Flocculation of flotation tailings in presence of silicate gel and polymer

Flotation tailings were successfully flocculated in the presence of cationic polyacrylamide and silica gel.The effects of various parameters such as polymer weight,charge density,and pH on the rate of flocculation were also investigated in the current study.The flocculation mechanism of the flocculant on tailings was investigated using zeta potential and Fourier transform infrared(FTIR)measurements.The results obtained reveal that 1)sodium silicate gel,used as a binder for the consolidation of tailings form primary flocs,acts as an anchor and the adsorption of polymer flocculant on these anchors results in the formation of larger flocs and,consequently,enhanced settling rate;2)flocculation in the presence of silica gel and polymer has a faster settling rate than single-polymer flocculation owing to the mechanisms of charge neutralization and bridging as identified using zeta potential and FTIR measurements.A pilot level study was conducted to investigate the influence of processed water on the flotation of scheelite.The results show that the proposed tailing disposal method could improve scheelite recovery by 2%(approximately)and could reduce the daily operation costs of the plant by approximately 108.57 USD.

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication systems.Existing research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy consumption.However,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the network.The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high availability.We propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS attacks.Given a number of input parameters,our model has two distinct capabilities.First,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions.Second,it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks.The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters.Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

It is necessary to know the status of adhesion conditions between wheel and rail for efficient accelerating and decelerating of railroad vehicle.The proper estimation of adhesion conditions and their real-time implementation is considered a challenge for scholars.In this paper,the development of simulation model of extended Kalman filter(EKF)in MATLAB/Simulink is presented to estimate various railway wheelset parameters in different contact conditions of track.Due to concurrent in nature,the Xilinx®System-on-Chip Zynq Field Programmable Gate Array(FPGA)device is chosen to check the onboard estimation ofwheel-rail interaction parameters by using the National Instruments(NI)myRIO®development board.The NImyRIO®development board is flexible to deal with nonlinearities,uncertain changes,and fastchanging dynamics in real-time occurring in wheel-rail contact conditions during vehicle operation.The simulated dataset of the railway nonlinear wheelsetmodel is tested on FPGA-based EKF with different track conditions and with accelerating and decelerating operations of the vehicle.The proposed model-based estimation of railway wheelset parameters is synthesized on FPGA and its simulation is carried out for functional verification on FPGA.The obtained simulation results are aligned with the simulation results obtained through MATLAB.To the best of our knowledge,this is the first time study that presents the implementation of a model-based estimation of railway wheelset parameters on FPGA and its functional verification.The functional behavior of the FPGA-based estimator shows that these results are the addition of current knowledge in the field of the railway.

AI-Enabled Grouping Bridgehead to Secure Penetration Topics of Metaverse

With the advent of the big data era,security issues in the context of artificial intelligence(AI)and data analysis are attracting research attention.In the metaverse,which will become a virtual asset in the future,users’communication,movement with characters,text elements,etc.,are required to integrate the real and virtual.However,they can be exposed to threats.Particularly,various hacker threats exist.For example,users’assets are exposed through notices and mail alerts regularly sent to users by operators.In the future,hacker threats will increase mainly due to naturally anonymous texts.Therefore,it is necessary to use the natural language processing technology of artificial intelligence,especially term frequency-inverse document frequency,word2vec,gated recurrent unit,recurrent neural network,and long-short term memory.Additionally,several application versions are used.Currently,research on tasks and performance for algorithm application is underway.We propose a grouping algorithm that focuses on securing various bridgehead strategies to secure topics for security and safety within the metaverse.The algorithm comprises three modules:extracting topics from attacks,managing dimensions,and performing grouping.Consequently,we create 24 topic-based models.Assuming normal and spam mail attacks to verify our algorithm,the accuracy of the previous application version was increased by∼0.4%-1.5%.

Embedded System Development for Detection of Railway Track Surface Deformation Using Contour Feature Algorithm

Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition monitoring system is essential to avoid accidents and heavy losses.Generally,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment.Therefore,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway track.The proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition process.The contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy regions.It works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black color.We have used the multiprocessing technique to overcome the massive processing and memory issues.This embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/h.The functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faults.An unhealthy frame’s onsite detection processing time was recorded at approximately 25.6ms.The proposed system can synchronize the acceleration data on specific railway track deformation.The proposed novel embedded system may be beneficial for detecting faults to overcome the conventional manual railway track condition monitoring,which is still being practiced in various developing or underdeveloped countries.

Experimental studies and artificial neural network modeling of surface tension of aqueous sodium L-prolinate solutions and piperazine blends

The surface tension study is very crucial for the design of CO2 gas absorption contacting equipment. The significance of the surface tension has been increasing due to its consideration in various technological fields. This property influences the mass transfer and hydrodynamics of gas absorption systems, mainly multiphase systems, in which the interface between gas and liquid exists. Therefore, in this study, surface tension of aqueous solutions of sodium L-prolinate(SP) and piperazine(PZ) blends were measured at ten different temperatures from(298.15 to 343.15) K. The SP mass fractions were 0.10, 0.20, and 0.30;while the mass fractions of PZ were 0.02 and 0.05. The experimental results showed that the surface tension increase with increasing the mass fractions of SP and PZ in aqueous blends, and decrease linearly with rising temperature. The experimental data of surface tension were correlated by two empirical correlations as a function of temperature and mass fractions for estimating the predicted data using the optimized correlation coefficients. Moreover, the modeling of surface tension data was carried out using Artificial Neural Network(ANN) approach. The results obtianed from ANN modeling were compared with applied empirical correlation. It was found that the ANN approach outperformed the empirical correlation used in this study. Besides, a quantitative analysis of variation(ANOVA) was performed in order to determine the significance of data. The surface tension of aqueous SP and SP + PZ was also compared with various conventional solvents.

A Novel Method Based on UNET for Bearing Fault Diagnosis

Reliability of rotating machines is highly dependent on the smooth rolling of bearings.Thus,it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach.In the recent past,Deep Learning(DL)has become applicable in condition monitoring of rotating machines owing to its performance.This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images.The proposed method is the UNET model that is a recent development in DL models.The model is applied to the 2D vibration images obtained by transforming normalized amplitudes of the time-series vibration data samples into the corresponding vibration images.The UNET model performs pixel-level feature learning using the vibration images owing to its unique architecture.The results demonstrate that the model can perform dense predictions without any loss of label information,generally caused by the sliding window labelling method.The comparative analysis with other DL models confirmed the superiority of the UNET model which has achieved maximum accuracy of 98.91%and F1-Score of 99%.

OTT Messages Modeling and Classification Based on Recurrent Neural Networks

A vast amount of information has been produced in recent years,which brings a huge challenge to information management.The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages.In this paper,we propose a nine-rectangle-grid information model according to the information value and privacy,and then present information use policies based on the rough set theory.Recurrent neural networks were employed to classify OTT messages.The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages,ending with a reliable trained classification model.Experimental results showed that the proposed method yielded an accurate classification performance and hence can be used for effective distribution and control of OTT messages.

Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

This investigative study is focused on the impact of wavelet on traditional forecasting time-series models,which significantly shows the usage of wavelet algorithms.Wavelet Decomposition(WD)algorithm has been combined with various traditional forecasting time-series models,such as Least Square Support Vector Machine(LSSVM),Artificial Neural Network(ANN)and Multivariate Adaptive Regression Splines(MARS)and their effects are examined in terms of the statistical estimations.The WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters,which has yielded tremendous constructive outcomes.Further,it is observed that the wavelet combined models are classy compared to the various time series models in terms of performance basis.Therefore,combining wavelet forecasting models has yielded much better results.

NLP-Based Subject with Emotions Joint Analytics for Epidemic Articles

have been focused on addressing the Covid-19 pandemic;for example,governments have implemented countermeasures,such as quarantining,pushing vaccine shots to minimize local spread,investigating and analyzing the virus’characteristics,and conducting epidemiological investigations through patient management and tracers.Therefore,researchers worldwide require funding to achieve these goals.Furthermore,there is a need for documentation to investigate and trace disease characteristics.However,it is time consuming and resource intensive to work with documents comprising many types of unstructured data.Therefore,in this study,natural language processing technology is used to automatically classify these documents.Currently used statistical methods include data cleansing,query modification,sentiment analysis,and clustering.However,owing to limitations with respect to the data,it is necessary to understand how to perform data analysis suitable for medical documents.To solve this problem,this study proposes a robust in-depth mixed with subject and emotion model comprising three modules.The first is a subject and non-linear emotional module,which extracts topics from the data and supplements them with emotional figures.The second is a subject with singular value decomposition in the emotion model,which is a dimensional decomposition module that uses subject analysis and an emotion model.The third involves embedding with singular value decomposition using an emotion module,which is a dimensional decomposition method that uses emotion learning.The accuracy and other model measurements,such as the F1,area under the curve,and recall are evaluated based on an article on Middle East respiratory syndrome.A high F1 score of approximately 91%is achieved.The proposed joint analysis method is expected to provide a better synergistic effect in the dataset.

The surveillance of arsenic levels in the drinking water of primary schools and the assessment of the potential cancer-related health risks of children in Multan, Pakistan

To investigate the levels of arsenic(As)in the groundwater and drinking water of primary schools and their potential impact on human health is main objective of this study.To assess risk,the following parameters i.e.,carcinogenic indices(CI),hazardous indexes(HI),carcinogenic risk(CR),and hazardous quotient(HQ)have been measured for both dermal and oral arsenic exposure.This study has been focused on primary schools located in four tehsils of Multan,where water samples were collected.Arsenic levels,with concentrations ranging from 3.31 to 191 mg/l,exceeding the safe limit set by the World Health Organization(WHO)have been detected in 99.9%of the samples.The results show that arsenic content ranged from 3.31 to 191 mg/l,with 99.9%of the samples exceeding the World Health Organization's safe limit of 10 mg/l.The CR and CI exceeded the USEPA limit of 10–6 in all four tehsils ranged from 2.6×10^(-6) to 9.4×10^(-6) and 0.00023 to 0.0009 respectively in both dermal and oral cases.,Multan Sadar(1.51,1.39)and Multan City(1.76,1.71)exhibited HI and HQ values that surpassed the threshold of 1.0,signaling a significant likelihood of chronic and cancer-causing health consequences for individuals who are exposed to polluted water causing health.The findings suggest that the groundwater in the Multan district poses a significant health risk to humans due to high levels of arsenic contamination.This contamination is associated with an increased risk of mortality from internal cancers(including liver,kidney,lung,and bladder),as well as a higher HI&CI incidence of skin cancer.Furthermore,pregnancy outcomes are affected,with associations found between arsenic exposure and lower birth weight,and infant mortality.The researchers propose that water supply organizations and educational institutions promptly take measures to ensure that the affected areas have access to arsenic-free drinking water for the well-being of students and residents.

Effect mechanism of nonane-1,1-bisphosphonic acid as an alternative collector in monazite flotation:Experimental and calculational studies

Monazite((Ce,La)PO_(4))is one of the major types of light rare earth minerals from which the light rare earth elements cerium(Ce)and lanthanum(La)are economically extracted.Flotation is extensively used to recover fine-grained monazite.Sodium oleate(NaOL)is considered as the collector with strong collecting ability for monazite flotation.However,this study shows that its collecting ability is still limited.In this paper,a phosphonic acid,nonane-1,1-bisphosphonic acid(C9-BPA),was employed as the novel collector in place of NaOL.Flotation experiments show that even when the C9-BPA dosage is less than one-fifth of the NaOL dosage,the monazite recove ry using C9-BPA as the collector is approximately 22 wt%higher than that using NaOL.The mechanism by which C9-BPA adsorbs on monazite was investigated using zeta potential,infrared(IR)spectroscopy and X-ray photoelectron spectroscopy(XPS)measurements as well as first-principles calculations.Zeta potential measurements show a more significant decrease in the zeta potentials of monazite after the addition of C9-BPA compared to those after the addition of NaOL.For C9-BPA-treated monazite,the characteristic peaks of C9-BPA are observed in the IR and C 1 s XPS spectrum,whereas for monazite treated by NaOL,no characteristic peak of NaOL was observed.Experimental results show that C9-BPA has a stronger affinity towards the monazite surface than NaOL as confirmed by the higher adsorption energy of CP-BPA on the monazite surface(-204.22 kJ/mol)than NaOL(-48.48 kJ/mol).This study demonstrates an extensive application value and prospect of C9-BPA in monazite flotation and helps design novel collectors with strong collecting ability for monazite flotation.