Novel polybenzimidazole/graphitic carbon nitride nanosheets composite membrane for the application of acid-alkaline amphoteric water electrolysis

It is a great challenge to develop membrane materials with high performance and long durability for acidalkaline amphoteric water electrolysis.Hence,the graphitic carbon nitride(g-C_(3)N_(4))nanosheets were compounded with the(2,2'-m-phenylene)-5,5'-benzimidazole(m-PBI)matrix for the preparation of m-PBI/g-C_(3)N_(4) composite membranes.The synthesis of g-C_(3)N_(4) nanosheets and m-PBI matrix have been confirmed by X-ray diffraction(XRD),scanning electron microscopy(SEM),transmission electron microscoy(TEM)and ^(1)H nuclear magnetic resonance spectra(^(1)H NMR),respectively.The fourier transform infrared spectroscopy(FT-IR)and SEM of the composite membranes showed the g-C_(3)N_(4) nanosheets were well dispersed in the m-PBI/g-C_(3)N_(4) composite membrane.The mechanical properties test exhibited the good mechanical strength,and the TGA curves of m-PBI showed the high thermal stability of composite membranes.Besides,the m-PBI/g-C_(3)N_(4) composite membrane showed excellent proton and hydroxide ion conductivity,which was higher than pure m-PBI and Nafion 115 membrane.The acid-alkaline amphoteric water electrolysis test showed m-PBI/1%g-C_(3)N_(4) composite membrane has the best performance with a current density of 800 mA cm^(-2) at cell voltage of 1.98 V at 20℃.It showed that m-PBI/g-C_(3)N_(4) composite membrane has a good application prospect for acid-alkaline amphoteric water electrolysis.

Research in Computer Viruses and Worms

Viruses and worms have become so common that most computer users now accept them as a fact of life.This paper introduces the definitions and difference of the computer viruses and worms.Some main research problems about the computer viruses and worms in recent years are also summarized and discussed in detail.Finally the developing trend of the computer virus and worms is proposed.

Towards Attaining Reliable and Efficient Green Cloud Computing Using Micro-Smart Grids to Power Internet Data Center

Energy generation and consumption are the main aspects of social life due to the fact that modern people’s necessity for energy is a crucial ingredient for existence. Therefore, energy efficiency is regarded as the best economical approach to provide safer and affordable energy for both utilities and consumers, through the enhancement of energy security and reduction of energy emissions. One of the problems of cloud computing service providers is the high rise in the cost of energy, efficiency together with carbon emission with regards to the running of their internet data centres (IDCs). In order to mitigate these issues, smart micro-grid was found to be suitable in increasing the energy efficiency, sustainability together with the reliability of electrical services for the IDCs. Therefore, this paper presents idea on how smart micro-grids can bring down the disturbing cost of energy, carbon emission by the IDCs with some level of energy efficiency all in an effort to attain green cloud computing services from the service providers. In specific term, we aim at achieving green information and communication technology (ICT) in the field of cloud computing in relations to energy efficiency, cost-effectiveness and carbon emission reduction from cloud data center’s perspective.

An Artificial Neural Network (ANN) Model for Predicting Water Absorption of Nanoclay-Epoxy Composites

Glass fiber reinforced epoxy (GFRE) composite materials are prone to suffer from water absorption due to their heterogeneous structure. The main process governing water absorption is diffusion of water molecules through the epoxy matrix. However, hydrolytic degradation may also take place during components service life specially due high temperatures. In order to mitigate the effects of the water diffusive processes in the deterioration of in-service behavior of epoxy matrix composites, the use of chemically modified nanoclays as an additive has been proposed and studied in previous works [1]. In this work, an Artificial Neural Network (ANN) model was developed for better understanding and predicting the influence of modified and unmodified bentonite addition on the water absorption behavior of epoxy-anhydride systems. An excellent correlation between model and experimental data was found. The ANN model allowed the identification of critical points like the precise temperature at which a particular system’s water uptake goes beyond a predefined threshold, or which system will resist an immersion longer than a particular time.

Antioxidant Activity Evaluation in a Series of Heterocyclic Compounds Derived from 1,8-Diaminonaphthalene

From (2,3-dihydro-1H-perimidin-2-yl)-phenyl, the substitution of OH group in ortho or para position on the phenyl ring, allows us to synthesize the studied compounds. These three compounds have been characterized by conventional spectroscopic methods (NMR and MS). The interest of this work is to review the antioxidant activity of our compounds. The antioxidant activity screening carried out according to FRAP and DPPH methods revealed significant anti-free radical properties for compounds 1 and 2 even at low concentrations. In contrast to the compound 2, compound 3 for which the OH group is substituted in para position has the lowest activity in both cases. Therefore the para position seems to be the least sensitive position to increase the antioxidant activity of this pharmacophore.

Empirical Determination of the Pion Mass Distribution

Existing pion+nucleus Drell-Yan and electron+pion scattering data are used to develop ensembles of modelindependent representations of the pion generalized parton distribution(GPD).Therewith,one arrives at a datadriven prediction for the pion mass distribution form factor,θ_(2)^(π).Compared with the pion elastic electromagnetic form factor,θ_(2)^(π)is harder:the ratio of the radii derived from these two form factors is r_(π)^(θ2)/r_(π)= 0.79(3).Our datadriven predictions for the pion GPD,related form factors and distributions should serve as valuable constraints on theories of pion structure.

All-Orders Evolution of Parton Distributions:Principle,Practice,and Predictions

Parton distribution functions(PDFs)are defining expressions of hadron structure.Exploiting the role of effective charges in quantum chromodynamics,an algebraic scheme is described which,given any hadron’s valence parton PDFs at the hadron scale,delivers predictions for all its PDFs(unpolarized and polarized)at any higher scale.The scheme delivers results that are largely independent of both the value of the hadron scale and the pointwise form of the charge;and,inter alia,enables derivation of a model-independent identity that relates the strength of the proton’s gluon helicity PDF,ΔG_(p)ζ,to that of the analogous singlet polarized quark PDF and valence quark momentum fraction.Using available data fits and theory predictions,the identity yieldsΔG_(p)(ζC=√3 GeV)=1.48(10).It furthermore entails that the measurable quark helicity contribution to the proton spin is a_(op)^(ζC)=0.32(3),thereby reconciling contemporary experiment and theory.

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Due to the rapid increase in urbanization and population,crowd gatherings are frequently observed in the form of concerts,political,and religious meetings.HAJJ is one of the well-known crowding events that takes place every year in Makkah,Saudi Arabia.Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence(AI)applications.The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification(SSODTL-CD2C)model.The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities.To attain this,SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm(OSSA)with EfficientNet model to derive the feature vectors.At the same time,Stacked Sparse Auto Encoder(SSAE)model is utilized for the classification of crowd densities.Finally,SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism.The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities.The obtained results demonstrated that the proposed SSODTLCD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%.So,the proposed method will be highly helpful in managing HAJJ and other crowded events.

Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion traffic.The vast network traffic volume also complicates most network monitoring systems and algorithms.Several intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these incidents.This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms.The stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression algorithm.Evaluated using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,respectively.The obtained results demonstrate improvements in other measurement criteria.

Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their traffic.Newresearch directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular networks.For the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph theory.Any number that can be uniquely calculated by a graph is known as a graph invariant.During the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation analysis.In any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network graph.This paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical networks.Since K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or networks.As the hexagonal networks are used in mobile base stations in layered,forms called honeycomb.The review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced forms.The deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection networks.The results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

Energy Efficient Unequal Fault Tolerance Clustering Approach

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network lifetime.For saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor Networks.Because of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure.For increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor nodes.An Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster Head.The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS.Thus,the MCH overhead reduces.During the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

Robust Deep Transfer Learning Based Object Detection and Tracking Approach

At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

Manipulating Skyrmion Motion on a Nanotrack with Varied Material Parameters and Tilted Spin Currents

Magnetic skyrmions are topological quasiparticles with nanoscale size and high mobility,which have potential applications in information storage and spintronic devices.The manipulation of skyrmion’s dynamics in the track is an important topic due to the skyrmion Hall effect,which can deviate the skyrmions from the preferred direction.We propose a new model based on the ferromagnetic skyrmion,where the skyrmion velocity can be well controlled by adjusting the direction of the current.Using this design,we can avoid the annihilation of the skyrmion induced by the skyrmion Hall effect,which is confirmed by our micromagnetic simulation based on Mumax^(3).In the meantime,we increase the average velocity of the skyrmion by varying the intrinsic material parameters in the track,where the simulations agree well with our analytical results based on the Thiele equation.Finally,we give a phase diagram of the output of the skyrmion in the T-type track,which provides some practical ways for design of logic gates by manipulating crystalline anisotropy through the electrical control.

Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the client.Clients can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and value.One of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’projects.The goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO clients.The projects belong to OSMO vendors,having offices in developing countries while providing services to developed countries.In the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed model.The proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden layers.The results express that the suggested model has gained a notable recognition rate in comparison to any previous studies.The current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

Proposed Privacy Preservation Technique for Color Medical Images

Nowadays,the security of images or information is very important.This paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image security.First,the secret medical image is encrypted using Advanced Encryption Standard(AES)algorithm.Then,the secret report of the patient is embedded into the encrypted secret medical image with the Least Significant Bit(LSB)watermarking algorithm.After that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)algorithm.Afterwards,the stego-image obtained is split into 16 parts.Finally,it is sent to the receiver.We adopt this strategy to send the secret medical image and report over a network securely.The proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to confirm the matching between the secret medical image before and after transmission.Simulation results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

Adaptive energy-preserving algorithms for guiding center system

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized plasmas.The adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving algorithms.These new adaptive algorithms are second order,and their algebraic order is carefully studied.Numerical results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Characteristics and in-hospital mortality of elderly patients with heart failure in Spanish hospitals

BACKGROUND The prevalence of heart failure(HF)increases with age,and it is one of the leading causes of hospitalization and death in older patients.However,there are little data on in-hospital mortality in patients with HF≥75 years in Spain.METHODS A retrospective analysis of the Spanish Minimum Basic Data Set was performed,including all HF episodes discharged from public hospitals in Spain between 2016 and 2019.Coding was performed using the International Classification of Diseases,10th Revision.Patients≥75 years with HF as the principal diagnosis were selected.We calculated:(1)the crude in-hospital mortality rate and its distribution according to age and sex;(2)the risk-standardized in-hospital mortality ratio;and(3)the association between in-hospital mortality and the availability of an intensive cardiac care unit(ICCU)in the hospital.RESULTS We included 354,792 HF episodes of patients over 75 years.The mean age was 85.2±5.5 years,and 59.2%of patients were women.The most frequent comorbidities were renal failure(46.1%),diabetes mellitus(35.5%),valvular disease(33.9%),cardiorespiratory failure(29.8%),and hypertension(26.9%).In-hospital mortality was 12.7%,and increased with age[odds ratio(OR)=1.07,95%CI:1.07–1.07,P<0.001]and was lower in women(OR=0.96,95%CI:0.92–0.97,P<0.001).The main predictors of mortality were the presence of cardiogenic shock(OR=19.5,95%CI:16.8–22.7,P<0.001),stroke(OR=3.5,95%CI:3.0–4.0,P<0.001)and advanced cancer(OR=2.6,95%CI:2.5–2.8,P<0.001).In hospitals with ICCU,the in-hospital risk-adjusted mortality tended to be lower(OR=0.85,95%CI:0.72–1.00,P=0.053).CONCLUSIONS In-hospital mortality in patients with HF≥75 years between 2016 and 2019 was 12.7%,higher in males and elderly patients.The main predictors of mortality were cardiogenic shock,stroke,and advanced cancer.There was a trend toward lower mortality in centers with an ICCU.

Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.