Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

RETRACTED: Nuclear Structure Study of Odd-Odd Yttrium Nuclei within Interacting-Boson Fermi-Fermion Model (IBFFM)

Short Retraction NoticeThe paper does not meet the standards of "Journal of Applied Mathematics and Physics". This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.Editor guiding this retraction: Prof. Wen-Xiu Ma (EiC of JAMP)The full retraction notice in PDF is preceding the original paper, which is marked "RETRACTED".

A Pioneering Approach to the Synthesis of Silver Nanoparticles

Our research introduces a groundbreaking chemical reduction method for synthesizing silver nanoparticles, marking a significant advancement in the field. The nanoparticles were meticulously characterized using various techniques, including optical analysis, structural analysis, transmission electron microscopy (TEM), and field-emission scanning electron microscope (FESEM). This thorough process instills confidence in the accuracy of our findings. The results unveiled that the silver nanoparticles had a diameter of less than 20 nm, a finding of great importance. The absorption spectrum decreased in the peak wavelength range (405 - 394 mm) with increasing concentrations of Ag nanoparticles in the range (1 - 5%). The XRD results indicated a cubic crystal structure for silver nanoparticles with the lattice constant (a = 4.0855 Å), and Miller indices were (111), (002), (002), and (113). The simulation on the XRD pattern showed a face center cubic phase with space group Fm-3m, providing valuable insights into the structure of the nanoparticles.

Prediction of seismic-induced bending moment and lateral displacement in closed and open-ended pipe piles:A genetic programming approach

Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment,typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction.Despite recent advancements in machine learning techniques,there is a persistent need to establish data-driven models that can predict these parameters without using numerical simulations due to the difficulties in conducting correct numerical simulations and the need for constitutive modelling parameters that are not readily available.This research presents novel lateral displacement and bending moment predictive models for closed and open-ended pipe piles,employing a Genetic Programming(GP)approach.Utilizing a soil dataset extracted from existing literature,comprising 392 data points for both pile types embedded in cohesionless soil and subjected to earthquake loading,the study intentionally limited input parameters to three features to enhance model simplicity:Standard Penetration Test(SPT)corrected blow count(N60),Peak Ground Acceleration(PGA),and pile slenderness ratio(L/D).Model performance was assessed via coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE),with R^(2) values ranging from 0.95 to 0.99 for the training set,and from 0.92 to 0.98 for the testing set,which indicate of high accuracy of prediction.Finally,the study concludes with a sensitivity analysis,evaluating the influence of each input parameter across different pile types.

Effect of elevated temperature and silica sand particle size on minimum fluidization velocity in an atmospheric bubbling fluidized bed

The impact of temperature and particle size on minimumfluidizing velocity was studied and analyzed in a small pilot scale of bubbling fluidized bed reactor.This study was devoted to providing some data about fluidization to the literature under high temperature conditions.The experiments were carried out to evaluate the minimum fluidizing velocity over a vast range of temperature levels from 20℃ to 850℃ using silica sand with a particle size of 300-425μm,425-500μm,500-600μm,and 600-710μm.Furthermore,the variation in the minimumfluidized voidage was determined experimentally at the same conditions.The experimental data revealed that the Umf directly varied with particle size and inversely with temperature,whileεmf increases slightly with temperature based on the measurements of height at incipient fluidization.However,for all particle sizes used in this test,temperatures above 700℃ has a marginal effect on Umf.The results were compared with many empirical equations,and it was found that the experimental result is still in an acceptable range of empirical equations used.In which,our findings are not well predicted by the widely accepted correlations reported in the literature.Therefore,a new predicted equation has been developed that also accounts for the affecting of mean particle size in addition to other parameters.A good mean relative deviation of 5.473% between the experimental data and the predicted values was estimated from the correlation of the effective dimensionless group.Furthermore,the experimental work revealed that the minimum fluidizing velocity was not affected by the height of the bed even at high temperature.

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

In the present industrial revolution era,the industrial mechanical system becomes incessantly highly intelligent and composite.So,it is necessary to develop data-driven and monitoring approaches for achieving quick,trustable,and high-quality analysis in an automated way.Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery.The advent of deep learning(DL)methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals.This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network(IIFD-SOIR)Model.The proposed model operates on three major processes namely signal representation,feature extraction,and classification.The proposed model uses a Continuous Wavelet Transform(CWT)is for preprocessed representation of the original vibration signal.In addition,Inception with ResNet v2 based feature extraction model is applied to generate high-level features.Besides,the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer.Finally,a multilayer perceptron(MLP)is applied as a classification technique to diagnose the faults proficiently.Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset.The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6%and 99.64%on the applied gearbox dataset and bearing dataset.The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.

Oilfield produced water treatment in internal-loop airlift reactor using electrocoagulation/flotation technique

Oilfield produced water is large quantities of salty water trapped in underground formations and subsisted under high temperatures and pressures that are brought to the surface along with oil during production. Produced water(PW) contains a lot of pollutants such as hydrocarbons and metals, this water must be treated before disposal. Therefore, different techniques are being used to treat produced water. Electrocoagulation is an efficient treatment technique involving the dissolution of anodes and formation of electro-coagulants, while the simultaneous generation of H_2 bubbles at the cathode leads to the pollutant removal by flotation. Electrocoagulation(EC)method is one of the most promising and widely used processes to treat oilfield produced water. In the present work, a conventional internal-loop(draught tube) airlift reactor was utilized as electrocoagulation/flotation cell for PW treatment by inserting two aluminum electrodes in the riser section of the airlift reactor. The EC airlift reactor was operated in a batch mode for the liquid phase. Different experimental parameters were studied on the oil and turbidity removal efficiencies such as current density, initial pH, electrocoagulation time, and air injection.The experimental results showed that mixing of the oil droplets in the PW was accomplished using only the liquid recirculation resulted by H_2 microbubbles generated by EC process which enhanced the oil removal. The experimental results further showed that the EC time required achieving ≥ 90% oil removal efficiency decreases from 46 to 15 min when operating current density increases from 6.8 to 45.5 mA·cm^(-2). This reactor type was found to be highly efficient and less energy consuming compared to conventional existing electrochemical cells which used mechanical agitation.

Packet Drop Battling Mechanism for Energy Aware Detection in Wireless Networks

Network security and energy consumption are deemed to be two important components of wireless and mobile ad hoc networks(WMANets).There are various routing attacks which harm Ad Hoc networks.This is because of the unsecure wireless communication,resource constrained capabilities and dynamic topology.In order to cope with these issues,Ad Hoc On-Demand Distance Vector(AODV)routing protocol can be used to remain the normal networks functionality and to adjust data transmission by defending the networks against black hole attacks.The proposed system,in this work,identifies the optimal route from sender to collector,prioritizing the number of jumps,the battery life,and security,which are fundamental prerequisites.Researches have proposed various plans for detecting the shortest route,as well as ensuring energy conversions and defense against threats and attacks.In this regard,the packet drop attack is one of the most destructive attack against WMANet communication and hence merits special attention.This type of attack may allow the attacker to take control of the attacked hubs,which may lost packets or transmitted information via a wrong route during the packets journey from a source hub to a target one.Hence,a new routing protocol method has been proposed in this study.It applies the concept of energy saving systems to conserve energy that is not required by the system.The proposed method for energy aware detection and prevention of packet drop attacks in mobile ad hoc networks is termed the Ad Hoc On-Demand and Distance Vector–Packet Drop Battling Mechanism(AODV–PDBM).

Optimization of Culture Conditions to Produce Phytase from <i>Aspergillus tubingensis SKA</i>

The effects of nutrients and physical conditions on phytase production were investigated with a recently isolated strain of Aspergillus tubingensis SKA under solid state fermentation on wheat bran. The nutrient factors investigated included carbon source, nitrogen source, phosphate source and concentration, metal ions (salts) and the physical parameters investigated included inoculum size, pH, temperature and fermentation duration. Our investigations revealed that optimal productivity of phytase was achieved using wheat bran supplemented with: 1.5% glucose. 0.5% (NH4)2SO4, 0.1% sodium phytate. Additionally, optimal physical conditions were 1 × 105 spore/g substrate, initial pH of 5.0, temperature of fermentation 30°C and fermentation duration of 96 h. Overall, a 34% improvement in phytase activity was achieved by using the optimal conditions.

Web Attack Detection Using the Input Validation Method:DPDA Theory

A major issue while building web applications is proper input validation and sanitization.Attackers can quickly exploit errors and vulnerabilities that lead to malicious behavior in web application validation operations.Attackers are rapidly improving their capabilities and technologies and now focus on exploiting vulnerabilities in web applications and compromising confidentiality.Cross-site scripting(XSS)and SQL injection attack(SQLIA)are attacks in which a hacker sends malicious inputs(cheat codes)to confuse a web application,to access or disable the application’s back-end without user awareness.In this paper,we explore the problem of detecting and removing bugs from both client-side and server-side code.A new idea that allows assault detection and prevention using the input validation mechanism is introduced.In addition,the project supports web security tests by providing easy-to-use and accurate models of vulnerability prediction and methods for validation.If these attributes imply a program statement that is vulnerable in an SQLIA,this can be evaluated and checked for a set of static code attributes.Additionally,we provide a script whitelisting interception layer built into the browser’s JavaScript engine,where the SQLIA is eventually detected and the XSS attack resolved using the method of input validation and script whitelisting under pushdown automatons.This framework was tested under a scenario of an SQL attack and XSS.It is demonstrated to offer an extensive improvement over the current framework.The framework’s main ability lies in the decrease of bogus positives.It has been demonstrated utilizing new methodologies,nevertheless giving unique access to sites dependent on the peculiarity score related to web demands.Our proposed input validation framework is shown to identify all anomalies and delivers better execution in contrast with the current program.

Synchronization Phenomena Investigation of a New Nonlinear Dynamical System 4D by Gardano’s and Lyapunov’s Methods

Synchronization is one of the most important characteristics of dynamic systems.For this paper,the authors obtained results for the nonlinear systems controller for the custom Synchronization of two 4D systems.The findings have allowed authors to develop two analytical approaches using the second Lyapunov(Lyp)method and the Gardanomethod.Since the Gardano method does not involve the development of special positive Lyp functions,it is very efficient and convenient to achieve excessive systemSYCR phenomena.Error is overcome by using Gardano and overcoming some problems in Lyp.Thus we get a great investigation into the convergence of error dynamics,the authors in this paper are interested in giving numerical simulations of the proposed model to clarify the results and check them,an important aspect that will be studied is Synchronization Complete hybrid SYCR and anti-Synchronization,by making use of the Lyapunov expansion analysis,a proposed control method is developed to determine the actual.The basic idea in the proposed way is to receive the evolution of between two methods.Finally,the present model has been applied and showing in a new attractor,and the obtained results are compared with other approximate results,and the nearly good coincidence was obtained.

A Formal Testing Model for Operating Room Control System Using Internet of Things

Technological advances in recent years have significantly changed the way an operating room works.This work aims to create a platformto solve the problems of operating room occupancy and prepare the rooms with an environment that is favorable for all operations.Using this system,a doctor can control all operation rooms,especially before an operation,and monitor their temperature and humidity to prepare for the operation.Also,in the event of a problem,an alert is sent to the nurse responsible for the room and medical stuff so that the problem can be resolved.The platformis tested using a Raspberry PI card and sensors.The sensors are connected to a cloud layer that collects and analyzes the temperature and humidity values obtained from the environment during an operation.The result of experimentations is visualized through a web application and an Android application.The platform also considers the security aspects such as authorization to access application functionalities for the Web and the mobile applications.We can also test and evaluate the system’s existing problems and vulnerabilities using the IEEE and owasp IoT standards.Finally,the proposed framework is extended with a model based testing technique that may be adopted for validating the security aspects.

Using Neural Networks to Predict Secondary Structure for Protein Folding

Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.

Effect of Poly-Alkylene-Glycol Quenchant on the Distortion, Hardness, and Microstructure of 65Mn Steel

Currently,the 65Mn steel is quenched mainly by oil media.Even though the lower cooling rate of oil compared to water reduces the hardness of steel post quenching,the deforming and cracking of parts are often minimized.On the other hand,the oil media also has the disadvantage of being ammable,creating smoke that adversely affects the media.The poly alkylene glycol(PAG)polymer quenchant is commonly used for quenching a variety of steels based on its advantages such as non-ammability and exible cooling rate subjected to varying concentration and stirring speed.This article examines the effect of PAG polymer quenching solution(with concentrations of 10%and 20%)on deformation,hardness,and microstructure of C-ring samples made of 65Mn steel.Furthermore,the performance of PAG polymer quenchant is also compared with those of two common quenching solutions:Water and oil.When cooling in water,the C-ring samples had the largest deformation and 2 times higher than the results obtained when a 10%PAG solution was used.In particular,similar levels of deformation on the C-ring samples were observed in both cases of 20%PAG solution and oil as the primary quenching media.Furthermore,the hardness level measured between the sampled parts quenched in the 20%PAG solution appeared to be more uniform than that obtained from the oil-quenched sample.The study of the microscopic structure of steel by optical microscopy combined with X-ray diffraction showed that the water hardened sample exhibited cracks and comprised of two phases,martensite and retained austenite.According to the results of Electron Back Scattering Diffraction(EBSD)analysis and backscattering electronic image(BSE),the content of austenite residue in the sample when the sample was cooled in PAG 10 and 20%solution was 3.21%and 4.73%,respectively and smaller than the measurements obtained from oil quenching solution.Thus,the 65Mn steel is cooled in 20%PAG solution for high hardness and more evenly distributed than when it is quenched in oil while still ensuring a small level of deformation.Therefore,the PAG 20%solution can completely replace oil as the main media used to quench the 65Mn steel.

Web Application Commercial Design for Financial Entities Based on Business Intelligence

Multiple customer data management has become a focus of attention in big organizations.Although much information is available,it does not translate into significant profitable value-added services.We present a design of a commercial web application based on business intelligence that generates information on social and financial behavior of clients in an organization;with the purpose of obtain additional information that allows to get more profits.This app will provide a broader perspective for making strategic decisions to increase profits and reduce internal investment costs.A case in point is the financial sector,a group of financial entities were used to make measurements and test them.A design to build a web application aimed at achieving a large and ambitious goal by means of defined tools reflecting clients’business needs is proposed.In this research,different techniques and technologies are explored,such as diagrams,frameworks,design,architecture,model entity-relationship,tables,equations,mental maps and development tools.Through the Personal Software Process methodology and with the help of information extraction,consolidation,and visualization,the implementation can be carried out.This article provides the importance of implementing business intelligence in an organization and expands on the steps needed for the implementation of this valuable technology.

Design and Implementation of Fingerprint Identification System Based on KNN Neural Network

Fingerprint identification and recognition are considered popular technique in many security and law enforcement applications. The aim of this paper is to present a proposed authentication system based on fingerprint as biometric type, which is capable of recognizing persons with high level of confidence and minimum error rate. The designed system is implemented using Matlab 2015b and tested on a set of fingerprint images gathered from 90 different persons with 8 samples for each using Futronic’s FS80 USB2.0 Fingerprint Scanner and the ftrScanApiEx.exe program. An efficient image enhancement algorithm is used to improve the clarity (contrast) of the ridge structures in a fingerprint. After that core point and candidate core points are extracted for each Fingerprint image and feature vector have been extracted for each point using filterbank_based algorithm. Also, for the matching the KNN neural network was used. In addition, the matching results were calculated and compared to other papers using some performance evaluation factors. A threshold has been proposed and used to provide the rejection for the fingerprint images that does not belong to the database and the experimental results show that the KNN technique have a recognition rate equal to 93.9683% in a threshold equal to 70%.

Breeding and Selection of Some Lines of Bread Wheat for Salt Tolerance

The aim of this experimental work is to select wheat progenies tolerant high levels of salinity. Studies were conducted to determine the realized response in salt tolerance of some selected progeny （F3） through one cycle of screening and selection and the extent of variability for salt tolerance among selected progeny （F3） of wheat. All the selected progeny （F3） derived from （F2） populations after exposure are to high salinity level, were tested for salt tolerance during all growth stages in sand cultures salinized with 0, 100, 175 mM NaCI as compared with the unselected progeny （F2）. Results showed that the selected progeny （F3） were in all cases except one, superior in yield at maturity and in most cases significantly so, both at 100 and 175 mM NaCl, to the yield of the unselected progeny （F2） controls. In this study, some selected progeny exhibited more than twice the seed yield compared with that obtained from unselected progeny at 175 mM NaCl, and their seed yield was also significantly higher than that at 0 mM NaCl. Results also indicated that the highest value percentage of realized response in salt tolerance was in （F3AS1） and （F3AS2） at 175 mM NaCI. It is important to bear in mind that the significant improvement in selected progeny was achieved just in a single cycle of selection. There is then a strong possibility that further increases in tolerance may be obtained through further cycles of selection till reaching F6 or F7 where would show genetics stability.

Payload Capacity Scheme for Quran Text Watermarking Based on Vowels with Kashida

The most sensitive Arabic text available online is the digital Holy Quran.This sacred Islamic religious book is recited by all Muslims worldwide including non-Arabs as part of their worship needs.Thus,it should be protected from any kind of tampering to keep its invaluable meaning intact.Different characteristics of Arabic letters like the vowels(),Kashida(extended letters),and other symbols in the Holy Quran must be secured from alterations.The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio(PSNR)and Embedding Ratio(ER).A watermarking technique with enhanced attributes must,therefore,be designed for the Quran’s text using Arabic vowels with kashida.The gap addressed by this paper is to improve the security of Arabic text in the Holy Quran by using vowels with kashida.The purpose of this paper is to enhance the Quran text watermarking scheme based on a reversing technique.The methodology consists of four phases:The first phase is a pre-processing followed by the second phase-the embedding process phase—which will hide the data after the vowels.That is,if the secret bit is“1”,then the kashida is inserted;however,the kashida is not inserted if the bit is“0”.The third phase is the extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR(for the imperceptibility)and ER(for the capacity).The experimental results show that the proposed method of imperceptibility insertion is also optimized with the help of a reversing algorithm.The proposed strategy obtains a 90.5%capacity.Furthermore,the proposed algorithm attained 66.1%which is referred to as imperceptibility.

A New Medical Image Enhancement Algorithm Based on Fractional Calculus

The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images.The captured images may present with low contrast and low visibility,which might inuence the accuracy of the diagnosis process.To overcome this problem,this paper presents a new fractional integral entropy(FITE)that estimates the unforeseeable probabilities of image pixels,posing as the main contribution of the paper.The proposed model dynamically enhances the image based on the image contents.The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’probability.Initially,the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image.Next,the contrast of the image is then adjusted to enhance the regions with low visibility.Finally,the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image.Tests were conducted on brain MRI,lungs CT,and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality.The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores.Overall,this model improves the details of brain MRI,lungs CT,and kidney MRI scans,and could therefore potentially help the medical staff during the diagnosis process.

Remote Sensing Monitoring Method Based on BDS-Based Maritime Joint Positioning Model

Complicated sea conditions have a serious impact on ship navigation safety and even maritime accidents.Accordingly,this paper proposes a remote sensing monitoring method based on the Beidou Navigation Satellite System(BDS)maritime joint positioning model.This method is mainly based on the BDS and multiple Global Navigation Satellite Systems(GNSS)to build a data fusion model,which can capture more steady positioning,navigation,and timing(PNT)data.Compared with the current Global Positioning System(GPS)and Global Navigation Satellite System(GLONASS)mandatory used by the International Maritime Organization(IMO),this model has the characteristics of more accurate positioning data and stronger stability.The static and dynamic measurement show that such a model works for maritime ships and maritime engineering.Combined with the Ship’s Automatic Identification System(AIS)and Geographic Information System(GIS),a BDS-based remote sensing monitoring method can cover the world,serve maritime ships and construct maritime engineering.