The fresh water green alga Chlorella vulgaris was selected to study its ability to degrade one of polycyclic aromatic hydrocarbon (PAHs)known is anthracene (ANT). ANT is widely used in artificial products such as wood preservatives; insecticides; dye and coating materials. This algae was cultivated in CH-10 medium under constant laboratory conditions and exposed to different concentrations (1, 3, and 5 mg/l) of anthracene for 3, 5, 7, 9 and 15 days, with the concentration of ANT measured  by high performance liquid chromatographic analysis (HPLC). The results showed that C.vulgaris has high ability to reduce anthracene to 80% at 1mg/Lcon centration after 3 days and 100% after 5 days, while at 3 and 5 mg / L concent

Chloroviruses are large viruses that replicate in chlorella-like green algae and normally exist as mutualistic endosymbionts (referred to as zoochlorellae) in protists such as Paramecium bursaria. Chlorovirus populations rise and fall in indigenous waters through time; however, the factors involved in these virus fluctuations are still under investigation. Chloroviruses attach to the surface of P. bursaria but cannot infect their zoochlorellae hosts because the viruses cannot reach the zoochlorellae as long as they are in the symbiotic phase. Predators of P. bursaria, such as copepods and didinia, can bring chloroviruses into contact with zoochlorellae by disrupting the paramecia, which results in an increase in virus titers in micr

The aim of human lower limb rehabilitation robot is to regain the ability of motion and to strengthen the weak muscles. This paper proposes the design of a force-position control for a four Degree Of Freedom (4-DOF) lower limb wearable rehabilitation robot. This robot consists of a hip, knee and ankle joints to enable the patient for motion and turn in both directions. The joints are actuated by Pneumatic Muscles Actuators (PMAs). The PMAs have very great potential in medical applications because the similarity to biological muscles. Force-Position control incorporating a Takagi-Sugeno-Kang- three- Proportional-Derivative like Fuzzy Logic (TSK-3-PD) Controllers for position control and three-Proportional (3-P) controllers for force contr

This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In ord

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

This study proposed control system that has been presented to control the electron lens resistance in order to obtain a stabilized electron lens power. This study will layout the fundamental challenges, hypothetical plan arrangements and development condition for the Integrable Optics Test Accelerator (IOTA) in progress at Fermilab. Thus, an effective automatic gain  control (AGC) unit has been introduced which prevents fluctuations in the internal resistance of the electronic lens caused by environmental influences to affect the system's current and power values ​​and keep them in stable amounts. Utilizing this unit has obtained level balanced out system un impacted with electronic lens surrounding natural varieties.