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Accelerated versus standard corneal cross linking in the treatment of ectasia post refractive surgery and penetrating keratoplasty: a medium term randomized trial

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International Journal of Ophthalmology(English edition) 2019年第11期12卷 1714-1719页

作者： Hany A.Khairy moataz f.elsawy Khaled Said-Ahmed Marwa A.Zaki Sameh S Mandour Department of Ophthalmology Menoufia University Hospitals

AIM: To compare the clinical outcomes of the standard corneal cross linking(CXL) and the accelerated CXL in patients with progressive corneal ectasia post refractive surgery and penetrating ***: Totally 120 eyes of 83 patients scheduled to receive either standard CXL(3 m W/cm^2 for a period of 30 min) or accelerated CXL(18 m W/cm2 for a period of 5 min). The main outcomes for comparison were the change in: maximum-K reading(K-max), manifest refractive spherical equivalent(SE), central corneal thickness(CCT), and the best corrected distance visual acuity(CDVA).RESULTS: One hundred and eleven eyes completed the study. The main outcome measurement was the K-max reading. Both group showed significant improvement in the value postoperatively at 6 and 12 mo. The mean change in the standard group was 1.21±0.11 D and in the accelerated group was 0.90±0.05 D at the end of 12 mo postoperatively, with no statistically significant difference between the 2 groups. Similarly, CDVA improved significantly from their preoperative value in the standard group by 2.98±0.11 letters, and in the accelerated group by 2.20±0.06 letters, with no statistically significant difference between the two groups. Both of the SE, and CCT showed no statistically significant difference at the end of follow up period in each ***: Both standard CXL and accelerated CXL are safe and effective treatment in halting ectasia after corneal refractive surgery. The accelerated CXL results are comparable to the standard CXL with short time exposure of the cornea to ultraviolet irradiation, leading to reduced operation time, reduced operative ocular discomfort, and corneal haze.

关键词： corneal cross linking accelerated refractivesurgery penetrating keratoplasty corneal ectasia

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A new approach to learning and recognizing leaf diseases from individual lesions using convolutional neural networks

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Information Processing in Agriculture 2023年第1期10卷 11-27页

作者： Lawrence C.Ngugi moataz Abdelwahab Mohammed Abo-Zahhad Department of Electronics and Communication Engineering Egypt-Japan University of Science and TechnologyAlexandriaEgypt Department of Electrical and Electronics Engineering Assiut UniversityAssiutEgypt

Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research *** studies have focused on recognizing diseases from images of whole *** approach limits the resulting models’ability to estimate leaf disease severity or identify multiple anomalies occurring on the same *** studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition *** those studies,however,the lesions were laboriously cropped by *** study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this *** datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network(CNN)models,*** report that GoogLeNet’s disease recognition accuracy improved by more than 15%when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves.A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this *** proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union(mIoU)score of 0.8448 and 0.6257 for the leaf and lesion pixel classes,*** terms of mean boundary f1 score,the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes,***,a fully automatic algorithm for leaf disease recognition from individual lesions is *** algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease *** proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.

关键词： Deep learning Precision agriculture Leaf disease recognition Complex background removal Leaf image segmentation Lesion classification

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The development of a high-performance Ni-superalloy additively manufactured heat pipe

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Advances in Manufacturing 2022年第4期10卷 610-624页

作者： Sheng Li Khamis Essa James Carr States Chiwanga Andrew Norton moataz M.Attallah School of Electromechanical Engineering Guangdong University of TechnologyGuangzhou 510006People’s Republic of China School of Engineering University of BirminghamEdgbastonBirmingham B152TTUK Henry Moseley X-ray Imaging facility Henry Royce Institute for Advanced MaterialsDepartment of MaterialsUniversity of ManchesterManchester M139PLUK European Thermodynamics Ltd 8 Priory Business ParkWistow RoadKibworthLeicestershire LE80RXUK Rolls-Royce plc P.O.Box 31Derby DE248BJUK School of Metallurgy and Materials University of BirminghamEdgbastonBirmingham B152TTUK

Additively manufacturing(AM)has been used to manufacture fine structures with structured/engineered porosity in heat management *** this study,laser powder bed fusion(LPBf)was used to manufacture a high-performance Ni-superalloy heat pipe,through tailoring LPBf process parameters to fabricate thin wall and *** using novel laser scanning strategies,wick structure heat pipes with maximised surface-area-to-volume ratio,fine features size around 100µm,and controlled porosity were successfully *** and X-ray microtomography(micro-CT)were used to investigate the 3D structure of the void space within the *** test results showed that most of the heat pipes made by LPBf had better performance than the conventionally manufactured *** study also investigated the influences of the process parameters on the porosity volume fraction and the feature *** results showed that LPBf process could fabricate thin structure due to the change of melt pool contact *** relationship between process parameters and bead size reported in this study could help design and manufacture heat pipe with complex fine structure.

关键词： Laser powder bed fusion(LPBf) Heat pipe Melt pool Microtomography(micro-CT)

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Recent advances in image processing techniques for automated leaf pest and disease recognition – A review

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Information Processing in Agriculture 2021年第1期8卷 27-51页

作者： Lawrence C.Ngugi moataz Abelwahab Mohammed Abo-Zahhad Department of Electronics and Communication Engineering Egypt-Japan University of Science and TechnologyP.O.Box 179New Borg El-Arab CityPostal Code 21934AlexandriaEgypt Department of Electrical and Electronics Engineering Assiut UniversityP.O.Box 71515AssiutEgypt

fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable ***,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme ***,this is expensive,time consuming and in some cases *** counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research *** this paper,we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning *** hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing *** particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB *** report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted *** have reported high recognition accuracies on particular datasets but in many cases,the performance of those systems deteriorated significantly when tested on different datasets or in field ***,progress made so far has been *** results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy,recall,precision,specificity,f1-score,training duration and storage requirements are also ***,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing *** also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.

关键词： Precision agriculture Machine learning Plant disease recognition Image processing Convolutional neural networks

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