Quintessence anisotropic stellar models in quadratic and Born-Infeld modified teleparallel Rastall gravity

This study aims to discuss anisotropic solutions that are spherically symmetric in the quintessence field,which describe compact stellar objects in the modified Rastall teleparallel theory of gravity.To achieve this goal,the Krori and Barua arrangement for spherically symmetric components of the line element is incorporated.We explore the field equations by selecting appropriate off-diagonal tetrad fields.Born-Infeld function of torsion f(T)=β√λT+1-1 and power law form h(T)=δTn are used.The Born-Infeld gravity was the first modified teleparallel gravity to discuss inflation.We use the linear equation of state pr=ξρto separate the quintessence density.After obtaining the field equations,we investigate different physical parameters that demonstrate the stability and physical acceptability of the stellar models.We use observational data,such as the mass and radius of the compact star candidates PSRJ 1416-2230,Cen X-3,&4U 1820-30,to ensure the physical plausibility of our findings.

基于连通区域离散度的断层识别

针对地震剖面数据中有效层位信息与噪声混合分布,传统方法难以对地震断层进行识别的问题,提出了一种基于连通区域离散度的地震断层识别方法.首先,基于八邻域连通对地震剖面二值图像中的连通区域进行标注并提取每一个连通区域的离散度等数字特征;然后,基于噪声和地震层位各自所属连通区域横向长度和离散度的不同对噪声进行去除;最后,利用地震层位所属连通区域的横向端点信息对地震断层进行识别并标注.通过实验将所提方法与传统方法进行了对比,实验结果验证了所提方法的有效性和实用性.

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment.To deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in COVID-19.The expert system has two input variables in layer 1,and seven input variables in layer 2.In layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are studied.Finally,advanced lab tests are conducted to identify the actual current status of the disease.The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining.MATLAB-2019a tool is used to conduct the simulation.The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan.

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.

Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidden layers to enhance the predictive performance of CGMKL significantly. The experimental results onvarious data sets demonstrate the superior performance of the E-CGMKLalgorithm compared to other competing methods including the benchmarkCGMKL.

Intelligent Dynamic Gesture Recognition Using CNN Empowered by Edit Distance

Human activity detection and recognition is a challenging task.Video surveillance can benefit greatly by advances in Internet of Things(IoT)and cloud computing.Artificial intelligence IoT(AIoT)based devices form the basis of a smart city.The research presents Intelligent dynamic gesture recognition(IDGR)using a Convolutional neural network(CNN)empowered by edit distance for video recognition.The proposed system has been evaluated using AIoT enabled devices for static and dynamic gestures of Pakistani sign language(PSL).However,the proposed methodology can work efficiently for any type of video.The proposed research concludes that deep learning and convolutional neural networks give a most appropriate solution retaining discriminative and dynamic information of the input action.The research proposes recognition of dynamic gestures using image recognition of the keyframes based on CNN extracted from the human activity.Edit distance is used to find out the label of the word to which those sets of frames belong to.The simulation results have shown that at 400 videos per human action,100 epochs,234×234 image size,the accuracy of the system is 90.79%,which is a reasonable accuracy for a relatively small dataset as compared to the previously published techniques.

Role of Organic Amendments to Mitigate Cd Toxicity and Its Assimilation in Triticum aestivum L.

In soil biota,higher and enduring concentration of heavy metals like cadmium(Cd)is hazardous and associated with great loss in growth,yield,and quality parameters of most of the crop plants.Recently,in-situ applications of eco-friendly stabilizing agents in the form of organic modifications have been utilized to mitigate the adverse effects of Cd-toxicity.This controlled experiment was laid down to appraise the imprints of various applied organic amendments namely poultry manure(PM),farmyard manure(FYM),and sugarcane press mud(PS)to immobilize Cd in polluted soil.Moreover,phytoavailability of Cd in wheat was also accessed under an alkaline environment.Results revealed that the addition of FYM(5–10 ton ha^(-1))in Cd-contaminated soil significantly increased germination rate,leaf chlorophyll content,plant height,spike length,biological and grain yield amongst all applied organic amendments.Moreover,the addition of FYM(5–10 ton ha^(-1))also reduced the phytoavailability of Cd by 73–85%in the roots,57–83%in the shoots,and 81–90%in grains of wheat crop.Thus,it is affirmed that incorporation of FYM(5–10 ton ha^(-1))performed better to enhance wheat growth and yield by remediating Cd.Thus,the application of FYM(5–10 ton ha^(-1))reduced the toxicity induced by Cd to plants by declining its uptake and translocation as compared to all other applied organic amendments to immobilize Cd under sandy alkaline polluted soil.

Robust Length of Stay Prediction Model for Indoor Patients

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.Hospital management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital management.It could allow them to take more patients for admission.As a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment.Therefore,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these issues.For this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,etc.have been analyzed to predict the LOS.We selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation criteria.Our results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected models.By Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital.Based on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases.This finding could help predict the future length of hospital stay of new patients,which will help the hospital administration estimate and manage their resources efficiently.

Explainable Artificial Intelligence Solution for Online Retail

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business.In this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one year.The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future.The permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the future.The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC curve.RF model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree classifier.Hence,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour.Our research also reveals the importance of page value as a key feature for capturing online purchasing trends.This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

Lithium-Ion Battery Screening by K-Means with DBSCAN for Denoising

Batteries are often packed together to meet voltage and capability needs.However,due to variations in raw materials,different ages of equipment,and manual operation,there is inconsistency between batteries,which leads to reduced available capacity,variability of resistance,and premature failure.Therefore,it is crucial to pack similar batteries together.The conventional approach to screening batteries is based on their capacity,voltage and internal resistance,which disregards how batteries perform during manufacturing.In the battery discharge process,real time discharge voltage curves(DVCs)are collected as a set of unlabeled time series,which reflect how the battery voltage changes.However,few studies have focused on DVC based battery screening.In this paper,we provide an effective approach for battery screening.First,we apply interpolation on DVCs and give a method to transform them into slope sequences.Then,we use density-based spatial clustering of applications with noise(DBSCAN)for denoising and treat the remaining data as input to the K-means algorithm for screening.Finally,we provide the experimental results and give our evaluation.It is proved that our method is effective.

Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases.The most fatal of these is the issue of heart disease that cannot be detected from a naked eye,and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate,body temperature,and blood pressure.The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner,followed by prescribing appropriate treatments and keeping prescription errors to a minimum.In developing countries,the domain of healthcare is progressing day by day using different Smart healthcare:emerging technologies like cloud computing,fog computing,and mobile computing.Electronic health records(EHRs)are used to manage the huge volume of data using cloud computing.That reduces the storage,processing,and retrieval cost as well as ensuring the availability of data.Machine learning procedures are used to extract hidden patterns and data analytics.In this research,a combination of cloud computing and machine learning algorithm Support vector machine(SVM)is used to predict heart diseases.Simulation results have shown that the proposed intelligent cloud-based heart disease prediction system empowered with a Support vector machine(SVM)-based system model gives 93.33%accuracy,which is better than previously published approaches.

Particle dynamics,black hole shadow and weak gravitational lensing in the f(Q)theory of gravity

We study the particle dynamics around a black hole(BH)in f(Q)gravity.First,we investigate the influence of the parameters of f(Q)gravity on the horizon structure of the BH,photon orbits and the radius of the innermost stable circular orbit(ISCO)of massive particles.We further study the effects of the parameters of f(Q)gravity on the shadow cast by the BH.Moreover,we consider weak gravitational lensing using the general method,where we also explore the deflection angle of light rays around the BH in f(Q)gravity in uniform and nonuniform plasma mediums.

Preliminary 3D printing of large inclined-shaped alumina ceramic parts by direct ink writing

Three dimensional(3D)printing technology by direct ink writing(DIW)is an innovative complex shaping technology,possessing advantages of flexibility in fabrication,high efficiency,low cost,and environmental-friendliness.Herein,3D printing of complex alumina ceramic parts via DIW using thermally induced solidification with carrageenan swelling was investigated.The rheological properties of the slurry under different thermally-induced modes were systematically studied.The solidification properties of thermally-induced pastes with varying contents of carrageenan were optimized.The experimental results showed that the optimized paste consisting of 0.4 wt%carrageenan could be rapidly solidified at about 55℃,which could print inclined-plane more than 60°in vertical without support,resulting in better homogeneity of the green body.A nearly pore-free structure was obtained after sintering at 1600℃ for 2 h.

Anisotropic stellar structures in the f(T)theory of gravity with quintessence via embedding approach

This work suggests a new model for anisotropic compact stars with quintessence in f(T)gravity by us-ing the off-diagonal tetrad and the power-law as f(T)=βT^(n),where T is the scalar torsion andβand n are real con-stants.The acquired field equations incorporating the anisotropic matter source along with the quintessence field,in f(T)gravity,are investigated by making use of the specific character of the scalar torsion T for the observed stars PSRJ1614-2230,4U1608-52.CenX-3,EXO1785-248,and SMCX-1.It is suggested that all the stellar struc-tures under examination are advantageously independent of any central singularity and are stable.Comprehensive graphical analysis shows that various physical features which are crucially important for the emergence of the stellar structures are conferred.

Physical aspects of anisotropic compact stars in f(T,T) gravity with off diagonal tetrad

This study addresses the formation of anisotropic compact star models in the background of f(T,T)gravity(where T and T represent the torsion and trace of the energy momentum tensor,respectively).f(T,T)gravity is an extension of the f(T)theory,and it allows a general non-minimal coupling between T and T.In this setup,we apply Krori and Barua's solution to the static spacetime with the components ξ=Br^(2)+c and ψ=Ar^(2).To develop viable solutions,we select a well-known model f(T,T)=αT^(m)+βT+Ф(where α and β are coupling parameters,and Ф indicates the cosmological constant).We adopt the conventional matching of interior and exterior space time to evaluate the unknowns,which are employed in the stellar configuration.We present a comprehensive discussion on the stellar properties to elaborate the anisotropic nature of compact stars corresponding to well-known models:PSRJ1416-2230,4U1608-52,CenX-3,EXO1785-248,and SMCX-1.Via physical analysis,it is observed that the solution of compact spheres satisfy the acceptability criteria,and its models behave optimally and depict stability and consistency,in accordance with f(T,T)gravity.