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Oxidative stress and redox signaling in CRPC progression: therapeutic potential of clinically-tested Nrf2-activators

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Cancer Drug Resistance 2021年第1期4卷 96-124页

作者： Debasis Mondal Devin Narwani shahnawaz Notta Dawood Ghaffar Nikhil Mardhekar syed s.a.quadri Debusk College of Osteopathic Medicine Lincoln Memorial UniversityKnoxvilleTN 37932USA

Androgen deprivation therapy(ADT)is the mainstay regimen in patients with androgen-dependent prostate cancer(PCa).However,the selection of androgen-independent cancer cells leads to castrate resistant prostate cancer(CRPC).The aggressive phenotype of CRPC cells underscores the need to elucidate mechanisms and therapeutic strategies to suppress CRPC *** ADT,the activation of androgen receptor(AR)transcription factor continues via crosstalk with parallel signaling *** of how these signaling cascades are initiated and amplified post-ADT is *** deprivation can increase oxidative stress and the resultant reactive oxygen species(ROs)may activate both AR and non-AR ***,ROs-induced inflammatory cytokines may further amplify these redox signaling pathways to augment AR ***,clinical trials using ROs quenching small molecule antioxidants have not suppressed CRPC progression,suggesting that more potent and persistent suppression of redox signaling in CRPC cells will be *** transcription factor Nrf2 increases the expression of numerous antioxidant enzymes and downregulates the function of inflammatory transcription factors,e.g.,nuclear factor kappa *** documented that Nrf2 overexpression can suppress AR-mediated transcription in CRPC cell ***,two Nrf2 activating agents,sulforaphane(a phytochemical)and bardoxolone-methyl(a drug in clinical trial)suppress AR levels and sensitize CRPC cells to *** observations implicate the benefits of potent Nrf2-activators to suppress the lethal signaling cascades that lead to CRPC *** review article will address the redox signaling networks that augment AR signaling during PCa progression to CRPC,and the possible utility of Nrf2-activating agents as an adjunct to ADT.

关键词： Prostate cancer androgen receptor hormone therapy endocrine resistance oxidative stress redox signaling Nrf2 Nrf2-activators

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Deep Learning Based Efficient Crowd Counting system

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Computers, Materials & Continua 2024年第6期79卷 4001-4020页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Muhammad Hamza Faheem syed shah Amanullah quadri Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Centre of Excellence in Cybercrime and Digital Forensics Naif Arab University for Security SciencesRiyadh14812Saudi Arabia Center of Excellence for Information Assurance(COEIA) King Saud UniversityRiyadh12372Saudi Arabia

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different *** is challenging to determine the approximate crowd size from an image of the crowd’s *** in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the *** employed the available public benchmark crowd-counting datasets UCF CC 50 and shanghaiTech parts A and B for model training and testing to validate the model’s *** analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean square Error(MsE),and compared the results of the proposed systems with the state-of-art models of crowd *** results show the superiority of the proposed system.

关键词： Crowd counting EfficientNet multi-head attention convolutional neural network transfer learning

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Automatic Finding of Brain-Tumour Group Using CNN segmentation and Moth-Flame-Algorithm,selected Deep and Handcrafted Features

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Computers, Materials & Continua 2024年第5期79卷 2585-2608页

作者： Imad saud Al Naimi syed Alwee Aljunid syed Junid Muhammad lmran Ahmad K.suresh Manic Faculty of Electronic Engineering Technology University Malaysia Perils(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Department of Electrical and Communication Engineering National University of Science and TechnologyAL HailAL Seeb130Sultanate of Oman Department of Research and Innovation Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)ChennaiTamiNadu602105India

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in ***’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality *** overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved *** research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved *** methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance *** study utilized clinical-grade brain MRI of BRATs and TCIA datasets for the *** framework segments detected the glioma(low/high grade)and glioblastoma class *** work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 *** study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.

关键词： Brain tumour VGG-UNet VGG16 moth-flame-algorithm classification

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Network security Enhanced with Deep Neural Network-Based Intrusion Detection system

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Computers, Materials & Continua 2024年第7期80卷 1457-1490页

作者： Fatma s.Alrayes Mohammed Zakariah syed Umar Amin Zafar Iqbal Khan Jehad saad Alqurni Information systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

This study describes improving network security by implementing and assessing an intrusion detection system(IDs)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDs is trained and validated by the model using the NsL-KDD dataset,a popular benchmark for IDs *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDs in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning Deep-Learning intrusion detection system security privacy deep neural network NsL-KDD Dataset

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CNN Channel Attention Intrusion Detection systemUsing NsL-KDD Dataset

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Computers, Materials & Continua 2024年第6期79卷 4319-4347页

Intrusion detection systems(IDs)are essential in the field of cybersecurity because they protect networks from a wide range of online *** goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection systems(NIDs)that can identify *** NsL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection ***,maintaining operating efficiency while improving detection accuracy is the primary goal of this ***,typical NIDs examines both risky and typical behavior using a variety of *** the NsL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel *** to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection ***,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this *** efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

关键词： Intrusion detection system(IDs) NsL-KDD dataset deep-learning machine-learning CNN channel Attention network security

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Detection of onset of agglomeration in a bubbling fluidized bed biomass combustor using reactive Eulerian computational fluid dynamics

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Particuology 2024年第7期90卷 504-515页

作者： Abdullah Tasleem Atta Ullah Fei Li Qun Yi William Nimmo syed sheraz Daood Department of Chemical Engineering Pakistan Institute of Engineering and Applied Sciences(PIEAS)Lehtrar RoadP.O.Nilore45650IslamabadPakistan Institute of Process Engineering Chinese Academy of SciencesBeijing100190China school of Chemical Engineering and Pharmacy Wuhan Institute of TechnologyWuhan430205China Energy Engineering Group Energy 2050Department of Mechanical EngineeringUniversity of SheffieldSheffieldS37RDUnited Kingdom Institute of Energy and Environmental Engineering Faculty of ElectricalEnergy&Environmental EngineeringUniversity of the PunjabQuaid-e-Azam CampusLahorePakistan Energy Engineering Research and Development Centre Faculty of ElectricalEnergy&Environmental EngineeringUniversity of the PunjabQuaid-e-Azam CampusLahorePakistan

The choice of a type of combustion technology to be used for heat or power generation depends on economic,technical,operational and fuel availability *** benefits associated with the evolving market driven by the fluidised bed combustion(FBC)technology cannot be overlooked especially when gauged at 65 GWth of worldwide installed capacity alongside added benefits of handling fuel variation,low pollutant emissions and high combustion *** or biomass waste will continue to have a vital role to play in the future FBC technology-based power *** often contains high levels of inorganic species that can form sticky agglomerates posing a significant risk to boiler operation resulting in unscheduled *** added complexity of the behaviour of the fuel and bed material mix highlights the requirement for simulation models to identify agglomeration to help improve the overall performance and reliability of FBC *** resolve this problem,this research devised a simulation strategy for the detection of agglomeration using the Eulerian–Eulerian *** developed modelling strategy is validated with the experimental data available in literature for two-dimensional simplified geometry of a pilot-scale fluidised bed *** model results were found promising and robust to predict bed defluidisation times and other parameters consistent with the experimental data.

关键词： Fluidized bed combustion Eulerian Models Biomass Agglomeration Defluidisation

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Enhancing skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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Computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： syeda shamaila Zareen Guangmin sun Mahwish Kundi syed Furqan Qadri salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and security Zhejiang LabHangzhou311121China Computer science Department MNS University of AgricultureMultan59220Pakistan

skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long short-TermMemory (LsTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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Flexible pavement longitudinal joint quality evaluation using non-destructive testing

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Journal of Road Engineering 2024年第2期4卷 189-202页

作者： Hamad Bin Muslim syed Waqar Haider Lev Khazanovich Department of Civil and Environmental Engineering Michigan State UniversityEast LansingMI 48824USA Department of Civil and Environmental Engineering University of PittsburghPittsburghPA 15261USA

Longitudinal joint construction quality is critical to the life of flexible *** deteriorated longitudinal joints has become a challenge for many highway *** the joint's quality through better compaction during construction can help achieve flexible pavements with longer service lives and less *** quality control(QC)and quality assurance(QA)plans provide limited ***,the risk of missing areas with poor joint compaction is significant.A density profiling system(DPs)is a non-destructive alternative to conventional destructive evaluation *** can provide quick and continuous real-time coverage of the compaction during construction in *** paper presents several case studies comparing various types of longitudinal joints and demonstrating the use of DPs to evaluate the joint's compaction *** paper shows that dielectric measurements can provide valuable insight into the ability of various construction techniques to achieve adequate levels of compaction at the longitudinal *** paper proposes a dielectric-based longitudinal joint quality index(LJQI)to evaluate the relative compaction of the joint during *** also shows that adopting DPs for assessing the compaction of longitudinal joints can minimize the risk of agencies accepting poorly constructed joints,identify locations of poor quality during construction,and achieve better-performing flexible pavements.

关键词： sustainable pavements Dielectric/density profiling system Quality assurance Non-destructive testing Asphalt longitudinal joint

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Mechanical performance of cold mix asphalt containing cup lump rubber as a sustainable bio-modifier

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Journal of Traffic and Transportation Engineering(English Edition) 2024年第3期11卷 424-440页

作者： syed Abdul Ghafar Muhammad Naqiuddin Mohd Warid Norhidayah Abdul Hassan Zulfiqar Ali Jattak Ashraf Abdalla Mohammed Radwan Faculty of Civil Engineering Universiti Teknologi MalaysiaSkudai 81310Malaysia Department of Civil Engineering Balochistan University of Information TechnologyEngineering&Management SciencesQuettaPakistan Department of Civil Engineering Balochistan University of Engineering and TechnologyKhuzdarPakistan Department of Civil Engineering Faculty of EngineeringUniversity of TripoliTripoliLibya

The road construction industry aims to contribute to the protection of already compromised *** mix asphalt(CMA)is a measure initiated by the road industry to protect the environment and preserve *** having additional benefits,CMA has attracted little attention due to its inferior ***'s performance is enhanced using a sustainable binder bio-modifier,natural cup lump rubber(CLR)is one of *** study evaluated the tensile properties,rutting,moisture susceptibility,and adhesion properties of CLR-modified CMA(CMA-CR).The tensile property was enhanced by 26%due to CLR ***-CR had excellent rutting resistance of less than 2 mm rut depth at 10,000 load cycles,showing 70%improvement compared with conventional *** susceptibility evaluation indicated that CMA-CR had tensile strength ratio(TsR)value of 104%,satisfying the minimum 80%requirement of AAsHTO *** also retained more than 96%bitumen *** moisture damage resistance was improved by 12%and 10%in terms of TsR and stripping,*** durability results revealed that the CMA-CR mixture prevented higher mass loss,representing 14%improvement compared with conventional CMA.

关键词： Cup lump rubber Cup lump modified cold mix asphalt Moisture damage Tensile property Rutting Digital imaging

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Classification and Comprehension of software Requirements Using Ensemble Learning

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Computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad syed Muqsit shaheed Rubia Fatima sajid shah Mohammad Elaffendi Gauhar Ali school of Computer science and Technology Anhui UniversityHefei230039China school of Computing sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer science and IT University of LahoreLahore55150Pakistan Department of Computer science Emerson UniversityPunjabMultan60000Pakistan EIAs(Emerging Intelligent Autonomous systems)Data science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy sliding Window(AsW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMIsE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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