NanoPhotonic structures for biosensing applications

Photonics -“ science of optics“ - has become one of the emerging sciences in many applications nowadays. The study of light interaction with matter has opened a lot of interesting phenomena that differ in their applications including sensing, modulation, demultiplexing, etc. Sensing applications represent a major part in the photonics field owing to their crucial role in the detecting and diagnosis of diseases in many medical applications. On the other hand, gas sensing is considered an important application in many industrial centers. During the manufacturing of several products, toxic gases may be generated and hence the ability to detect such types of gases becomes a necessity. The first part of this thesis is concerned with sensing applications using plasmonic and photonic structures. Several plasmonic and photonic structures are proposed that are characterized by their ultimate sensitivity and high performance. Other parameters are taken into consideration like the CMOS compatibility of our design and the possibility of being integrated with electronic chips. Beside optical sensing and their important role in biomedical and environmental applications, optical demultiplexers are considered from the main blocks in different communication systems that are based on wavelength division multiplexing (WDM). The need to highly select certain wavelength to carry the data during transmission is increasing. In the second part of the thesis, the design methodology of an optical filter is discussed. The optical filter can fit into many applications including demultiplexing and sensing. An optical demultiplexer is proposed and characterized by its high selectivity of wavelength in the near-infrared range to fit with the telecommunication systems. In addition, the transmission levels are of an acceptable range to ensure high signal to noise ratio. 9 The third and the last part of the thesis is concerned with optical coupling from free-space to guided structures. In the last part, an optical grating coupler is proposed that is characterized by its high transmission levels. The grating coupler couples the light from free-space to a shallow waveguide with a narrow lateral dimension. Such system can fit in many applications including sensing and modulation applications

Induced Pipeline Voltage Near-by Hybrid Transmission Lines

The aim of this paper is to calculate the induced voltage, nearby hybrid transmission lines. Two hybrid transmission lines are simulated and modeled. The first line is double-circuit operating with ac voltage of 220 kV and bipolar circuit with a dc voltage of ±500 kV, while the second line is with flat configuration operating at 500 kV ac and of ±500 kV. The induced voltage on the pipeline is calculated due the mutual effect of the electric field of these lines. The charge simulation technique and COMSOL computer package are used for calculating the electric field underneath the hybrid lines with and without pipelines and are used to determine the induced voltage on the pipelines. The maximum induced voltage is 9.5 kV for the first line at spacing (S) of 10 m and DC circuit height of 15.7 m against 4.3 kV for height of 24.9 m. The corresponding values for the second line at the same spacing S of 10 m are 7.08 and 3.1 kV for DC circuit heights of 19.1 m and 30 m respectively. The maximum induced voltage on the pipeline is slightly changed due to changing the phase sequence of the AC circuit and the spacing between AC and DC circuits. Keywords: Induced Voltage, – Electric Fields, HVDC Transmission, Finite Element Method, Hybrid Transmission Lines

Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery

Natural protected areas are vital for biodiversity, climate change mitigation, and supporting ecological processes. Despite their significance, comprehensive mapping is hindered by a lack of understanding of their characteristics and a missing land cover class definition. This paper aims to advance the explanation of the designating patterns forming protected and wild areas. To this end, we propose a novel framework that uses activation maximization and a generative adversarial model. With this, we aim to generate satellite images that, in combination with domain knowledge, are capable of offering complete and valid explanations for the spatial and spectral patterns that define the natural authenticity of these regions. Our proposed framework produces more precise attribution maps pinpointing the designating patterns forming the natural authenticity of protected areas. Our approach fosters our understanding of the ecological integrity of the protected natural areas and may contribute to future monitoring and preservation efforts

THE RELATIONSHIP BETWEEN INTRUSIVE COGNITIONS AND DEFENSE MECHANISMS IN HEALTHY AND CLINICAL POPULATIONS

Purpose: to examine the relationship between defense mechanisms and intrusive cognitions in normal healthy individuals and psychiatric patients. Methodology: The study sample consists of a healthy group (n=60; 30 males & 30 females), whereas the clinical group (n=66; 34 males, 32 females) includes patients with major depressive disorder (12 patients, 5 males, 7 females), schizophrenia (31 patients; 14 males, 17 females), obsessive-compulsive disorder (23 patients; 15 males, 8 females). We used several scales to measure the following variables: intrusive cognitions, intrusive memories, and defense mechanisms. Finding: The results show that there is a positive correlation between defense mechanisms and intrusive cognitions in healthy and clinical groups. Intrusive cognitions were more common in the patient than in a healthy group. Furthermore, there was no significant difference between males and females in measures of intrusive thoughts and memories in both groups. Implications: These findings have implications for behavioral treatment. Treatments used for managing posttraumatic stress disorder can also be used for the treatment of a major depressive disorder, OCD, and schizophrenia. Originality: This investigation the relationship between intrusive cognitions and defense mechanisms in healthy and clinical populations and its implication on the cue exposure therapy that can be the treatment of intrusive cognitions and thoughts in with major depressive disorder, OCD, and schizophrenia

Effects of gamma irradiation on salted and frozen mullet fish during storage period

The objective of the present study was to investigate the influence of gamma irradiation on decontamination of microbial loud of salted and frozen mullet fish. Mullet fish samples were divided into two groups: The first group was frozen at – 20C° and the second group was dry salted with ordinary commercial refined salt at ratio 1 kg fish / 0.8 kg. Each group was subjected to 0, 2.5 and 5 kGy of gamma irradiation doses before storage period. The results showed that, gamma irradiation hadn’t any significant (P?0.05) effects on the chemical composition and minerals content of mullet fish samples under investigation. Gamma irradiation doses (2.5 and 5.0 kGy) had not significant remarkable effect at P?0.05 on sensory properties (appearance, odor, texture and color) of dry and frozen mullet fish samples neither at zero time nor during storage periods. Microbial aspects (total bacterial counts, coliform bacteria, salmonella, sporeforming bacteria and total molds and yeasts) were detected. Also it could be concluded that, gamma irradiation had a significant (P?0.05) decrease effects on microbial loud and improve the microbiological safety of salted and frozen mullet fishes without any adverse effects on their chemical contents and quality. Keywords: key words, Gamma irradiation, Salting, freezing, Mullet fish, Minerals, Microbial loud

Hematological Indices in Chronic Kidney Disease Patients and The Effect of Hemodialysis on These Indices

Background: Chronic kidney disease (CKD) is a major health issue that affects people all over the globe. Hematological problems are connected to varying grades of CKD. Objective: To study the relationship between chronic kidney disease and hematological indices, and to study the effect of hemodialysis on these parameters. Subjects and Methods: At Internal Medicine, Faculty of Medicine, Zagazig University, Sharkia, Egypt. we conducted this cross-sectional study in addition to the Nephrology Unit of El-Sahel teaching hospital from May 2019 to February 2020 on 165 subjects categorized into three groups with each group of 55 subjects. Group I included normal healthy persons. Group II involved chronic kidney disease patients not on hemodialysis (NDD-CKD) while Group III included patients on maintenance hemodialysis (DD-CKD). All participants' histories were gathered, with particular attention paid to demographic data and the co-morbid medical conditions as diabetes mellitus and hypertension. Full clinical examination was done including local and systemic examinations. In addition, a complete blood count, urea, and creatinine levels were measured. Results: There is a significant association between CKD and changes in RBCs indices with a significant effect of hemodialysis on these changes. A decrease in the mean platelet count in diseased groups compared with normal ones was revealed. We also found differences between the 3 groups according to WBCs indices with significant changes between the 3 groups in TLC. Conclusion: CKD impacts all hematological parameters and hemodialysis also influence all these parameters

Status and origin of Egyptian local rabbits in comparison with Spanish common rabbits using mitochondrial DNA sequence analysis

[EN] Mitochondrial DNA (mtDNA) and cytochrome b (cyt b) gene sequences were used to determine the status of genetic diversity and phylogeny for 132 individuals from local rabbit breeds in Egypt and Spain. The Egyptian local rabbit breeds were Egyptian Red Baladi (ERB), Egyptian Black Baladi (EBB) and Egyptian Gabali Sinai (EGS). However, the Spanish local rabbit breed was Spanish common rabbit (SCR). Previous breeds were compared with European Wild Rabbit taken from Albacete, Spain (EWR). A total of 353 mutations, 290 polymorphic sites, 14 haplotypes, 0.06126 haplotype diversity and –1.900 (P<0.05) for Tajima’s D were defined in this study. Haplotype A mostly occurred in 83.3% of Egyptian rabbits and 11.7 % of EWR, while haplotype B occurred in 63.8% of Spanish rabbits and 36.2% of the EGS breed. A total of 47 domestic and wild Oryctolagus cuniculus published sequences were used to investigate the origin and relation among the rabbit breeds tested in this study. The most common haplotype (A) was combined with 44.7% of published sequences. However, haplotype B was combined with 8.5%. Haplotypes of Egyptian, SCR and EWR were scattered in cluster 1, while we found only one EGS haplotype with two haplotypes of EWR in cluster 2. Our results assumed that genetic diversity for ERB, EBB and SCR was very low. Egyptian breeds and SCR were introduced from European rabbits. We found that ERB and EBB belong to one breed.Emam, AM.; Afonso, S.; González-Redondo, P.; Mehaisen, G.; Azoz, A.; Ahmed, N.; Fernand, N. (2020). Status and origin of Egyptian local rabbits in comparison with Spanish common rabbits using mitochondrial DNA sequence analysis. World Rabbit Science. 28(2):93-102. https://doi.org/10.4995/wrs.2020.12219OJS93102282Abrantes J., Areal H., Esteves P.J. 2013. Insights into the European rabbit (Oryctolagus cuniculus) innate immune system: genetic diversity of the toll-like receptor 3(TLR3) in wild populations and domestic breeds. 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A Convolutional Neural Network for Automatic Brain Tumor Detection

Magnetic resonance imaging (MRI) combined with artificial intelligence (AI) algorithms to detect brain tumors is one of the important medical applications.  In this study, a Convolutional neural network (CNN) model is proposed to detect meningioma and pituitary, which was tested with a dataset consisting of two categories of tumors with 1,800 MRI images from several persons. The CNN model is trained via a Python library, namely TensorFlow, with an automatic tuning approach to obtain the highest testing accuracy of tumor detection. The CNN model used Python programming language in Google Colab to detect sensitivity, precision, the area under the PR and receiver operating characteristic (ROC), error matrix, and accuracy. The results show that the proposed CNN model has a high performance in the detection of brain tumors. It achieves an accuracy of 95.78% and a weighted average precision of 95.82%

Edge-based Compression and Classification for Smart Healthcare Systems: Concept, Implementation and Evaluation

Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the net- work edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart health- care systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make au- tonomous decisions for smart health applications. In particular, we present and im- plement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low com- putational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%