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.

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Incident Detection Based on Differential Analysis

Internet services and web-based applications play pivotal roles in various sensitive domains, encompassing e-commerce, e-learning, e-healthcare, and e-payment. However, safeguarding these services poses a significant challenge, as the need for robust security measures becomes increasingly imperative. This paper presented an innovative method based on differential analyses to detect abrupt changes in network traffic characteristics. The core concept revolves around identifying abrupt alterations in certain characteristics such as input/output volume, the number of TCP connections, or DNS queries—within the analyzed traffic. Initially, the traffic is segmented into distinct sequences of slices, followed by quantifying specific characteristics for each slice. Subsequently, the distance between successive values of these measured characteristics is computed and clustered to detect sudden changes. To accomplish its objectives, the approach combined several techniques, including propositional logic, distance metrics (e.g., Kullback-Leibler Divergence), and clustering algorithms (e.g., K-means). When applied to two distinct datasets, the proposed approach demonstrates exceptional performance, achieving detection rates of up to 100%.

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。

基于4C/ID模型的面向综合学习的实证研究

本研究旨在探讨两种教学方法:4C/ID(A Four-Component Instructional Design)模型与传统模式对物理专业大学生知识获取和学习迁移的影响。实验组使用4C/ID模型设计进行教学,而控制组则使用传统模式,评估学生的学业表现,包括知识获取与学习迁移,同时对学生的认知负荷及焦虑情绪进行调查。结果显示,实验组在知识获取和学习迁移的评价上均优于控制组,但认知负荷和焦虑情绪也高于控制组,最后对产生的原因和进一步优化进行探讨。

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.

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.

急性髓系白血病患者骨髓中ID3/ID4基因的表达及其临床意义

目的:探索分化抑制因子3及分化抑制因子4(inhibitor of differentiation 3 and inhibitor of differentiation 4,ID3/ID4)两基因在急性髓系白血病(acute myeloid leukemia,AML)患者骨髓中的表达及其临床意义。方法:应用实时荧光定量PCR的方法检测32例非恶性血液病(设对照组)及133例初诊AML患者骨髓单个核细胞中ID3/ID4转录本水平,通过分组分析两者表达的临床意义。结果:AML患者骨髓中ID3/ID4转录本水平较对照组均显著降低(P=0.001及0.002),并且两者之间表达存在轻度正相关(r=0.282,P=0.001)。接收者操作特征曲线分析揭示ID3/ID4转录本水平可作为辅助诊断AML的潜在分子标志(AUC=0.682,P=0.001及AUC=0.673,P=0.002)。通过分组分析发现ID3低表达组患者年龄略小于ID3高表达组患者(P=0.054),NRAS突变频率略低于ID3高表达组患者(P=0.053)。ID4低表达组患者白细胞计数略高于ID4高表达组患者(P=0.088),CEBPA突变频率略高于ID4高表达组患者(P=0.099)。此外,无论在全部患者还是非M3患者中,ID4低表达组病例经过诱导化疗后达完全缓解的概率略低于ID4高表达组病例(P=0.080及0.065)。生存分析发现AML患者及其亚组(非M3及正常核型)中ID3低表达与ID3高表达组患者总体生存相似(P>0.05),ID4低表达病例的总体生存略低于ID4高表达组病例(P=0.058),而在非M3及正常核型患者中存在显著统计学差异(P=0.014及0.002)。结论:ID3/ID4表达下调可能是AML中的常见分子事件,其中ID4表达可能为AML预后判断提供重要参考。

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.

1例先天性糖基化障碍Id型的产前超声表现

先天性糖基化障碍Id型(congenital disorder of glycosylation type Id,CDG-Id)是由于ALG3基因变异,导致编码的α-1,3-甘露糖基转移酶缺陷。本例孕妇32岁,孕7产1,其中第5次单胎妊娠时外院超声提示胎儿畸形,引产后至复旦大学附属妇产科医院行基因检测提示为ALG3基因变异[NM_005787:c.67C>T(p.Gln23*),杂合,父源;NM_005787:c.1188G>A(p.Trp396*),杂合,母源]。本次单胎妊娠21周,我院产前超声表现为胎儿多发畸形,以小下颌、小脑蚓部缺失、后颅窝囊性占位、四肢长骨均短小、脊柱侧弯和手关节僵硬为主要表现。孕妇遂至外院引产,引产后基因检测结果证实仍为ALG3基因变异。本文重点介绍CDG-Id型的产前超声表现及遗传学特征,以提高对本病的认识。

基于改进ID3算法的非结构化大数据分类优化方法

针对非结构化大数据在分类过程中,由于其数据中存在大量的冗余数据,若不能及时清洗大数据中的冗余数据,会降低数据分类精度的问题,提出一种基于改进ID3(Iterative Dichotomiser 3)算法的非结构化大数据分类优化方法。该方法针对非结构化大数据集合中冗余数据多以及维度繁杂的问题,对数据进行清洗处理,并结合有监督辨识矩阵完成数据降维;根据数据降维结果,采用改进ID3算法建立用于数据分类的决策树分类模型,通过该模型对非结构化大数据进行分类处理,从而实现数据的精准分类。实验结果表明,使用该方法对非结构化大数据分类时,分类效果好,精度高。

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4C/ID理论的“国际货运代理”综合学习任务设计

国际货运代理业务是一项多环节、涉及知识面广,跨界融合外贸、物流、关检、法律、信息技术等相关知识的复杂技能。高等职业教育重在培养具备综合能力的高素质技术型应用人才,这就要求教师从实现综合学习的基石—学习任务设计开始就要着力关注学生综合能力的培养。学习任务的设计既要对接企业工作岗位对从业者掌握复杂技能的综合能力要求,又要符合学生的学习规律和认知规律,便于学生对复杂技能的消化和吸收,帮助学生在任务操练的过程中建立起对国际货运代理业务的全局观。这种学习任务设计将有助于学生构建综合性的知识库,有助于学生提高处理国际货运代理真实工作任务的综合能力,有助于学生在国际货运代理行业转型升级中快速实现知识与技能的迁移和迭代。

4C/ID模型在商科课程思政教学模式中的构建及应用

“大思政”课程建设时代,高质量的课程思政教学成为人才培养的内核。高职教育以培养高素质技能型人才为目标,商科教育侧重素质素养的养成与提升,而思政教育与商科课程的融合在深度上还有一定的差距。本文梳理了高职教育商科课程的特性,针对商科课程思政教学存在的问题,提出基于4C/ID模型的课程思政教学设计思路,并以《管理学基础》课程思政教学为例,设计4C/ID模型课程思政教学实施路径,为商科课程思政教学的实践提供借鉴和参考。

Intrusion Detection System Using Classification Algorithms with Feature Selection Mechanism over Real-Time Data Traffic

The key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal.These IDS uses many methods of machine learning(ML)to learn from pastexperience attack i.e.signatures based and identify the new ones.Even though these methods are effective,but they have to suffer from large computational costs due to considering all the traffic features,together.Moreover,emerging technologies like the Internet of Things(Io T),big data,etc.are getting advanced day by day;as a result,network traffics are also increasing rapidly.Therefore,the issue of computational cost needs to be addressed properly.Thus,in this research,firstly,the ML methods have been used with the feature selection technique(FST)to reduce the number of features by picking out only the important ones from NSL-KDD,CICIDS2017,and CIC-DDo S2019datasets later that helped to build IDSs with lower cost but with the higher performance which would be appropriate for vast scale network.The experimental result demonstrated that the proposed model i.e.Decision tree(DT)with Recursive feature elimination(RFE)performs better than other classifiers with RFE in terms of accuracy,specificity,precision,sensitivity,F1-score,and G-means on the investigated datasets.

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

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