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

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Communications in Theoretical Physics 2023年第12期75卷 115-128页

作者： allah ditta Xia Tiecheng Farruh Atamurotov Ibrar Hussain G Mustafa Department of Mathematics Shanghai University and Newtouch Center for Mathematics of Shanghai UniversityShanghai200444ShanghaiChina New Uzbekistan University Movarounnahr Street 1Tashkent 100000Uzbekistan Central Asian University Milliy Bog’Street 264Tashkent 111221Uzbekistan Tashkent University of Applied Sciences Str.Gavhar 1Tashkent 100149Uzbekistan Institute of Theoretical Physics National University of UzbekistanTashkent 100174Uzbekistan School of Electrical Engineering and Computer Science National University of Sciences and TechnologyH-12IslamabadPakistan Department of Physics Zhejiang Normal UniversityJinhua 321004China

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.

关键词： f(Q)gravity black holes photon motion shadow and lensing

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

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan allah ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

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 o

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

Robust Length of Stay Prediction Model for Indoor Patients

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Computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan allah ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

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 sta

关键词： Length of stay machine learning robust model random forest regression

Explainable Artificial Intelligence Solution for Online Retail

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Computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi allah ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

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.

关键词： Explainable artificial intelligence online retail neural network random forest regression

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

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Phyton-International Journal of Experimental Botany 2022年第11期91卷 2491-2504页

作者： Tauqeer Ahmad Yasir Sobia Aslam Muhammad Shahid Rizwan allah Wasaya Muhammad Ateeq Muhammad Naeem Khan Sikander Khan Tanveer Walid Soufan Basharat Ali allah ditta Arpna Kumari Ayman EL Sabagh College of Agriculture Bahauddin Zakariya UniversityBahadur Sub-Campus Layyah31200Pakistan Cholistan Institute of Desert Studies The Islamia University of BahawalpurBahawalpur63100Pakistan Directorate General Soil Survey of Punjab Agriculture DepartmentLahore54780Pakistan National Agricultural Research Centre Islamabad44000Pakistan Plant Production Department College of Food and Agriculture SciencesKing Saud UniversityRiyadh11451Saudi Arabia Department of Agricultural Engineering Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan School of Biological Sciences The University of Western AustraliaPerthWA 6009Australia Department of Environmental Sciences Shaheed Benazir Bhutto University SheringalDir(U)Khyber Pakhtunkhwa18000Pakistan Academy of Biology and Biotechnology Southern Federal UniversityRostov-on-Don344006Russia Department of Agronomy Faculty of AgricultureKafrelsheikh UniversityKafr El Sheikh33511Egypt

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.

关键词： Cadmium toxicity mitigation chlorophyll attributes morphometry yield attributes cereal

Effect of microstructures restoration on high temperature fatigue behavior of DZ125 superalloy

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Progress in Natural Science:Materials International 2021年第4期31卷 633-640页

作者： Shifu Wang allah ditta Yi Xu Zheng Zhang School of Materials Science and Engineering Beihang University

Combination of hot isostatic pressing(HIP) and rejuvenation heat treatment(RHT) technology was used to restore creep-damaged DZ125 directional solidified superalloy, and the influence of microstructure restoration on high temperature fatigue behavior of the samples was explored. The results show that the HIP+RHT process could effectively heal internal cavities and recover the degraded γ’ phase in creep-damaged DZ125 superalloy to cubic particles similar as in as-received sample. After restoration treatment, the stress concentration areas inside the sample eradicated with the healing of the internal cavities, and the fatigue source areas were limited only to near surface than initiating from inside as in the as-received and creep-damaged samples. As a result, the restored sample had higher crack initiation life and lower crack propagation rate compared to as-received and creep damaged samples. The TEM microstructure characterization near fatigue fracture showed that the restoration of the degraded γ0 phase eliminated tangled dislocation in creep damaged sample and produced evenly distributed dislocations in the γ channel with short curved line-like morphology, like the as-received sample, which effectively hindered the dislocations movement during subsequent deformation, and strengthen the fatigue resistant of alloy. Therefore, it can be concluded that the HIP-RHT process, through the combined effect of internal cavities healing and the restoration of the degraded microstructures, renders higher high temperature fatigue life than creep-damaged and even higher than as-received DZ125 superalloy.

关键词： Hot isostatic pressing Rejuvenation heat treatment High temperature fatigue behavior Healing of internal cavities Restoring of degraded microstructures

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

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Computers, Materials & Continua 2020年第12期65卷 2111-2122页

作者： Yudong Wang Jie Tan Zhenjie Liu allah ditta Institute of Automation Chinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China University of Education TownshipCollege Rd Lahore PunjabLahore54770Pakistan

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.

关键词： Lithium-ion battery battery screening K-means denoising

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

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Chinese Physics C 2021年第4期45卷 426-439页

作者： allah ditta Mushtaq Ahmad Ibrar Hussain G.Mustafa Department of Mathematics COMSATS University IslamabadLabore CampusPakistan National University of Computer and Emerging Sciences Chiniot-Faisalabad CampusPakistan School of Electrical Engineering and Computer Science National University of Sciences and TechnologyH-12IslanpabadPakistan Department of Mathematics Shanghai UniversityShanghai 200444China

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.

关键词： quintessence power-law model f(T)gravity anisotropic compact stars

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

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Chinese Physics C 2021年第8期45卷 190-205页

作者： M.Zubair allah ditta G.Abbas Rabia Saleem Department of Mathematics COMSATS University IslamabadLahore CampusLahorePakistan Department of Mathematics The Islamia University of BahawalpurBahawalpurPakistan

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.

关键词： f(T,T)gravity compact stars stellar models