Novel photoreceptor cells, pupillometry and electrodiagnosis in orbital, vitreo-retinal and refractive disorders

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Empirical Evidence of Co-Movement between the Canadian CDS, Stock Market And TSX 60 Volatility Index: A Wavelet Approach

Purpose- The prime objective of this study was to find the co-movement between the Canadian credit default swaps market, the Stock market and volatility index (TSX 60 Index) Design/ Methodology- To achieve this purpose, daily data containing 2870 observations starting from the 1st of January 2009 to the 30th of December 2019 were analyzed. This study employed the wavelet approach to present results in short-term, medium-term, long-term, and very long time. Findings- The findings of this study showed a negative correlation between the CDS market, stock market, and the TSX 60 index in the short-term as well as in the long-term term, while in medium-term and very long-term period correlation is strongly positive. The wavelet co-movement results in the short-term and long-term were negative, while this relationship in the medium-term and very long-term period was strongly positive. Practical Implications- This research provides simultaneous valuable information for investment decisions in the short, medium, and long term time horizons, as well as for the policymakers in the Canadian credit default swaps market, stock market, and the volatility index (TSX 60 Index)

Susceptibility of Continual Learning Against Adversarial Attacks

Recent continual learning approaches have primarily focused on mitigating catastrophic forgetting. Nevertheless, two critical areas have remained relatively unexplored: 1) evaluating the robustness of proposed methods and 2) ensuring the security of learned tasks. This paper investigates the susceptibility of continually learned tasks, including current and previously acquired tasks, to adversarial attacks. Specifically, we have observed that any class belonging to any task can be easily targeted and misclassified as the desired target class of any other task. Such susceptibility or vulnerability of learned tasks to adversarial attacks raises profound concerns regarding data integrity and privacy. To assess the robustness of continual learning approaches, we consider continual learning approaches in all three scenarios, i.e., task-incremental learning, domain-incremental learning, and class-incremental learning. In this regard, we explore the robustness of three regularization-based methods, three replay-based approaches, and one hybrid technique that combines replay and exemplar approaches. We empirically demonstrated that in any setting of continual learning, any class, whether belonging to the current or previously learned tasks, is susceptible to misclassification. Our observations identify potential limitations of continual learning approaches against adversarial attacks and highlight that current continual learning algorithms could not be suitable for deployment in real-world settings.Comment: 18 pages, 13 figure

Computational modeling of animal behavior in T-mazes: Insights from machine learning

This study investigates the intricacies of animal decision-making in T-maze environments through a synergistic approach combining computational modeling and machine learning techniques. Focusing on the binary decision-making process in T-mazes, we examine how animals navigate choices between two paths. Our research employs a mathematical model tailored to the decision-making behavior of fish, offering analytical insights into their complex behavioral patterns. To complement this, we apply advanced machine learning algorithms, specifically Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid approach involving Principal Component Analysis (PCA) for dimensionality reduction followed by SVM for classification to analyze behavioral data from zebrafish and rats. The above techniques result in high predictive accuracies, approximately 98.07% for zebrafish and 98.15% for rats, underscoring the efficacy of computational methods in decoding animal behavior in controlled experiments. This study not only deepens our understanding of animal cognitive processes but also showcases the pivotal role of computational modeling and machine learning in elucidating the dynamics of behavioral science

The technical mediator in contemporary Iraqi formation

 The current research (technical mediator in contemporary Iraqi painting) dealt with the concept of technical mediator in contemporary arts and its role in the qualitative transformation in art, the diversity of discoveries in technical media that artists use in their artistic achievement, and the extent of their impact on contemporary Iraqi painting, where the research problem was identified (what is The nature and role of technical media in showing works in contemporary Iraqi painting) and (What are the intellectual and aesthetic concepts of media and techniques as references for contemporary art in Iraq). The second topic dealt with technical media and their impact on the plastic arts, and the third topic focused on the technical medium and the mechanisms of its manifestation in contemporary Iraqi painting, and the third chapter, which included samples of the sample numbered (3), was a model representing the work of three Iraqi artists, while the fourth chapter included the results of the research. And conclusions, and sealed by the researcher sources and references. Conclusions: - 1. Achieving visual creativity in artistic works by experimenting with contemporary media and techniques. 2. The clear influence of contemporary Western techniques, in the use of new non-circulated media, as a result of constant friction with the latest prevailing artistic trends. 3. Contemporary media and techniques are clearly influential in the Iraqi plastic achievement through the dismantling of forms, giving expression a dramatic dimension, and modifying the media from its relations to become self-sufficient, starting towards innovation and singularity in the constructivist style. 4. The Iraqi artist was tempted to experiment according to contemporary propositions, after he was tired of the traditional ideas and methods, which have become stagnant in a vicious circle. 5. The works of the three artists (Kuish, Abdeen, Bilal) were distinguished by the freedom to choose the appropriate media for their work, and to try to dialogue with the other through multimedia to convey the suffering of his people and the crises they are going throug

Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images

The ongoing COVID-19 pandemic has already taken millions of lives and damaged economies across the globe. Most COVID-19 deaths and economic losses are reported from densely crowded cities. It is comprehensible that the effective control and prevention of epidemic/pandemic infectious diseases is vital. According to WHO, testing and diagnosis is the best strategy to control pandemics. Scientists worldwide are attempting to develop various innovative and cost-efficient methods to speed up the testing process. This paper comprehensively evaluates the applicability of the recent top ten state-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically detecting COVID-19 infection using chest X-ray images. Moreover, it provides a comparative analysis of these models in terms of accuracy. This study identifies the effective methodologies to control and prevent infectious respiratory diseases. Our trained models have demonstrated outstanding results in classifying the COVID-19 infected chest x-rays. In particular, our trained models MobileNet, EfficentNet, and InceptionV3 achieved a classification average accuracy of 95\%, 95\%, and 94\% test set for COVID-19 class classification, respectively. Thus, it can be beneficial for clinical practitioners and radiologists to speed up the testing, detection, and follow-up of COVID-19 cases

Enzyme inhibition and antibacterial potential of 4-Hydroxycoumarin derivatives

The 4-Hydroxycoumarin derivatives are known to show a broad spectrum of pharmacological applications. In this paper we are reporting the synthesis of a new series of 4-Hydroxycoumarin derivatives synthesized through Knovenegal condensation; they were characterized by using UV-Vis, FT-IR, NMR spectroscopies. The synthesized compounds were evaluated for antibacterial activity against Staphylococcus aureus and Salmonella typhimurium strains. The compounds (2), (3) and (8) showed favorable antibacterial activity with zone of inhibitions 26.5± 0.84, 26.0 ± 0.56 and 26.0 ± 0.26 against Staphylococcus aureus (Gram-positive) respectively. However, the compounds (5) and (9) were found more active with 19.5 ± 0.59 and 19.5 ± 0.32 zone of inhibitions against Salmonella typhimurium (Gram-negative). Whereas, in urease inhibition assay, none of the synthesized derivatives showed significant anti-urease activity; although, in carbonic anhydrase-II inhibition assay, the compound (2) and (6) showed enzyme inhibition activity with IC50 values 263±0.3 and 456±0.1, respectively

Integrated hybrid Raman/fiber Bragg grating interrogation scheme for distributed temperature and point dynamic strain measurements

We propose and experimentally demonstrate the feasibility of an integrated hybrid optical fiber sensing interrogation technique that efficiently combines distributed Raman-based temperature sensing with fiber Bragg grating (FBG)-based dynamic strain measurements. The proposed sensing system is highly integrated, making use of a common optical source/receiver block and exploiting the advantages of both (distributed and point) sensing technologies simultaneously. A multimode fiber is used for distributed temperature sensing, and a pair of FBGs in each discrete sensing point, partially overlapped in the spectral domain, allows for temperature-independent discrete strain measurements. Experimental results report a dynamic strain resolution of 7.8  nε/√Hz within a full range of 1700 με and a distributed temperature resolution of 1°C at 20 km distance with 2.7 m spatial resolution