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Carrot juice is a critical source of vitamins, selenium, and β-carotene, which is suggested to protect from Rheumatoid Arthritis (RA). The present study aimed to show the effect of carrot juice supplementation compared to selenium tablet supplementation, so our study includes (44) blood samples belonging to young men with RA. All patients aged (20-45) years, 44 blood samples were obtained before treatment at week (0), twenty-four men with RA were supplied with fresh carrot juice. The other group of 20 patients was given an artificial selenium supplement for 21 days as well, and the results were analyzed.The samples were collected from Kirkuk hospital, and external specialized clinical from October/2019 to September /2020; experimental groups were divided into three groups: Group 1 : (44) men Rheumatoid arthritis (RA) before treatment, Group 2: (24) men with RA+ Carrot juice, Group 3: (20) men with RA +Selenium Tab, We reach to following results: the patients who have RA consumption Carrot juice and patients take up Se tab. show significant decrease respectively in RBCs, WBCs, ERS, and RF compared with the Rheumatoid arthritis group, so as the results show a significant decrease in Leptin, IL-6, C-Reactive Protein, and TNF-α concentrations in comparison with the Rheumatoid arthritis group. In contrast, we found a significant increase in GSH, Selenium concentrations, and VD3 in men with RA administration carrot juice and patients' consumption Se tab. Respectively compared with the RA group and, finally, our finding shows no difference in Ceruloplasmin in experimental groups.

Simple and rapid synchronous fluorometric methods were adopted and validated for the simultaneous analysis of a binary mixture of diphenhydramine (DIP) and ibuprofen (IBU) (Mix I) or DIP and phenylephrine (PHE) (Mix II) in their co-formulated pharmaceuticals without prior separation. Analysis of Mix I is based on the measurement of the peak amplitudes (D1 ) of synchronous fluorescence intensities at 265.1 nm for DIP and 260 nm for IBU. The relationship between the concentration and the amplitude of the first-derivative synchronous fluorescence spectra showed good linearity over the concentration ranges 0.50-10.00 μg ml-1 and 0.50-7.90 μg ml-1 for DIP and IBU, respectively. Analysis of Mix II was based on measurement of the peak amplitude (D1 ) synchronous fluorescence intensities at 230 nm for DIP and at 253.9 nm for PHE. Moreover, for Mix II, the peak amplitude (D2 ) synchronous fluorescence intensities were measured at 227.9 nm for DIP and at 264.9 nm for PHE. Calibration plots were rectilinear over the concentration range 0.30-3.50 μg ml-1 and 0.03-0.75 μg ml-1 for DIP and PHE, respectively. The proposed methods were successfully applied to determine the studied compounds in pure form and in pharmaceutical preparations.

With the complex structure of planktonic species and an immense amount of data captured from autonomous underwater vehicles (AUVs), a large burden is placed on the domain experts for plankton taxa labeling. At the same time, the most prominent machine learning (ML) methods for classification rely heavily on a massive amount of labeled datasets to create and train neural network classifier models that perform their tasks accurately. Active Learning (AL) is an ML paradigm that reduces this manual effort by proposing algorithms that support the construction of the training datasets, thus enlarging the sets while minimizing human involvement. To build the training set, AL methods apply heuristics to select a subset of images, i.e., samples, from the entire data. The selected samples that capture the common statistical patterns or feature space are likely to include all the information needed for the training and the learning processes. In addition, the algorithm should prioritize samples that are likely belonging to multiple classes, i.e., having close inter-class boundaries, and might lead to model confusion. Many of the current AL approaches fail to incorporate both types of samples representing the statistical pattern and the samples in which the particular machine learning model is uncertain about. In this paper, we extend our framework which addresses these challenges with an augmentation module to increase the robustness of the model and ensure its adaptability to the planktonic domain. We compare the framework with existing hybrid AL techniques and test an adaption of our extended framework on the planktonic domain. The empirical results from the experiments exerted in this paper confirm higher accuracy achieved by the new extended framework.

Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, require making valid and solid decisions using one or a combination of robust reasoning strategies, algorithms, and robustness analysis. However, in dynamic environments, data can be incomplete, skewed, contradictory, and redundant impacting the reasoning. Basing decisions on these data can lead to inconsistent, irrational, and unreasonable cyber-physical systems’ movements, adversely impacting the system’s reliability and integrity. This paper presents the assessment of robust reasoning for autonomous cyber-physical systems in dynamic environments. In this work, robust reasoning is considered as 1) the capability of drawing conclusions with available data by applying classical and non-classical reasoning strategies and algorithms and 2) act and react robustly and safely in dynamic environments by employing robustness analysis to provide options on possible actions and evaluate alternative decisions. The result of the research shows that different common existing strategies, algorithms and analyses can be provided together with a comparison of their applicabilities, benefits, and drawbacks in the context of cyber-physical systems operating in dynamically changing environments. The conclusion is that robust reasoning in cyber-physical systems can handle dynamic environments. Moreover, combining these strategies and algorithms with robustness analysis can support achieving robust behavior in autonomous cyber-physical systems while operating in dynamically changing environments.

Plankton species are of vital importance to the marine food chain. They are susceptible to minor changes in their environment, which can lead to rapid and devastating changes in the global ecosystem. Thus, monitoring plankton species and their population dispersion is crucial to understanding the dynamics of their community abundance as well as their consumers in higher trophic levels. Technological advancements of systems providing high-resolution imaging augmented by powerful computing devices made it possible to infer the distribution from sampling millions of planktonic images at low cost. Yet, this requires an extensive and time consuming manual labeling effort. The process of training to distinguish different species on manually labeled data is called supervised learning. The objective of this paper is to find new algorithms capable of minimizing the training supervision and assisting in discovering unseen classes. We explore the use of unsupervised classes of models for in-situ classification and identification of planktonic images. The aim is to embed those models into existing robotic imaging platforms to enhance the classification ability and to allow the discovery of new classes without any prior knowledge or exhaustive labeling effort. This work compares different models and shows their abilities to learn essential data structures over the National Science Bowl planktonic dataset.

Planktonic organisms form the principal source for consumers on higher trophic levels in the food chain. Studying their community dispersion is vital to our understanding of the planet's ecological systems. With the recent technological advancements in imaging systems, capturing images of planktons in-situ is made possible by embedding mobile underwater robots with sophisticated camera systems and computing power that implement deep machine learning approaches. Efforts of applying deep learning methods to plankton imaging systems have been limited to classification, while detection and segmentation has been left to traditional methods in this context. There is a variety of publicly available datasets made suited for planktonic species classification. These datasets consist of images of individual species. Thus, they do not represent the actual environment, which is usually given by a scene representation more suited for object localization, detection and semantic segmentation. In this paper we propose a novel custom dataset [1] from planktonic images captured in-situ in a lab environment suited for supervised learning of object detection and instance segmentation. The data is tested in experiments using the state-of-the-art deep learning visual recognition method of Mask R-CNN. The experiment results show the potential of this method and create a baseline analysis module for real-time in-situ image processing. We provide a comparison of how the method is performing when trained on automatically processed and annotated images from existing segmentation frameworks using traditional methods. This comparison illustrates the importance of utilizing proper data and the potential for success if provided11All results, code and metrics used for the experiments are provided in: https://github.com/AILARON/Segmentation.

Planktons are vital to our planet's ecosystems. Mapping and monitoring planktonic-distribution in the ocean is of great importance in understanding these ecosystems as well as gaining insights on the general health of our planet. The task of identifying different planktonic species and their concentrations is a critical yet challenging part of this endeavor. In this paper, we explore the utilization of one-shot classifiers on in-situ captured planktonic images. The similarity-based classification method used by the one-shot classification algorithms gives rise to various research opportunities outside of the typical classification paradigm. Exploring the development of a proficient plankton image classification framework that can determine how different or how similar two plankton specimens are, can inform us if they belong to the same species, if they are different variations of the same species or if we have encountered a completely unseen classes of plankton. This similarity is valuable information that assists in further mapping the diversity of the planktonic species. We further extend the Siamese one-shot classifier with few-shot classifier to improve the model performance. Empirical evaluations of the new extension yield promising results.