基于混合嵌入和胶囊网络的恶意URL分类模型

作为最常见的网络安全威胁之一,恶意URL攻击每年都会造成巨大的经济损失,尽管研究人员已经提出了许多方法来检测恶意URL,但现有方法存在不能充分利用URL提供的有用信息和对URL中的区分性特征提取不充分的问题,导致分类性能不佳。提出了一种基于混合嵌入和胶囊网络的恶意URL分类模型,引入高速网络和胶囊网络从URL的混合嵌入表示中提取区分性特征,提高了模型的分类性能。在公开数据集ISCX-URL2016上的实验结果表明,该文模型与其他基线方法相比,实现了更好的分类性能,能够有效应对混淆技术的多样性。

Progress in the Study of Laboratory Indicators Related to Acute Pancreatitis

Acute pancreatitis (AP) is one of the more common gastrointestinal diseases in clinics and is characterized by rapid progression, many complications, and high mortality. When it develops into severe pancreatitis, its prognosis is poor. Therefore, early assessment of the degree of inflammatory response plays a crucial role in the treatment plan and prognosis of patients. More and more studies have shown that the levels of D-dimer (D-D), angiotensin-2 (Ang-2), phosphate, heparin-binding protein (HBP), retinol-binding protein-4 (RBP4), and osteoblastic protein (OPN) are closely related to the severity of acute pan-creatitis and can be used as effective indicators for early assessment of AP. In this paper, the research progress of the above indicators in assessing the severity of AP is summarized.

Impact of numerous larval diets on the biology of pink bollworm Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)under laboratory conditions

Background Pink bollworm,Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)has become a poten-tial pest of cotton by causing substantial yield losses around the world including Pakistan.Keeping in view the facts like limited research investigations,unavailability,and high cost of artificial diet’s constituents and their premixes,the present research investigations on the dietary aspect of P.gossypiella were conducted.The larvae of P.gossypiella were reared on different diets that were prepared using indigenous elements.The standard/laboratory diet com-prised of wheat germ meal 34.5 g,casein 30.0 g,agar–agar 20.0 g,sucrose 10.0 g,brewer’s yeast 5.0 g,α-cellulose 1.0 g,potassium-sorbate1.5 g,niplagin 0.5 g,decavitamin 0.01 g,choline-chloride 0.06 g,maize-oil 3.30 g,honey 2.0 g,and water 730.0 mL.Alternatives to cotton bolls and wheat germ meal were okra seed sprouts,okra fruit,cottonseed meal,and okra seed meals,which were included in the study to introduce an efficient and economic mass-rearing system.Results The larval development completed in 19.68d±0.05 d with a weight of 20.18mg±0.20 mg at the fourth instar fed on the cottonseed meal-based diet instead of wheat germ meal based diet.On the same diet,84.00%±4.00%,17.24 mg±0.03 mg,and 7.76d±0.06 d were recorded as pupae formation,pupal weight,and pupal duration,respectively.Adult emergence,76.00%±1.00%was recorded from pupae collected from larvae raised on cottonseed meal-based diet.These male and female moths lived for 40.25d±0.10 d,and 44.34d±0.11 d,respectively.Females deposited 21.28±0.04 eggs per day with the viability of 65.78%±0.14%.The larval mortal-ity at the fourth instar was 37.20%±1.36%and malformed pupation of 12.00%±1.41%was recorded.Replacement of wheat germ meal with that of local meals(cottonseed and okra seed)in the standard laboratory diet has saved 463.80 to 467.10 PKR with 1.62 to 1.63 cost economic returns,respectively.Conclusion This research is of novel nature as it provides a concise and workable system for the economic and suc-cessful rearing of P.gossypiella under laboratory conditions.

Impact of Laboratory Value Flowsheet in Electronic Health Record (EHR) Documentation Time

Research on the use of EHR is contradictory since it presents contradicting results regarding the time spent documenting. There is research that supports the use of electronic records as a tool to speed documentation;and research that found that it is time consuming. The purpose of this quantitative retrospective before-after project was to measure the impact of using the laboratory value flowsheet within the EHR on documentation time. The research question was: “Does the use of a laboratory value flowsheet in the EHR impact documentation time by primary care providers (PCPs)?” The theoretical framework utilized in this project was the Donabedian Model. The population in this research was the two PCPs in a small primary care clinic in the northwest of Puerto Rico. The sample was composed of all the encounters during the months of October 2019 and December 2019. The data was obtained through data mining and analyzed using SPSS 27. The evaluative outcome of this project is that there is a decrease in documentation time after implementation of the use of the laboratory value flowsheet in the EHR. However, patients per day increase therefore having an impact on the number of patients seen per day/week/month. The implications for clinical practice include the use of templates to improve workflow and documentation as well as decreasing documentation time while also increasing the number of patients seen per day. .

Laboratory or Department?Exploration and Creation in Computer Science and Technology

In the very beginning,the Computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the university.As computer science developed as a discipline in its own right,boundaries necessarily arose between it and other disciplines,in a way that is now often detrimental to progress.Therefore,it is necessary to reinvigorate the relationship between computer science and other academic disciplines and celebrate exploration and creativity in research.To do this,the structures of the academic department have to act as supporting scaffolding rather than barriers.Some examples are given that show the efforts being made at the University of Cambridge to approach this problem.

Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7

Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.

网络信息资源归档挑战与对策——基于URL无序性和时效性差异的视角

根据西方网络信息资源归档最新理论与实践成果及将异常现象作为示踪剂的研究方法,本研究尝试将网络信息资源归档系统的后台异常现象引导至前台讨论,从URL无序性和时效性差异的研究视角阐释解构、规范URL的必要性以及通过自动代码或搜索工具对时间戳展开简单聚合或排序的弊端,提出超越以算法为中心的归档逻辑并广泛关注网络基础设施动态与关系互动或对我国网络信息资源归档研究具有一定启示。

National Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization

National Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.The laboratory was reconstructed based on former State Key Laboratory of Baiyun Obo Rare Earth Resources Researches and Comprehensive Utilization.The key laboratory takes Baotou Research Institute of Rare Earths as the main research body,cooperating with scientific and technological strength from Lanzhou University and Changsha Research Institute of Mining and Metallurgy.

一种基于FTCNN-BILSTM的恶意URLs检测方法

针对目前恶意URL检测模型中泛化性不够好,准确率不够高的问题,提出一种基于字符嵌入编码的FTCNN-LSTM的恶意URL多分类检测方法。该方法对URL中的每一个字符进行向量化编码,并通过批规范化和定向Dropout对神经网络进行剪枝,并使用Focal Loss损失函数解决数据集不平衡的问题。实验结果显示,与机器学习训练的模型相比,该方法在多分类上召回率提升了1.73%。该模型在ISCX-URL2016和malicious_phish URL数据集上的多分类平均预测结果均能达到98.63%以上,具有较好的泛化能力。

基于代价敏感学习的恶意URL检测研究

随着大数据时代的到来,恶意URL作为Web攻击的媒介渐渐威胁着用户的信息安全。传统的恶意URL检测手段如黑名单检测、签名匹配方法正逐步暴露缺陷,为此本文提出一种基于代价敏感学习策略的恶意URL检测模型。为提高卷积神经网络在恶意网页检测领域的性能,本文提出将URL数据结合HTTP请求信息作为原始数据样本进行特征提取,解决了单纯URL数据过于简单而造成特征提取困难的问题,通过实验对比了三种编码处理方式,根据实验结果选取了最佳字符编码的处理方式,保证了后续检测模型的效果。同时本文针对URL字符输入的特点,设计了适合URL检测的卷积神经网络模型,为了提取数据深层特征,使用了两层卷积层进行特征提取,其次本文在池化层选择使用BiLSTM算法提取数据的时序特征,同时将该网络的最后一个单元输出达到池化效果,避免了大量的模型计算,保证了模型的检测效率。同时为解决数据样本不均衡问题,在迭代过程中为其分配不同惩罚因子,改进了数据样本初始化权重的分配规则并进行了归一化处理,增加恶意样本在整体误差函数中的比重。实验结果表明本文模型在准确率、召回率以及检测效率上较优于其他主流检测模型,并对于不均衡数据集具有较好的抵抗能力。

基于Stacking集成学习的恶意URL检测系统设计与实现

针对传统URL检测方法在恶意URL检测时存在的准确率不高、实时性差等问题,提出一种基于Stacking集成学习的算法模型。该模型采用机器学习单一方法中的岭分类、支持向量机、朴素贝叶斯作为初级学习器,采用逻辑回归作为次级学习器,通过初级学习器和次级学习器相结合的双层结构对URL进行检测。使用大量的URL数据集分别对单一方法中的模型和Stacking集成学习方法的模型进行训练,并对每种模型进行评估。评估结果表明,Stacking集成学习的算法模型对恶意URL检测的准确率可达98.75%,与其他模型相比提升0.75%以上。采用Flask作为开发框架,实现了恶意URL检测系统的功能,并对系统进行云端等部署,得到系统根据用户输入的URL链接可以输出URL的检测结果,具有较好的应用价值。

基于双层注意力机制的恶意URL检测

随着信息化技术的不断发展,网络空间中存在的威胁也在不断变化。其中,基于恶意URL的攻击手段层出不穷。针对恶意URL识别与检测问题进行了深入探究,设计并实现了具有双层注意力机制的Bi-LSTM网络模型对恶意URL进行识别和检测,并将其命名为A2Bi-LSTM。该模型分别在字符级别及单词级别对恶意URL中包含的可疑内容进行注意力权值的计算,进一步提升了恶意URL的识别精度。实验结果表明,A2Bi-LSTM对恶意URL的识别准确率达到97%,相较于传统检测模型有着更好的检测效果,能够有效应对此类攻击威胁,有助于网络空间安全体系的构建。

Optimal laboratory testing protocol for patients with acne taking oral isotretinoin

BACKGROUND Isotretinoin is a widely prescribed drug for various dermatological conditions,especially acne.Blood tests are routinely performed to monitor its side effects.However,the optimal testing schedule remains controversial.AIM To evaluate the laboratory monitoring tests and schedules of patients with acne taking isotretinoin to determine the most efficient testing regimen.METHODS We retrospectively reviewed electronic medical records from the Dermatology Department of Dongguk University Ilsan Hospital from 2005 to 2020 for patients prescribed isotretinoin for acne who underwent monthly blood tests.RESULTS Aspartate aminotransferase(AST),alanine aminotransferase(ALT),and trigly-ceride(TG)levels significantly changed between 5 and 6 mo when the total prescription period and dose variables were considered altogether.The high-density lipoprotein level also significantly changed between 5 and 6 mo.However,low-density lipoprotein(LDL)and total cholesterol levels significantly changed between 1 and 2 mo.CONCLUSION We recommend testing AST,ALT,and TG levels once every 5 to 6 mo.We also suggest testing LDL and total cholesterol levels during the first and second months.

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems,scams,and other such cyberattacks.These attacks hijack huge quantities of the available data,incurring heavy financial loss.At the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious URLs.Finally,AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

一种基于BLCNA模型的恶意URL检测技术

针对传统方法对恶意URL识别准确度不高、耗时长的问题,提出一种基于注意力机制的神经网络联合模型(BLCNA)来检测恶意URL。提取URL的语义信息和视觉信息进行编码处理,结合双向长短期记忆网络(BiLSTM)和胶囊网络(CapsNet)构建神经网络联合模型来同步捕获语义和视觉特征,利用注意力机制增加关键特征的权重,基于有效特征完成对URL的分类。实验结果表明,所提方法在检测恶意URL检测方面优于其他方法,准确率可以达到99.79%。

Modelling an Efficient URL Phishing Detection Approach Based on a Dense Network Model

The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing.The deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator(URLs)analysis.The legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions,which deals with the detection of phishing.Contrarily,the URLs in both classes from the login page due,considering the representation of a real case scenario and the demonstration for obtaining the rate of false-positive with the existing approaches during the legal login pages provides the test having URLs.In addition,some model reduces the accuracy rather than training the base model and testing the latest URLs.In addition,a feature analysis is performed on the present phishing domains to identify various approaches to using the phishers in the campaign.A new dataset called the MUPD dataset is used for evaluation.Lastly,a prediction model,the Dense forward-backwards Long Short Term Memory(LSTM)model(d−FBLSTM),is presented for combining the forward and backward propagation of LSMT to obtain the accuracy of 98.5%on the initiated login URL dataset.

Contribution of Simulation to the Skills Laboratory for the Learning of Obstetrical Examination in an African Setting with Limited Resources

Objective: To evaluate the interest of the simulation in the learning of the obstetric examination of parturient by medical students. Materials and methods: It was an analytical cross-sectional study that took place from 1 to 28 February 2020 at the Yalgado Ouedraogo Teaching Hospital in Ouagadougou, Burkina Faso. The study population was made up of medical students at the Doctorate II level. The previous experience of the students in the obstetric examination of the parturient, the contribution of the simulation on their feelings and on their skills towards this examination were studied. The Mc Nemar Chi2 test was used to compare percentages on paired samples. Results: The level of difficulty in performing the obstetrical examination in the parturient evolved downwards after the practice of the simulation. The stress level to perform the obstetric exam has been significantly reduced. On the other hand, confidence has increased. The students’ ability to appreciate each of the elements of the obstetric examination studied had improved after the practice of simulation. Conclusion: The simulation in the skills laboratory was a great contribution and the acquisition of models of better fidelity would help to improve the contribution to the confidence building and the acquisition of skills by learners.

Study on Load Bearing Characteristics of Novel Expandable Deepwater Drilling Conductor Based on Laboratory Experiment and Field Test

A novel expandable conductor was designed and applied in deep-water drilling to improve the vertical and lateral bearing capacity with a significant reduction of conductor jetting depth and soaking time. The vertical and lateral bearing capability of expandable conductors was depicted based on the ultimate subgrade reaction method and pile foundation bearing theory. The load-bearing characteristics of a laboratory-scale expandable conductor were analyzed through laboratory experiments. The serial simulation experiments are accomplished to study the bearing characteristics(vertical ultimate bearing capacity, lateral soil pressure, and lateral displacement) during the conductor soaking process. The laboratory experimental results show that the larger the length and thickness of expandable materials are,the higher the bearing capacity of the wellhead will be. During the conductor soaking process, the soil pressure around the three expandable conductors increases faster, strings representing a stronger squeezing effect and resulting in higher vertical bearing capacity. Furthermore, the lateral displacement of novel expandable conductor is smaller than that of the conventional conductor. All the advantages mentioned above contributed to the reduction of conductor’s jetting depth and soaking time. Lastly, the application workflow of a novel expandable deep-water drilling conductor was established and the autonomous expandable conductor was successfully applied in the South China Sea with a significant reduction of conductor’s jetting depth and soaking time. According to the soil properties and designed installation depth of the surface conductor, the arrangement of expandable materials should be designed reasonably to meet the safety condition and reduce the construction cost of the subsea wellhead.

Machine Learning Techniques for Detecting Phishing URL Attacks

Cyber Attacks are critical and destructive to all industry sectors.They affect social engineering by allowing unapproved access to a Personal Computer(PC)that breaks the corrupted system and threatens humans.The defense of security requires understanding the nature of Cyber Attacks,so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber Attacks.Cyber-Security proposes appropriate actions that can handle and block attacks.A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private information.One of the online security challenges is the enormous number of daily transactions done via phishing sites.As Cyber-Security have a priority for all organizations,Cyber-Security risks are considered part of an organization’s risk management process.This paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks.A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website(1 or−1).Furthermore,we determined the confusion matrices of Machine Learning models:Neural Networks(NN),Na飗e Bayes,and Adaboost,and the results indicated that the accuracies achieved were 90.23%,92.97%,and 95.43%,respectively.

The Influence of Integrated Chinese and Western Medicine Treatment on the Quality of Life and Laboratory Indicators of Patients with Novel Coronavirus Pneumonia

Objective: To investigate the influence of integrated Chinese and Western medicine treatment on the quality of life and laboratory indicators of patients with novel coronavirus pneumonia (COVID-19). Methods: A prospective, self-controlled study was conducted to analyze the changes in corresponding laboratory indicators and quality of life in 75 confirmed COVID-19 patients treated with integrated Chinese and Western medicine in our hospital during the early stage, middle stage, recovery period, and two weeks after discharge. The effectiveness and safety of the treatment regimen were evaluated in conjunction with the time for 2019-nCoV nucleic acid conversion, disease progression, and adverse reactions. Results: The PLT levels in the initial stage were significantly lower than those in the recovery period in 75 patients. The CRP levels in the initial stage were significantly lower than those after discharge for 2 weeks. The TBIL, IBTL, and DHIL levels in the initial stage were significantly lower than those in the middle stage. The K+ levels in the initial stage were significantly lower than those in the recovery period and after discharge for 2 weeks. The LYMGH levels in the initial stage were significantly lower than those in the recovery period and after discharge for 2 weeks. The TP and ALB levels in the initial stage were higher than those in the middle stage and the recovery period. The LDH levels, scores of daily activity limitation, scores of respiratory distress symptoms, scores of psychological emotions, CT imaging scores, and positive rate of nucleic acid were significantly lower than those in the recovery period and after discharge for 2 weeks. The AG, CK, CK-MB, and α-HBDH levels in the initial stage were significantly higher than those in the recovery period. However, the AMY level in the initial stage was significantly lower than that in the recovery period and after discharge for 2 weeks (p Conclusion: Integrated Chinese and Western medicine treatment has a significant impact on laboratory indicators such as PLT, LYMGH, CRP, TBIL, IBTL, DHIL, TP, ALB, K+, AG, LDH, CK, CK-MB, α-HBDH, AMY, CT imaging, and 2019-nCoV nucleic acid in COVID-19 patients. It has good clinical efficacy and safety, and can improve the quality of life of patients.