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Electro-optic tuning in composite silicon photonics based on ferroionic 2D materials

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Light(Science & Applications) 2024年第5期13卷 852-864页

作者： ghada Dushaq Solomon Serunjogi Srinivasa R.Tamalampudi mahmoud Rasras Department of Electrical and Computer Engineering New York University Abu DhabiP.O.Box 129188Abu DhabiUnited Arab Emirates NYU Tandon School of Engineering New York UniversityNew YorkNYUSA

Tunable optical materials are indispensable elements in modern optoelectronics,especially in integrated photonics circuits where precise control over the effective refractive index is essential for diverse ***-dimensional materials like transition metal dichalcogenides(TMDs)and graphene exhibit remarkable optical responses to external ***,achieving distinctive modulation across short-wave infrared(SWIR)regions while enabling precise phase control at low signal loss within a compact footprint remains an ongoing *** this work,we unveil the robust electro-refractive response of multilayer ferroionic two-dimensional CuCrP2S6(CCPS)in the near-infrared wavelength *** integrating CCPS into silicon photonics(SiPh)microring resonators(MRR),we enhance lightmatter interaction and measurement sensitivity to minute phase and absorption *** show that electrically driven Cu ions can tune the effective refractive index on the order of 2.8×10−3 RIU(refractive index unit)while preserving extinction ratios and resonance ***,these devices exhibit low optical losses and excellent modulation efficiency of 0.25 *** with a consistent blue shift in the resonance wavelengths among all devices for either polarity of the applied *** results outperform earlier findings on phase shifters based on ***,our study demonstrates distinct variations in electro-optic tuning sensitivity when comparing transverse electric(TE)and transverse magnetic(TM)modes,revealing a polarization-dependent response that paves the way for diverse applications in light *** combined optoelectronic and ionotronic capabilities of two-terminal CCPS devices present extensive opportunities across several *** potential applications range from phased arrays and optical switching to their use in environmental sensing and metrology,optical imaging systems,and neuromorphic systems in light-sensitive artificial synapses.

关键词： tuning polarization refractive

埃及产百里香和牛至精油、提取物的抗氧化和抗菌活性研究

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河南大学学报（医学版） 2020年第3期39卷 157-170页

作者： Adel FAHMED ghada abd-elmonsef mahmoud 马常阳康文艺河南大学国家食用菌加工技术研发专业中心河南开封475004中国埃及农业研究中心园艺研究所药用植物和芳香植物研究系吉萨12619埃及艾斯尤特大学科学院植物与微生物系艾斯尤特71515埃及河南省药食两用资源功能研究国际联合实验室河南开封475004中国

[目的]本文对埃及产的百里香(Thymus vulgaris L.)和牛至(Origanum vulgare L.)的化学成分、抗氧化和抗菌活性进行了研究。[方法]本文采用GC-MS法和福林酚方法对样品中的化学成分和多酚物质进行了分析,同时使用DPPH试剂和抑菌圈实验评... 详细信息

[目的]本文对埃及产的百里香(Thymus vulgaris L.)和牛至(Origanum vulgare L.)的化学成分、抗氧化和抗菌活性进行了研究。[方法]本文采用GC-MS法和福林酚方法对样品中的化学成分和多酚物质进行了分析,同时使用DPPH试剂和抑菌圈实验评价样品的抗氧化和抗菌效果。[结果]埃及产百里香精油中主要含有百里香酚(45.19%)、p-伞花烃(13.59%)、香芹酚(7.65%)和松油烯(6.82%),牛至精油中主要有香芹酚(52.46%)、松油烯-4-醇(7.43%)、顺-β-萜品醇(5.64%)和松油烯(4.73%)。百里香和牛至的精油中的多酚含量分别为228.78 mg/g和129.43 mg/g,表现出显著的DPPH自由基清除能力,而残渣中的多酚含量相对较少(123.68 mg/g和138.26 mg/g),但抗氧化能力要更强。在抗菌能力方面,两种提取物均对Klebsiella pneumonia(35.8±0.89)mm和(37±0.47)mm的抑制能力最强,而百里香和牛至的精油则分别对Rhodotorula mucilaginosa(25.7±0.27)mm和Aspergillus flavus(16.5±0.24)mm。[结论]埃及产百里香和牛至均具有明显的抗氧化和抗菌活性。

关键词：百里香牛至精油残渣抗氧化和抗菌活性

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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Computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail ghada Eltaweel Mohamed Meselhy Eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

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Journal of Computer and Communications 2024年第3期12卷 160-183页

作者： Hend S. Salem Mohamed A. Mead ghada S. El-Taweel Department of Computer Sciences Suez Canal University Ismailia Egypt

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

关键词： Big COVID-19 Data Machine Learning Hyperparameter Optimization Particle Swarm Optimization Computational Intelligence

Systemic oncological therapy in breast cancer patients on dialysis

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World Journal of Clinical Oncology 2024年第6期15卷 730-744页

作者： Salman Khan ghada Araji Ekrem Yetiskul Praneeth Reddy Keesari Fadi Haddadin Zaid Khamis Varun Chowdhry Muhammad Niazi Sarah Afif Meekoo Dhar Suzanne El-Sayegh Department of Internal Medicine Northwell Health-Staten Island University HospitalStaten IslandNY 10305United States Department of Hematology and Oncology Northwell Health-Staten Island University HospitalStaten IslandNY 10305United States Department of Internal Medicine CUNY School of MedicineNew YorkNY 10031United States

The advancement of renal replacement therapy has significantly enhanced the survival rates of patients with end-stage renal disease(ESRD)over ***-ever,this prolonged survival has also been associated with a higher likelihood of cancer diagnoses among these patients including breast *** cancer treatment typically involves surgery,radiation,and systemic therapies,with ap-proaches tailored to cancer type,stage,and patient ***,renal replacement therapy complicates systemic therapy due to altered drug clearance and the necessity for dialysis *** review emphasizes the need for opti-mized dosing and administration strategies for systemic breast cancer treatments in dialysis patients,aiming to ensure both efficacy and ***,ch-allenges in breast cancer screening and diagnosis in this population,including soft-tissue calcifications,are highlighted.

关键词： Breast cancer Systemic therapy Renal replacement therapy Dialysis End-stage renal disease Hormone therapy Chemotherapy

Survey study of the etiology of non-traumatic altered consciousness in the Emergency Department at Suez Canal University Hospital in Egypt

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World Journal of Clinical Cases 2023年第6期11卷 1310-1317页

作者： Bassant S Moussa Zeinab M abd Elatiff ghada M Kamal Eldin Elhadary Emergency Medicine Department Suez Canal UniversityFaculty of MedicineIsmailia 41522Egypt

BACKGROUND Disorders of consciousness including coma in non-trauma patients can be caused by a wide variety of pathologies affecting the central nervous *** represent a frequent challenge in emergency medicine and are combined with a very high in-hospital ***,early treatment of these patients is vital and increases the likelihood of a good *** To identify the causes of altered consciousness presentation to the Emergency Department at Suez Canal University *** This was a descriptive cross-sectional study conducted on 87 patients with acute non-traumatic disturbed level of consciousness(DLOC)at the Emergency *** The mean age of the studied patients was 60.5±13.6 *** them,60%were males and 40%were *** most common cause of acute non-traumatic DLOC was systemic infection,such as sepsis and septic shock(25.3%),followed by respiratory causes(24.1%)and neurological causes(18.4%).CONCLUSION The most common cause of acute non-traumatic DLOC was systemic infections followed by respiratory and neurological causes.

关键词： Disturbed level of consciousness Non-traumatic Emergency department

Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

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Computer Systems Science & Engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai ghada El-Banby Hesham F.A.Hamed Gerges M.Salama Mohammed abd-Elnaby Fathi E.abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

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Computer Systems Science & Engineering 2023年第4期45卷 361-376页

作者： ghada Emam Atteia Information Technology Department College of Computer&Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11461Saudi Arabia

Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone *** prognosis of ALL is indispensable for the effectual remediation of this *** screening of ALL is conducted through manual examination of stained blood smear microscopic images,a process which is time-consuming and prone to ***,many deep learning-based computer-aided diagnosis(CAD)systems have been established to automatically diagnose *** paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear *** introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks(CNNs)to identify the existence of ALL in blood *** augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature *** latent features are used to perform image classification using Support Vector Machine(SVM)*** obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image ***,the classification performance of the system with various sizes of the latent feature set is *** retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.

关键词： Autoencoder deep learning CNN leukemia diagnosis computeraided diagnosis

An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

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Computers, Materials & Continua 2022年第3期70卷 4393-4410页

作者： Abeer D.Algarni Walid El-Shafai ghada M.El Banby Fathi E.abd El-Samie Naglaa F.Soliman Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Saudi Arabia Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia 44519Egypt

COVID-19 remains to proliferate precipitously in the *** has significantly influenced public health,the world economy,and the persons’***,there is a need to speed up the diagnosis and precautions to deal with COVID-19 *** this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical *** paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)*** proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT *** classification stage is firstly applied to detect and classify the input medical CT ***,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT *** classification stage is implemented based on a simple and efficient CNN deep learning *** model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)*** layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection *** the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid *** proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,*** summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis *** the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the *** region of interest can be assesses both automatically and through ***

关键词： COVID-19 segmentation classification CNN SPCNN CT images