进行学术交流的途径。拟投稿的作者,请登录我刊网站https://gdy.qk.wmu.edu.cn,在页面右侧栏“下载中心”查看“OSID码创建流程及投稿要求”,按照步骤生成OSID码,将此码附于稿件中,然后在网站进行相应的投稿。