Priority-based joint EDF–RM scheduling algorithm for individual real-time task on distributed systems

The Journal of Supercomputing - Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise...     

Modeling and control of a finger-like mechanism using bending shape memory alloys

Microsystem Technologies - In this research a biologically inspired finger-like mechanism similar to human musculoskeletal system is developed based on Shape Memory Alloys (SMAs). SMA actuators are...     

Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization

Neural Computing and Applications - Cryptography often involves substituting (and converting) the secret information into dummy data so that it could reach the desired destination without leakage....     

A systematic review on emperor penguin optimizer

Neural Computing and Applications - Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold....     

Feature Selection and Extraction Methods for Power Systems Transient Stability Assessment Employing Computational Intelligence Techniques

One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia’s power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.     

Effects of Solvent Exchange Period and Heat Treatment on Physical and Chemical Properties of Rice Husk Derived Silica Aerogels

In this study, hydrophobic silica aerogels were synthesized from rice husk ash-derived sodium silicate through sol-gel processing, solvent exchange, surface modification and ambient pressure drying. By volume, 10% of trimethylchlorosilane (TMCS) in 90% of n-hexane was used as a hydrophobic solution in the surface modification process. The physical and chemical properties of silica aerogels were characterized by density and porosity measurements, scanning electron microscopy (SEM), Fourier transforms infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller theory (BET) and dynamic scanning calorimetry (DSC). The hydrogels prepared were in the form of 2.5 ± 0.5 mm beads and then converted into alcogels through solvent exchange with ethanol for repetition of 3, 6 and 9 days. It is found that the optimal quality of silica aerogels with the BET surface area as high as 668.82 m²/g was obtained from the alcogels of the solvent exchange period of 9 days. Depending on the size of the gel’s block, a longer solvent exchange period will ensure adequate removal of pore water. Post heat treatment on silica aerogels obtained from the 9 days of solvent exchange at 200, 300 and 400 °C for 2 h results in slight decreased of aerogel’s density from 0.048 g/cm³ to 0.039 g/cm³ and the hydrophobicity of the aerogels is decreased above 380 °C as confirmed by DSC analysis.

     

Activation of Nrf2/HO-1 by Peptide YD1 Attenuates Inflammatory Symptoms through Suppression of TLR4/MYyD88/NF-κB Signaling Cascade

In this study, we investigate the immunomodulatory effects of a novel antimicrobial peptide, YD1, isolated from Kimchi, in both in vitro and in vivo models. We establish that YD1 exerts its anti-inflammatory effects via up-regulation of the Nrf2 pathway, resulting in the production of HO-1, which suppresses activation of the NF-κB pathway, including the subsequent proinflammatory cytokines IL-1β, IL-6, and TNF-α. We also found that YD1 robustly suppresses nitric oxide (NO) and prostaglandin E2 (PGE₂) production by down-regulating the expression of the upstream genes, iNOS and COX-2, acting as a strong antioxidant. Collectively, YD1 exhibits vigorous anti-inflammatory and antioxidant activity, presenting it as an interesting potential therapeutic agent.     

A polar-based guided multi-objective evolutionary algorithm to search for optimal solutions interested by decision-makers in a logistics network design problem

In practical multi-objective optimization problems, respective decision-makers might be interested in some optimal solutions that have objective values closer to their specified values. Guided multi-objective evolutionary algorithms (guided MOEAs) have been significantly used to guide their evolutionary search direction toward these optimal solutions using by decision makers. However, most guided MOEAs need to be iteratively and interactively evaluated and then guided by decision-makers through re-formulating or re-weighting objectives, and it might negatively affect the algorithms performance. In this paper, a novel guided MOEA that uses a dynamic polar-based region around a particular point in objective space is proposed. Based on the region, new selection operations are designed such that the algorithm can guide the evolutionary search toward optimal solutions that are close to the particular point in objective space without the iterative and interactive efforts. The proposed guided MOEA is tested on the multi-criteria decision-making problem of flexible logistics network design with different desired points. Experimental results show that the proposed guided MOEA outperforms two most effective guided and non-guided MOEAs, R-NSGA-II and NSGA-II.     

An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.