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Evaluation of AVNRT & AVRT by Different Criteria: Old & New

World Journal of Cardiovascular Surgery 2024年第7期14卷 95-106页

作者： Abdul Hamid Tahmina Alam Sonali rizwan rehan Pijous Biswas Subas Caandro Datta Asif Zaman Department of Cardiology Chattagram Medical College Hospital Chattagram Bangladesh Chattagram Medical College Hospital Chattagram Bangladesh Department of Cardiology National Institute of Cardiovascular Disease Dhaka Bangladesh Department of Cardiology Bangamata Sheikh Fozilatunnesa Mujib General Hospital Sirajganj Bangladesh Department of Cardiology Evercare Hospital Dhaka Bangladesh

The two most frequent causes of paroxysmal SVT are atrioventricular tachycardia (AVRT) and atrioventricular nodal re-entrant tachycardia (AVNRT). The purpose of this study was to assess the diagnostic efficacy of traditional and newly proposed ECG criteria in the identification of Avnrt and Avrt. Aim of the Study: The aim of this study was to evaluate Atrioventricular Nodal Reentrant Tachycardia (AVNRT) and Atrioventricular Re-entrant Tachycardia (AVRT) using both traditional and novel criteria. Methods: This prospective observational study was conducted at the Electrophysiology Unit, Department of Cardiology, National Institute of Cardiovascular Diseases (NICVD) in Dhaka, from February 2019 to January 2020. A total of 62 patients with Supraventricular Tachycardia (SVT) undergoing electrophysiology study (EPS) were included. Standard ECG criteria were applied for the differential diagnosis, and electrophysiological diagnoses were made using established criteria. Statistical analysis, including descriptive statistics and appropriate tests, was performed using SPSS 23.0. Result: In our study of 62 patients with Supraventricular Tachycardia (SVT), we found that 66.1% had AVNRT and 33.9% had AVRT. The mean age in AVNRT was higher than AVRT (41.3 ± 9.7 vs. 38.5 ± 14.3, p = 0.36) with statistically no significant difference, with similar gender distribution between AVNRT and AVRT groups. Classical AVNRT criteria were present in 30.6% of patients, and 45.2% showed a Pseudo R' wave in aVR. Additionally, 30.6% had an RP interval ≥100ms, more prevalent in AVRT patients (66.7%). Conclusion: Integrating traditional and novel criteria, including lead aVR analysis, enhances the electrocardiographic diagnosis of AVNRT and AVRT, offering a pathway to refined patient care.

关键词： SVT (Supraventricular Tachycardia) AVNRT (Atrioventricular Nodal Re-Entrant Tachycardia) AVRT (Atrioventricular Re-Entrant Tachycardia) ECG Criteria Electrophysiology

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Tribological properties of Al-GNP composites at elevated temperature

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Friction 2024年第5期12卷 1028-1041页

作者： Sunil POUDEL rizwan BAJWA Yongde XIA Zakir KHAN Yi ZHANG Yanqiu ZHU College of Engineering Mathematics and Physical SciencesUniversity of ExeterExeterEX44QFUK European Technical Centre Daido Metal Co.Ltd.Iliminster SomersetTA199PHUK

Lighter and more powerful next generation vehicles and other rotary machinery demand bearings to operate in harsher conditions for higher efficiency,and the continuous development of advanced low-wear and friction materials is thus becoming even more important to meet these *** aluminium composites reinforced with high performance lubricate phases such as graphene nanoplatelets(GNPs)are very promising and have been vigorously *** maintaining a low coefficient of friction(COF)and offering great strength against wear due to their self-lubricating capability,the solid lubricant like GNPs protect the bearing surface from wear damage and prevent change in metallurgical properties during temperature *** paper first studies the high-temperature tribological performance of aluminium matrix composites reinforced with GNP,consolidated via powder metallurgy,then elucidates their tribological *** report that the best tribological performance is achieved by the composite containing 2.0 wt%GNP,with an extraordinarily low COF of 0.09 and a specific wear rate of 3.5×10^(−2)mm^(3)·N^(−1)·m^(−1),which represent 75%and 40%reduction respectively,against the plain aluminium consolidated under identical *** in-track and out-of-track Raman analysis have confirmed the role of GNPs in creating a tribofilm on the counterpart surface which contributed to the excellent performance.

关键词： nanocomposite graphene nanoplatelet(GNP) tribology powder metallurgy

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Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

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Computers, Materials & Continua 2024年第3期78卷 3413-3429页

作者： Rabia Abid Muhammad rizwan Abdulatif Alabdulatif Abdullah Alnajim Meznah Alamro Mourade Azrour Department of Computer Science Kinnaird College for WomenLahorePakistan College of Science and Engineering University of DerbyDerbyDE221GBUK Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia STI Laboratory IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsMeknesMorocco

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based *** this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and *** Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data *** this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare *** experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)*** experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

关键词： Artificial intelligence data privacy federated machine learning healthcare system security

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Multi-functional metasurface:ultra-wideband/multi-band absorption switching by adjusting guided-mode resonance and local surface plasmon resonance effects

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Communications in Theoretical Physics 2024年第6期76卷 174-184页

作者： Wenxin Li Shubo Cheng Huafeng Zhang Zao Yi Bin Tang Can Ma Pinghui Wu Qingdong Zeng rizwan Raza School of Physics and Optoelectronic Engineering Yangtze UniversityJingzhouHubei 434023China Joint Laboratory for Extreme Conditions Matter Properties Southwest University of Science and TechnologyMianyang 621010China School of Chemistry and Chemical Engineering Jishou UniversityJishou 416000China School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213164China Department of Oncology Sichuan Science City HospitalMianyangSichuan Province 621000China Office of Science and Technology Quanzhou Normal UniversityQuanzhou 362000China School of Physics and Electronic-information Engineering Hubei Engineering UniversityXiaogan 432000China Department of Physics COMSATS Institute of Information Technology 54000 LahorePakistan

This study introduces an innovative dual-tunable absorption film with the capability to switch between ultra-wideband and narrowband *** manipulating the temperature,the film can achieve multi-band absorption within the 30-45 THz range or ultra-wideband absorption spanning 30-130 THz,with an absorption rate exceeding ***,the structural parameters of the absorption film are optimized using the particle swarm optimization(PSO)algorithm to ensure the optimal absorption *** absorption response of the film is primarily attributed to the coupling of guided-mode resonance and local surface plasmon resonance *** film's symmetric structure enables polarization incoherence and allows for tuning through various means such as doping/voltage,temperature and structural *** the case of a multi-band absorption response,the film exhibits good sensitivity to refractive index changes in multiple absorption ***,the absorption spectrum of the film remains effective even at large incidence angles,making it highly promising for applications in fields such as biosensing and infrared stealth.

关键词： PSO optimization ultra-wideband/narrowband absorption switching intelligent absorption film vanadium dioxide

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Tb^(3+)-nucleic acid probe-based label-free and rapid detection of mercury pollution in food

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Food Science and Human Wellness 2024年第2期13卷 993-998页

作者： Xuhan Xia Chenxi Zhou Yulin Zhu Yi Dong Qiang He Mohammad rizwan Khan Yuanlong Chi Rosa Busquets Ruijie Deng Yao Ren College of Biomass Science and Engineering Healthy Food Evaluation Research CenterSichuan UniversityChengdu 610065China Department of Chemistry College of ScienceKing Saud UniversityRiyadh 11451Saudi Arabia School of Life Sciences Pharmacy and ChemistryKingston University LondonKingston Upon Thames KT12EEUK

Mercury is a threatening pollutant in food,herein,we developed a Tb^(3+)-nucleic acid probe-based label-free assay for mix-and-read,rapid detection of mercury *** assay utilized the feature of light-up fluorescence of terbium ions(Tb^(3+))via binding with single-strand *** ion,Hg^(2+)induced thymine(T)-rich DNA strand to form a double-strand structure(T-Hg^(2+)-T),thus leading to fluorescence *** on the principle,Hg^(2+)can be quantified based on the fluorescence of Tb^(3+),the limit of detection was 0.0689μmol/L and the linear range was 0.1-6.0μmol/*** to the specificity of T-Hg^(2+)-T artificial base pair,the assay could distinguish Hg^(2+)from other metal *** recovery rate was ranged in 98.71%-101.34%for detecting mercury pollution in three food *** assay is low-cost,separation-free and mix-to-read,thus was a competitive tool for detection of mercury pollution to ensure food safety.

关键词： Mercury pollution Food safety Nucleic acid probe Label-free Terbium

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Fentanyl and xylazine crisis:Crafting coherent strategies for opioid overdose prevention

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World Journal of Psychiatry 2024年第6期14卷 760-766页

作者： Lakshit Jain Jasleen Kaur Shahana Ayub Danya Ansari rizwan Ahmed Abdul Qadir Dada Saeed Ahmed Department of Psychiatry University of ConnecticutFarmingtonCT 06032United States Addiction Services Division Connecticut Valley HospitalMiddletownCT 06457United States Department of Psychiatry Institute of LivingHartfordCT 06102United States Department of Medicine Islamabad Medical and Dental CollegeIslamabad 44000Pakistan Department of Medicine Liaquat College of Medicine and DentistryKarachi 75290Pakistan Department of Medicine Trinity School of MedicineRoswellGA 30075United States Addiction Services and Dual Diagnosis Unit Saint Francis Hospital and Medical CenterHartfordCT 06105United States

The United States is in the throes of a severe opioid overdose epidemic,primarily fueled by the pervasive use of fentanyl and the emerging threat of xylazine,a veterinary sedative often mixed with *** high potency and long duration of fentanyl is compounded by the added risks from xylazine,heightening the lethal danger faced by opioid *** such as enhanced surveillance,public awareness campaigns,and the distribution of fentanylxylazine test kits,and naloxone have been undertaken to mitigate this ***-related overdose deaths persist despite these efforts,partly due to inconsistent policies across states and resistance towards adopting harm reduction strategies.A multifaceted approach is imperative in effectively combating the opioid overdose *** approach should include expansion of treatment access,broadening the availability of medications for opioid use disorder,implementation of harm reduction strategies,and enaction of legislative reforms and diminishing stigma associated with opioid use disorder.

关键词： Fentanyl Xylazine Opioid overdose Epidemic Opioid use disorder Buprenorphine Medications for opioid use disorder

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Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

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Computers, Materials & Continua 2024年第2期78卷 2047-2066页

作者： Muhammad rizwan Muhammad Faheem Mushtaq Maryam Rafiq Arif Mehmood Isabel de la Torre Diez Monica Gracia Villar Helena Garay Imran Ashraf Department of Artificial Intelligence The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Information Technology Khwaja Fareed University of Engineering and ITRYKhan64200Pakistan Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Signal Theory and Communications and Telematic Engineering Unviersity of ValladolidPaseo de Belén 15Valladolid47011Spain Faculty of Social Science and Humanities Universidad Europea del Atlántico Isabel Torres 21Santander39011Spain Department of Project Management Universidad Internacional IberoamericanaCampeche24560México Fundación Universitaria Internacional de Colombia Bogotá111611Colombia Universidad Internacional Iberoamericana AreciboPuerto Rico00613USA Universidade Internacional do Cuanza Cuito46703Angola Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541South Korea

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media *** study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the ***,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic *** baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the *** FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to ***,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual *** proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText *** proposed model better captures the contextual features of the relatively small sample class and aids in the detection of

关键词： Depression classification deep learning FastText machine learning

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Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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Computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed Alsekait Ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad rizwan Rashid Rana Zohair Ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

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Computer Systems Science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi Saad Awadh Alanazi Shahid Naseem Muhammad Anwar Madallah Alruwaili Muhammad rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad Ahmad Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia Department of Computer Sciences Kinnaird College forWomenLahorePunjab54700Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia Computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan Department of Basic Sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies *** vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end *** anomaly detector investigates these identified risks and provides secure network *** enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)*** anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation *** classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning

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