Analysis of CH_(4) and H_(2) Adsorption on Heterogeneous Shale Surfaces Using aMolecular Dynamics Approach

Determining the adsorption of shale gas on complex surfaces remains a challenge in molecular simulation studies.Difficulties essentially stem from the need to create a realistic shale structure model in terms of mineral heterogeneityand multiplicity.Moreover,precise characterization of the competitive adsorption of hydrogen andmethane in shale generally requires the experimental determination of the related adsorptive capacity.In thisstudy,the adsorption of adsorbates,methane(CH_(4)),and hydrogen(H_(2))on heterogeneous shale surface modelsof Kaolinite,Orthoclase,Muscovite,Mica,C_(60),and Butane has been simulated in the frame of a moleculardynamic’s numerical technique.The results show that these behaviors are influenced by pressure and potentialenergy.On increasing the pressure from 500 to 2000 psi,the sorption effect for CH_(4)significantly increasesbut shows a decline at a certain stage(if compared to H_(2)).The research findings also indicate that raw shalehas a higher capacity to adsorb CH_(4)compared to hydrogen.However,in shale,this difference is negligible.

Detecting and Preventing of Attacks in Cloud Computing Using Hybrid Algorithm

Cloud computing is the technology that is currently used to provide users with infrastructure,platform,and software services effectively.Under this system,Platform as a Service(PaaS)offers a medium headed for a web development platform that uniformly distributes the requests and resources.Hackers using Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks abruptly interrupt these requests.Even though several existing methods like signature-based,statistical anomaly-based,and stateful protocol analysis are available,they are not sufficient enough to get rid of Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks and hence there is a great need for a definite algorithm.Concerning this issue,we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis,Spearman coefficient,and mitigation technique.It can easily differentiate common traffic and attack traffic.Not only that,it greatly helps the network to distribute the resources only for authenticated requests.The effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99%for the numerous DoS attack as well as DDoS attacks.

Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

Validation of Foreground Emission Model using GALEX Medium Observations

The study of diffuse ultraviolet(UV)background radiation is vital in the investigation of stellar and galactic evolution.Space-based UV observations are comprised of both foreground and background radiations.The foreground emission in an observation is a result of solar contamination in the direction of observation.In our previous work,we modeled airglow(one of the major constituents of the foreground emission)as a function of10.7 cm Solar Flux and Sun Angle with great accuracy using GALEX deep observations.We adopt a similar methodology to validate the obtained model and run equivalent experiments here using far-UV(FUV)and nearUV(NUV)GALEX medium imaging surveys(MIS)with a total exposure time greater than 3300 s.We obtained a predictive model having excellent compatibility with the earlier model.Our analysis shows that the total foreground emission varies between 59 and 295 photon units in FUV whereas in NUV,it varies between 671 and1195 photon units depending upon the date and time of observation.We also noticed a strong correlation between the background emission and optical depth both in FUV and NUV,especially in the low density regions.This clearly indicates that the major contributor in diffuse background radiation is the starlight scattered by interstellar dust grains.

A Universal BIST Approach for Virtex-Ultrascale Architecture

Interconnected cells,Configurable Logic Blocks(CLBs),and input/output(I/O)pads are all present in every Field Programmable Gate Array(FPGA)structure.The interconnects are formed by the physical paths for connecting the blocks.The combinational and sequential circuits are used in the logic blocks to execute logical functions.The FPGA includes two different tests called interconnect testing and logical testing.Instead of using an additional circuitry,the Built-in-Self-Test(BIST)logic is coded into an FPGA,which is then reconfigured to perform its specific operation after the testing is completed.As a result,additional test circuits for the FPGA board are no longer required.The FPGA BIST has no area overhead or performance reduction issues like conventional BIST.A resource-efficient testing scheme is essential to assure the appropriate operation of FPGA look-up tables for effectively testing the functional operation.In this work,the Configurable Logic Blocks(CLBs)of virtex-ultrascale FPGAs are tested using a BIST with a simple architecture.To evaluate the CLBs’capabilities including distributed modes of operation of Random Access Memory(RAM),several types of configurations are created.These setups have the ability to identify 100%stuck-at failures in every CLB.This method is suitable for all phases of FPGA testing and has no overhead or performance cost.

An Efficient Hybrid Optimization for Skin Cancer Detection Using PNN Classifier

The necessity of on-time cancer detection is extremely high in the recent days as it becomes a threat to human life.The skin cancer is considered as one of the dangerous diseases among other types of cancer since it causes severe health impacts on human beings and hence it is highly mandatory to detect the skin cancer in the early stage for providing adequate treatment.Therefore,an effective image processing approach is employed in this present study for the accurate detection of skin cancer.Initially,the dermoscopy images of skin lesions are retrieved and processed by eliminating the noises with the assistance of Gaborfilter.Then,the pre-processed dermoscopy image is segmented into multiple regions by implementing cascaded Fuzzy C-Means(FCM)algorithm,which involves in improving the reliability of cancer detection.The A Gabor Response Co-occurrence Matrix(GRCM)is used to extract melanoma parameters in an effi-cient manner.A hybrid Particle Swarm Optimization(PSO)-Whale Optimization is then utilized for efficiently optimizing the extracted features.Finally,the fea-tures are significantly classified with the assistance of Probabilistic Neural Net-work(PNN)classifier for classifying the stages of skin lesion in an optimal manner.The whole work is stimulated in MATLAB and the attained outcomes have proved that the introduced approach delivers optimal results with maximal accuracy of 97.83%.

Early Detection of Heartbeat from Multimodal Data Using RPA Learning with KDNN-SAE

Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.In contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.In the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CIF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.

Topic Controlled Steganography via Graph-to-Text Generation

Generation-based linguistic steganography is a popular research area of information hiding.The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to.However,in the course of our experiment,we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text,which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases,and that the topic of generated texts is uncontrollable,so there is still room for improvement in concealment.In this paper,we propose a topic-controlled steganography method which is guided by graph-to-text generation.The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs,and the topic of the generated texts is controllable.We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation.Different from traditional linguistic steganography methods,we encode the secret information during graph path coding rather than using conditional probability.We test our method in different aspects and compare it with other text generative steganographic methods.The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.

Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

At an early point,the diagnosis of pancreatic cancer is mediocre,since the radiologist is skill deficient.Serious threats have been posed due to the above reasons,hence became mandatory for the need of skilled technicians.However,it also became a time-consuming process.Hence the need for automated diagnosis became mandatory.In order to identify the tumor accurately,this research pro-poses a novel Convolution Neural Network(CNN)based superior image classi-fication technique.The proposed deep learning classification strategy has a precision of 97.7%,allowing for more effective usage of the automatically exe-cuted feature extraction technique to diagnose cancer cells.Comparative analysis with CNN-Grey Wolf Optimization(GWO)is carried based on varied testing and training outcomes.The suggested study is carried out at a rate of 90%–10%,80%–20%,and 70%–30%,indicating the robustness of the proposed research work.Outcomes show that the suggested method is effective.GWO-CNN is reli-able and accurate relative to other detection methods available in the literatures.

Microscopic and metabolic investigations disclose the factors that lead to skin cracking in chili-type pepper fruit varieties

The hydrophobic cuticle encasing the fruit skin surface plays critical roles during fruit development and post-harvest.Skin failure often results in the fruit surface cracking and forming a wound-periderm tissue made of suberin and lignin.The factors that make the fruit skin susceptible to cracking have yet to be fully understood.Herein,we investigated two varieties of chili peppers(Capsicum annuum L.),Numex Garnet,whose fruit has intact skin,and Vezena Slatka,whose fruit has cracked skin.Microscopical observations,gas chromatography-mass spectrometry,biochemical and gene expression assays revealed that Vezena Slatka fruit form a thicker cuticle with greater levels of cutin monomers and hydroxycinnamic acids,and highly express key cutin-related genes.The skin of these fruit also had a lower epidermal cell density due to cells with very large perimeters,and highly express genes involved in epidermal cell differentiation.We demonstrate that skin cracking in the Vezena Slatka fruit is accompanied by a spatial accumulation of lignin-like polyphenolic compounds,without the formation of a typical wound-periderm tissues made of suberized cells.Lastly,we establish that skin cracking in chili-type pepper significantly affects fruit quality during post-harvest storage in a temperature-dependent manner.In conclusion,our data highlight cuticle thickness and epidermal cell density as two critical factors determining fruit skin susceptibility to cracking in chili-type pepper fruit.

顾客满意度指数与质量指数的异同分析

本文以美国顾客满意度指数 ACSI和浙江省质量指数 ZQI为例，对顾客满意度指数 CSI和质量指数 QI的异同点进行分析，为建立具有中国特色的质量评价方法和构建中国顾客满意度指数 CCSI提供帮助。

全球碳捕集与封存技术进展及其在水泥行业应用前景初探

作为全球应对气候变化的技术途径之一，碳捕集与封存（Carbon Capture and Storage，CCS）技术受到了世界各国的广泛重视。尽管国人对其尚有陌生，但CCS技术已是当前气候变化领域最前沿、最重大的课题之一，全球积极倡导碳减排的主要能源研究机构、组织和国家，已经一致将CCS技术列为未来的碳减排关键技术，并斥巨资开展CCS技术的相关研究和工业化实践。

转型的迷茫与困惑

对于1990年前后苏联的激进"转型",美方的对策可以归纳为老布什政府的"三部曲":苏解体前消极观望、以不变应万变;解体后的短暂震撼与迷茫;以及随后的惊喜和庆贺。然而自诩为"赢得冷战"的布什,由于美国经济不振,民心浮动,在苏联解体后不久也输掉了大选。至此,尽管布、戈二人在外交方面的业绩可圈可点(戈1990年还获得诺贝尔和平奖),但都被各自的人民所抛弃。其实冷战的结局远非输赢可以定论。冷战是延续数百年的"西方内战"、即西方强权和主要意识形态相互对立和冲突的巅峰和最后阶段;各方均付出沉重代价。此后20年间,美国为追求绝对安全和绝对行动自由,耗损了大量软硬实力。冷战结束的真正意义在于,西方在整合内部以后,得以转向针对非西方世界的"文明冲突"。在学界,苏联解体使西方"苏联学"顿失目标和动力,西方国际关系学主流也集体失语,面临着非主流和新生派的强力挑战。美国《共产主义问题》杂志死而复生,成为美国的"苏联研究"转向"俄罗斯学"的缩影;西方的俄罗斯研究既面临转型的挑战和契机,也不乏巨大的想象和操作空间。

可靠性维修基本原理——工业管理的未来

工业维修正经历一场维修部门从集中管理到高水平的业务职能管理的转变。在许多工厂里,维修费用占生产成本从4％飞速增长到14％,往往大于利润(参见图1)。杜邦公司宣称。

《2004中国现代化报告》与杭州比照研究

《2 0 0 4中国现代化报告》提出了一个新的理论框架———现代化的阶段论 :工业文明与知识文明两阶段论。杭州作为“发展中国家”中国的一座“发展中城市”与第一次经典现代化的完成还有差距 ,又在改革开放中萌发出一部分第二次现代化要素。杭州已在不少方面已基本上达到第一次现代化所要求的指标 ,在如何走“新型工业化道路”的问题上 ,当前应着重探索加大技术创新力度等方面 。

印尼海洋主张如何对接“一带一路”?

全球海洋支轴计划要求印尼建设更多港口、海洋桥梁、采购不同大小的船舶，这与中国通过海丝路计划对印尼海洋基础设施进行投资，有着巨大的重叠潜力。

Taste masking of ciprofloxacin by ion-exchange resin and sustain release at gastric-intestinal through interpenetrating polymer network

The aim of the study was to taste mask ciprofloxacin(CP)by using ion-exchange resins(IERs)followed by sustain release of CP by forming interpenetrating polymer network(IPN).IERs based on the copolymerization of acrylic acid with different cross linking agents were synthesised.Drug-resin complexes(DRCs)with three different ratios of drug to IERs(1:1,1:2,1:4)were prepared&evaluated for taste masking by following in vivo and in vitro methods.Human volunteers graded ADC 1:4,acrylic acid-divinyl benzene(ADC-3)resin as tasteless.Characterization studies such as FTIR,SEM,DSC,P-XRD differentiated ADC 1:4,from physical mixture(PM 1:4)and confirmed the formation of complex.In vitro drug release of ADC 1:4 showed complete release of CP within 60 min at simulated gastric fluid(SGF)i.e.pH 1.2.IPN beads were prepared with ADC 1:4 by using sodium alginate(AL)and sodium alginate-chitosan(AL-CS)for sustain release of CP at SGF pH and followed by simulated intestinal fluid(SIF i.e.pH 7.4).FTIR spectra confirmed the formation of IPN beads.The release of CP was sustain at SGF pH(<20%)whereas in SIF media it was more(>75%).The kinetic model of IPN beads showed the release of CP was non-Fickian diffusion type.

用不锈钢包裹建筑——可持续性幕墙和屋面

恰当地选择和维护不锈钢,可延用几个世纪而保持其吸引力。在历经如此之长的使用寿命后,不锈钢具有的高废钢价值和低腐蚀率,保证它使用期结束后可以回收再利用,且回收利用率非常之高。本文阐释了不锈钢在可持续屋面和幕墙工程上的运用。

Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents

In this research paper,we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus,thereby checking whether it is possible to detect extremist messages in the Kazakh language.To do this,the authors trained models using six classic machine-learning algorithms such as Support Vector Machine,Decision Tree,Random Forest,K Nearest Neighbors,Naive Bayes,and Logistic Regression.To increase the accuracy of detecting extremist texts,we used various characteristics such as Statistical Features,TF-IDF,POS,LIWC,and applied oversampling and undersampling techniques to handle imbalanced data.As a result,we achieved 98%accuracy in detecting religious extremism in Kazakh texts for the collected dataset.Testing the developed machine learningmodels in various databases that are often found in everyday life“Jokes”,“News”,“Toxic content”,“Spam”,“Advertising”has also shown high rates of extremism detection.

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers,providers and the workers. Requisition for Edge Computing based items havebeen increasing tremendously. Apart from the advantages it holds, there remainlots of objections and restrictions, which hinders it from accomplishing the needof consumers all around the world. Some of the limitations are constraints oncomputing and hardware, functions and accessibility, remote administration andconnectivity. There is also a backlog in security due to its inability to create a trustbetween devices involved in encryption and decryption. This is because securityof data greatly depends upon faster encryption and decryption in order to transferit. In addition, its devices are considerably exposed to side channel attacks,including Power Analysis attacks that are capable of overturning the process.Constrained space and the ability of it is one of the most challenging tasks. Toprevail over from this issue we are proposing a Cryptographic LightweightEncryption Algorithm with Dimensionality Reduction in Edge Computing. Thet-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. Thethree dimensional image data obtained from the system, which are connected withit, are dimensionally reduced, and then lightweight encryption algorithm isemployed. Hence, the security backlog can be solved effectively using thismethod.