Intelligent Learning Algorithms for Active Vibration Control

YesThis correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments

Intelligent Active Vibration Control for a Flexible Beam System

YesThis paper presents an investigation into the development of an intelligent active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed on the basis of optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used for AVC system design. The system is then implemented, tested and its performance assessed for GAs and ANFIS based design. Finally a comparative performance of the algorithm in implementing AVC system using GAs and ANFIS is presented and discussed through a set of experiments

Automatic Face and Hijab Segmentation Using Convolutional Network

Taking pictures and Selfies are now very common and frequent between people. People are also interested in enhancing pictures using different image processing techniques and sharing them on social media. Accurate image segmentation plays an important role in portrait editing, face beautification, human identification, hairstyle identification, airport Surveillance system and many other computer vision problems. One specific functionality of interest is automatic face and veil segmentation as this allows processing each separately. Manual segmentation can be difficult and annoying especially on smartphones small screen. In this paper, the proposed model uses fully convolutional network (FCN) to make semantic segmentation into skin, veil and background. The proposed model achieved an outperforming result on the dataset which consists of 250 images with global accuracy 92% and mean accuracy 92.69

Hepcidin and iron status in chronic kidney disease

Hepcidin is a critical inhibitor of iron export frommacrophages, enterocytes, and hepatocytes. Given that itis filtered and degraded by the kidney, its elevated levelsin renal failure have been suggested to play a role in thedisordered iron metabolism of uremia. It is a smalldefensin-like peptide whose production by hepatocytes ismodulated in response to anemia, hypoxia, orinflammation. Hepcidin could also act as an indicator offunctional iron deficiency (FID) in chronic kidneydisease (CKD) patients. This study was performed toassess hepcidin and its correlations with renal function,iron status parameters {serum iron, serum ferritin,transferrin saturation (TSAT) and soluble transferrinreceptor (sTfR)}, inflammatory cytokines (IL-6&IFN-?)and inflammatory markers (CRP) in patients with CKDeither on conservative treatment or on maintenancehemodialysis (HD). Serum prohepcidin was higher inHD patients compared to controls and CKD patients. Inthe whole patient group, serum hepcidin correlatedsignificantly with hemoglobin (Hb), IL-6, creatinine,CRP, sTfR and urinary hepcidin. In HD groupprohepcidin correlated significantly with creatinine.Multiple regression analysis showed that prohepcidinwas most predicted by serum creatinine and CRP.Elevated prohepcidin levels in HD patients studied couldmainly be due to its accumulation in impaired renalfunction in addition to low-grade inflammation,frequently encountered in this population

The role of three different contrast-enhanced, abbreviated MRI protocols as a screening tool of hepatocellular carcinoma in patients with chronic hepatitis C virus infection

Purpose: Our study aims to assess the role and diagnostic performance of 3 different contrast-enhanced, abbreviated magnetic resonance imaging (MRI) protocols as a screening tool of hepatocellular carcinoma (HCC). Material and methods: Our retrospective study included 80 patients who were screened for HCC: 47 patients revealed 138 focal hepatic lesions. MRI examinations were performed including full CE-MRI protocols. The MRI was done on a 1.5 T machine. Then 3 different abbreviated contrast-enhanced MRI protocols were analysed separately. The standard dynamic contrast MRI and abbreviated protocols were evaluated following the LI-RADS 2018 lexicon diagnostic features. Results: A considerable overall kappa (k) agreement between the abbreviated 1, 2, and 3 protocols on LI-RADS classification was noted with k = 0.865. There was almost perfect agreement between all abbreviated protocols and full standard protocol on LI-RADS classification, with k = 0.890. As regards the k agreement on LI-RADS classification, there was a considerable highest agreement between the abbreviated 1 protocol and the full standard protocol, with k = 0.980. The abbreviated 1 and 2 protocols showed high diagnostic performance on LI-RADS classification of lesions, with 100% sensitivity, specificity, PPV, NPV, and accuracy, while the abbreviated 3 protocol showed a lesser but comparable sensitivity 96.9%, NPV 99.4, and accuracy 99.4%. Conclusions: Abbreviated contrast-enhanced MRI protocols can be used as a screening tool for the detection of HCC, with high sensitivity, specificity, PPV, NPV, and accuracy close to the full protocol. There was a considerable highest agreement between the abbreviated 1 protocol and the full standard protocol. Subsequently, this protocol can be used as a standard protocol for screening high-risk patients

Public realm for enhanced quality of Life :Interactive spaces combating climate change

Currently, facing climate change stands as a crucial focus within architecture, urban planning and design .Urban design is constructive endeavor aimed at benefiting the public. The public realm that creates the environment for individuals and communities to effectively, safely, meaningfully and permanently lead their lives .For a prolonged duration the public realm have been formulated to accommodate severe climatic conditions .the reality persists that the essence of a signification public realm continue to be widely disregarded . The aim is to clarify how exceptional quality, effectively designed, and well-managed approaches can be beneficial to implement as a strategy for climate change adaptation and environmental resilience as well as rising the standard of quality of life. based on the literatures analysis .both empirical and theoretical studies as well as developing the concepts of adaptive reorganization of the existing public spaces using landscape design ,in an effort to improve the quality of life and to tackle climate change . As a result, the paper concludes that the focus on integrating elements of urban planning and design for public realm ,as well as the aesthetic and visual quality ,demonstrates how adaptation to climate change can become a catalyst for change .by transforming challenges into resources and creating value, it plays a significant role in enhancing the quality of open spaces . Additionally, an increase in interaction with the space was observed, including motivation for engaging in activities and a sense of happines

Real-time system identification using intelligent algorithms

This research presents an investigation into the development of real time system identification using intelligent algorithms. A simulation platform of a flexible beam vibration using finite difference (FD) method is used to demonstrate the real time capabilities of the identification algorithms. A number of approaches and algorithms for on line system identifications are explored and evaluated to demonstrate the merits of the algorithms for real time implementation. These approaches include identification using (a) traditional recursive least square (RLS) filter, (b) Genetic Algorithms (GAs) and (c) adaptive Neuro_Fuzzy (ANFIS) model. The above algorithms are used to estimate a linear discrete second order model for the flexible beam vibration. The model is implemented, tested and validated to evaluate and demonstrate the merits of the algorithms for real time system identification. Finally, a comparative performance of error convergence and real time computational complexity of the algorithms is presented and discussed through a set of experiments

Using recycled materials towards sustainable, eco-friendly building in hot regions – A Review

Due to population growth and increased urban activity, there is a rise in waste production, which poses health and environmental risks. Additionally, the extraction and transportation of natural resources to places of employment require significant energy consumption, and the world has therefore moved to seek new alternatives away from traditional construction methods by recycling building materials such as concrete, brick, glass, wood, etc., or using new building materials such as rice straw, plastics, corks, etc., taking into account architectural design. The research is aimed at demonstrating that recycled materials have the potential to perform a better environmental performance by preserving the environment and the natural resources used to produce construction materials and providing energy of all kinds, as well as reducing costs

Wound Healing Activity of Brown Algae plus Polyherbal Extract in Normal and Alloxan-induced Diabetic Rats

The aim of the present study was to assess the wound healing efficacy of the experimental medicinal extract mixture (EMEM) composed of brown algae, Cystoseira trinodis, plus three medicinal plants (garlic, liquorice and ginger). The extract was applied on induced excised wounds in rats, either healthy or induced hyperglycemia by alloxan. Healing activity was evaluated for up to 21 days after wounding in terms of decrease in surface area of wound, percentage of wound closure and tissue regeneration using digital planimetry analysis and histopathological examination. The study revealed that topical application of the extract improved and enhanced the healing process in diabetic rat model when compared with diabetic non-treated rats. The extract promoted wound contraction, reduced the wound closure time and induced proliferation of fibroblast as well as angiogenesis and re-epithelialization

Stellar Image Interpretation System using Artificial Neural Networks: Unipolar Function Case

An artificial neural network based system for interpreting astronomical images has been developed. The system is based on feed-forward Artificial Neural Networks (ANNs) with error back-propagation learning. Knowledge about images of stars, cosmic ray events and noise found in images is used to prepare two sets of input patterns to train and test our approach. The system has been developed and implemented to scan astronomical digital images in order to segregate stellar images from other entities. It has been coded in C language for users of personal computers. An astronomical image of a star cluster from other objects is undertaken as a test case. The obtained results are found to be in very good agreement with those derived from the DAOPHOTII package, which is widely used in the astronomical community. It is proved that our system is simpler, much faster and more reliable. Moreover, no prior knowledge, or initial data from the frame to be analysed is required