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Tsinghua Science and Technology 2024年第2期29卷 400-414页

作者： nasir Ahmad Jalali Hongsong Chen Department of Computer Science University of Science and Technology Beijing(USTB)Beijing 100083China

The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band *** such systems,all participants related to commercial and industrial systems must communicate and generate ***,due to the small storage capacities of IoT devices,they are required to store and transfer the generated data to third-party entity called“cloud”,which creates one single point to store their ***,as the number of participants increases,the size of generated data also ***,such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security,privacy,and *** address these challenges,Federated Learning(FL)has been proposed as a reasonable decentralizing approach,in which clients no longer need to transfer and store real data in the central ***,they only share updated training models that are trained over their private *** the same time,FL enables clients in distributed systems to share their machine learning models collaboratively without their training data,thus reducing data privacy and security ***,slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed ***,these unnecessary communication rounds make the system vulnerable to security and privacy issues,because irrelevant model updates are sent between clients and ***,in this work,we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song(CKKS)to encrypt model parameters for their local information privacy-preserving *** proposed solution uses the impetus term to speed up model convergence during the model training ***,it establishes a secure communication channel between IoT devices and the *** a

关键词： Federated Learning(FL) Internet of Things(IoTs) lightweight Transport Layer Security(iTLS) Cheon-Kim-Kim-Song(CKKS)

Can NogoA Be a Suitable Target to Treat Ischemic Stroke?

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Neuroscience Bulletin 2024年第4期40卷 557-560页

作者： Suliman Khan Abdul nasir medical Research Center The Second Affiliated Hospital of Zhengzhou UniversityZhengzhou540014China

Treating stroke or restoring brain function after stroke remains challenging for clinicians[1,2].Researchers have largely focused on developing neuroprotective strategies and translating these discoveries from rodent experiments to the clinical setting;however,these have been typified by several failures[2].It is known that astrocytes play a crucial role in the pathogenesis of ischemic stroke[3,4].They are activated in response to stimuli arising from injury,characterized by drastic changes to their morphology and function-associated gene *** astrocytes exacerbate secondary neurodegeneration(repair of nervous tissue by the generation of new neurons,glial cells,axons,myelin,or synapses)[5]as well as provide neuroprotection during CNS pathologies[6].It is worth noting that myelin-related neurite growth inhibitors induce the suppression of axon regeneration after stroke[7].

关键词： protective NogoA clinical

Tuning structural,electrical,dielectric and magnetic properties of mg-Cu-Co ferrites via dysprosium(Dy^(3+))doping

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Journal of Rare Earths 2024年第1期42卷 137-146,I0005页

作者： maria Akhtar m.S.Hasan nasir Amin N.A.morley muhammad Imran Arshad Department of Physics Government College UniversityFaisalabad38000Pakistan Department of Physics The University of Lahore53700Pakistan Department of materials Science and Engineering The University of SheffieldS13JDUK Department of Physics&Astronomy University College LondonUK

In current research work,Dy3+substituted mg0.5Cu0.25Co0.25Fe2-xDyxO4(0.0≤x≤0.04 with the step interval of 0.01)soft ferrites were synthesized by the sol-gel auto combustion *** prepared samples were characterized by the techniques using X-ray diffraction(XRD),scanning electron microscopy(SEm),energy dispersive X-ray spectroscopy(EDS),Fourier transform infrared(FTIR)spectroscopy,curre nt-voltage(Ⅰ-Ⅴ)measurement,LCR meter,vibrating sample magnetometer(VSm)and *** data reveal that the average crystallite size is 49.71 nm and the lattice constant is 0.83703 nm for sample x=*** non-uniform grain growth was demonstrated by micrographs and impurity-free elemental composition was observed from EDX *** DC resistivity has an increasing and decreasing trend in ferromagnetic and paramagnetic regions with an increase in ***,the high resistivity is observed with the order of 1010Ω·cm,and the activation energy is 0.944 eV for samples x=*** dielectric parameters including dielectric constant,dielectric losses,and impedance gradually decrease with the increase in frequency from 8 Hz to 8 *** minimum dielectric loss at high frequency is found for sample x=*** coercivity(Hc)and saturation magnetization(ms)are found in the ranges of 520.82-544.02 Oe and 20.5841-21.1473 emu/g,*** observations confirm that dysprosium(x=0.03)doped mCC-soft ferrites may be applicable in transformer cores,microwave absorbance,and telecommunication devices.

关键词： Soft ferrites DC resistivity Dielectric loss Impedance Coercivity Rare earths

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聚磷腈的合成与表征

聚磷腈的合成与表征

mixed convectional and chemical reactive flow of nanofluid with slanted mHD on moving permeable stretching/shrinking sheet through nonlinear radiation,energy omission

第十八届全国波谱学学术年会

作者： Akram muhammad 王立俞豪杰 Hamad Khalid nasir m.abbasi m.Saleem Zain-ul-Abdin 陈永盛化学工程联合国家重点实验室化学工程与生物工程学系浙江大学

＜正＞聚磷腈是结构可调的聚合物,在生物医药领域（例如药物输送、组织工程等）有着广泛的应用。聚磷腈是骨架上磷原子与氮原子交替排布有机-无机杂化材料,两个侧链单元（通常是有机基团）通过共价键接到每个磷原子上。聚二氯磷腈（DPC...

＜正＞聚磷腈是结构可调的聚合物,在生物医药领域（例如药物输送、组织工程等）有着广泛的应用。聚磷腈是骨架上磷原子与氮原子交替排布有机-无机杂化材料,两个侧链单元（通常是有机基团）通过共价键接到每个磷原子上。聚二氯磷腈（DPCP）是合成聚磷腈的重要中间体,DPCP上的氯原子通过亲核试剂取代得到生物稳定或生物可降解的聚合物[1,2]。由六氯三聚磷腈（HCCP）合成PDCP过程中,单体HCCP的纯化非常重要。通过重结晶与升华的方法来纯化HCCP,其纯度可由31P NmR来测定。纯净的HCCP在20 ppm处有

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Chinese Physics B 2024年第5期33卷 193-202页

作者： Saleem nasir Sekson Sirisubtawee Pongpol Juntharee Taza Gul Department of mathematics Faculty of Applied ScienceKing Mongkut's University of Technology North BangkokBangkok 10800Thailand Department of mathematics City University of Science and Information TechnologyPeshawarPakistan

Hybrid nanofluids are remarkable functioning liquids that are intended to reduce the energy loss while maximizing the heat *** the involvement of suction and nonlinear thermal radiation effects,this study attempted to explore the energy transmission features of the inclined magnetohydrodynamic(mHD)stagnation flow of CNTs-hybrid nanofluid across the nonlinear permeable stretching or shrinking *** work also included some noteworthy features like chemical reactions,variable molecular diffusivity,quadratic convection,viscous dissipation,velocity slip and heat omission *** appropriate similarity components,the model equations were modified to ODEs and computed by using the HAm *** impact of various relevant flow characteristics on movement,heat and concentration profiles was investigated and plotted on a *** various model factors,the significance of drag friction,heat and mass transfer rate were also computed in tabular and graphical *** leads to the conclusion that such factors have a considerable impact on the dynamics of fluid as well as other engineering measurements of ***,viscous forces are dominated by increasing the values ofλ_(p),δ_(m)andδ_(q),and as a result,F(ξ)accelerates while the opposite trend is observed for m andφ.The drag friction is boosted by the augmentation m,λ_(p)andφ,but the rate of heat transfer *** to our findings,hybrid nanoliquid effects dominate that of ordinary nanofluid in terms of F(ξ),Θ(ξ)andφ(ξ)*** HAm and the numerical technique(shooting method)were found to be in good agreement.

关键词： hybrid nanofluid(SWCNT+mWCNT/H_(2)O) velocity slip conditions nonlinear thermal radiation exponential stretching/shrinking sheet inclined magnetohydrodynamic(mHD)stagnation flow

Exploring multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

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Computers, materials & Continua 2024年第6期79卷 4603-4620页

作者： mohammad Aldossary Hatem A.Alharbi nasir Ayub Department of Computer Engineering and Information College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Al-Dawasir11991Saudi Arabia Department of Computer Engineering College of Computer Science and EngineeringTaibah UniversityMadinah42353Saudi Arabia Department of Creative Technologies Air University IslamabadIslamabad44000Pakistan

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer ***,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy *** paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research *** IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data *** sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for *** data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center *** the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT *** model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a mean Squared Error(mSE)of 5.33%and a mean Absolute Error(mAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power ***,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector machine(SVm),Decision Trees,and *** NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark *** findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive

关键词： Cloud computing energy efficiency data center optimization internet of things(IoT) hybrid models

Data-Driven Decision-making for Bank Targetmarketing Using Supervised Learning Classifiers on Imbalanced Big Data

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Computers, materials and Continua 2024年第1期81卷 1703-1728页

作者： nasir F. Ahmed A.A. SabirKiraz m. Yevseyeva I. Saif m. School of Computer Science and Informatics De Montfort University Leicester LE1 9BH United Kingdom Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor 86400 Malaysia

Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve, while Extreme Gradient Boosting (XGBoost) outperforms other traditional classifiers in terms of F-1 score. Employing oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost, delivering superior results across all metrics, particularly when using the SmOTE variant known as the BorderlineSmOTE2 technique. The study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced datasets. These factors include the importance of selecting appropriate datasets for training and testing, choosing the right classifiers, employing effective techniques for processing and handling imbalanced datasets, and identifying suitable metrics for performance evaluation. Additionally, factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making. © 2024 The Authors. Published by Tech Science Press.

关键词： Big data data mining data visualization imbalanced dataset label encoding machine learning sampling techniques

Photochemical reactions as synthetic tool for pharmaceutical industries

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Chinese Chemical Letters 2024年第9期35卷 9-31页

作者： Shehla Khalid muhammad Bilal nasir Rasool muhammad Imran Department of Chemistry Government College UniversityFaisalabad 38000Pakistan School of Chemistry and Chemical Engineering Shandong UniversityJi’nan 250100China Department of Chemistry Faculty of ScienceKing Khalid UniversityAbha 61413Saudi Arabia

In the quest for new agrochemicals and pharmaceuticals,chemists seek access to reliable and mild synthetic techniques to allow for the systematic modification of chemical structures,exploration of unexplored chemical space,and facilitation of practical synthesis in their search for novel agrochemicals and *** this regard,photocatalytic reactions enabled the synthesis of intricate and more functionalized *** review overviews the developed synthetic methodologies and their utility in the chemical synthesis of *** review also offers in-depth insights into contemporary photoredox reactions such as allylic additions,cyclization,reductive cross-coupling,C–H activation,ring opening,oxidative cross-coupling,dehydrogenation,desulphonation,and *** provides a positive outlook for the promising future of this field.

关键词： Photocatalyst Photochemical reactions Drugs Natural products Late-stage functionalization

Cervical Cancer Prediction Empowered with Federated machine Learning

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Computers, materials & Continua 2024年第4期79卷 963-981页

作者： muhammad Umar nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy m.A.Khan Khan muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of medicalThings (IomT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology