Artemisinin Story from the Balkans

The isolation is reported of artemisinin (qinghaosu), a drug remarkably effective against malaria, from the aerial parts of Artemisia annua L. (sweet wormwood) at the Department of Chemistry, University of Belgrade (now Faculty of Chemistry), Serbia by the end of 1970, almost two years before the isolation of the same compound in China

Microfiltration of distillery stillage: Influence of membrane pore size

Stillage is one of the most polluted waste products of the food industry. Beside large volume, the stillage contains high amount of suspended solids, high values of chemical oxygen demand and biological oxygen demand, so it should not be discharged in the nature before previous purification. In this work, three ceramic membranes for microfiltration with different pore sizes were tested for stillage purification in order to find the most suitable membrane for the filtration process. Ceramic membranes with a nominal pore size of 200 nm, 450 nm and 800 nm were used for filtration. The influence of pore size on permeate flux and removal efficiency was investigated. A membrane with the pore size of 200 nm showed the best filtration performance so it was chosen for the microfiltration process

Optimization of cultivation medium for the production of antibacterial agents

Optimization of the cultivation medium for production of antibiotic effective against pathogenic bacteria Staphylococcus aureus using strain of Streptomyces spp. isolated from the environment represents the aim of this study. After the biosynthesis, the medium was analyzed by determining residual sugar and nitrogen, and the antibiotic activity was determined using diffusion-disc method. Experiments were carried out in accordance with the Box-Behnken design, with three factors varied on three levels (glucose: 10.0, 30.0 and 50.0 g/L; soybean meal: 5.0, 15.0 and 25.0 g/L; phosphates: 0.5, 1.0 and 1.5 g/L) and for the optimization of selected parameters Response Surface Methodology was used. The obtained model with the desirability function of 0.985 estimates that the lowest amounts of residual sugar (0.89 g/L) and nitrogen (0.24 g/L) and the largest possible inhibition zone diameter (21.88 mm) that with its antibiotic activity against S. aureus creates the medium containing 10.0 g/L glucose, 5.0 g/L soybean meal and 1.04 g/L phosphates

Quercetagetin 6,7,3',4'-tetramethyl ether: a new flavonol from Artemisia annua

Plant: Artemisia annua L. Source: South of Belgrade. A CHCl, extract of the whole plant yielded after chromatography a new polar flavonol, yellow crystals, mp 171-172 degrees, M+ m/e 374, C19H18O8. characterized by UV/VIS, the pattern of mass fragmentation, NMR. Methylation gave a hexamethyl ether identical in all respects (mmp, TLC, IR, NMR, UV, MS) to the known quercetagetin hexamethyl ether obtained from an authentic source

Image Registration Algorithm for Deep Learning-Based Stereo Visual Control of Mobile Robots

Since the emergence of deep learning as a dominant technique for numerous tasks in the computer vision domain, the robotics community has strived to utilize its potential. Deep learning represents a framework capable of learning the most complex models necessary to carry out various robotic tasks. We propose to integrate deep learning and one of the fundamental robotic algorithms—visual servoing. Fully convolutional neural networks are used for semantic segmentation, which represents the process of labeling every pixel within the image. The obtained information from labeled (categorical) images can be crucial for mobile robot control in dynamic environments. To adequately utilize semantic segmentation for mobile robot control, the segmented images acquired at the desired and the current pose need to be registered (aligned). Since the accuracy of visual servoing depends on the accuracy of the image registration process, we propose to increase the accuracy of mobile robot positioning by analyzing three different optimization algorithms devoted to the registration of categorical images. The standard gradient descent algorithm is compared to the OnePlusOneEvolutionary algorithm, and simulated annealing. Moreover, different cost functions such as Mattes mutual information, global accuracy, and mean intersection over union are also investigated. All the algorithms are tested on our own wheeled mobile robot RAICO (Robot with Artificial Intelligence based COgnition) developed within the Laboratory for robotics and artificial intelligence. The results indicate that the algorithm with a larger exploration to exploitation ratio provides better results. Moreover, the cost function with the steepest convex domain is more advantageous

Real-Time Mobile Robot Perception Based on Deep Learning Detection Model

The recent advances in deep learning models have enabled the robotics community to utilize their potential. The mobile robot domain on which deep learning has the most influence is scene understanding. Scene understanding enables mobile robots to exist and execute their tasks through processes such as object detection, semantic segmentation, or instance segmentation. A perception system that can recognize and locate objects in the scene is of the highest importance for achieving autonomous behavior of robotic systems. Having that in mind, we develop the mobile robot perception system based on deep learning. More precisely, we utilize an accurate and fast Convolution Neural Network (CNN) model to enable a mobile robot to detect objects in its scene in a real-time manner. The integration of two CNN models (SSD and MobileNet) is performed and implemented on mobile robot RAICO (Robot with Artificial Intelligence based COgnition). The experimental results show that the proposed perception system enables a high degree of object recognition with satisfying inference speed, even with limited processing power provided by Nvidia Jetson Nano integrated within RACIO

Design and Development of a Holonomic Mobile Robot for Material Handling and Transportation Tasks

Modern intelligent manufacturing systems are dynamic environments with the ability to respond and adapt to various internal and external changes that can occur during the manufacturing process. By default, they imply efficient, reliable, and flexible material handling and transportation system, which can be effectively realized by using intelligent mobile robots. In order to achieve locomotion of an intelligent mobile robot that will minimize the usage of space within the manufacturing environment, we propose the development of a new holonomic mobile robot DOMINO (Deep learning-based Omnidirectional Mobile robot with INtelligentcOntrol). 3D model of holonomic mobile robot prototype is developed in CAD software package SolidWorks, and designed parts are produced with additive manufacturing technology. Single board computer Raspberry Pi 4 and microcontroller board Arduino Mega 2560 are used for motion control of the holonomic mobile robot, while control actions are determined by the defined kinematic model of the omnidirectional wheeled mobile robot. The experimental verification shows that the holonomic mobile robot is capable of following a predetermined path while successfully avoiding obstacles within a laboratory model of a manufacturing environment

Deep learning of mobile service robots

In the last two decades, the development of state -of-the-art artificial intelligence (AI) models has significantly increased the utilization of commercial and task-specific robots in the service domain. The additional level of intelligence introduced by AI models has enabled service robots to coexist within different human environments and collaborate with end-users. One of the most promising AI techniques, Deep Learning (DL), can provide service robots with a wide range of abilities, such as detecting human pose and emotions, understanding natural languages, as well as scene understanding. Achieved abilities can enable mobile service robots to execute specific tasks in real and stochastic environments. Having that in mind, in this chapter, we provide an in-depth analysis of the tasks that are best-suited for DL within the service robots domain. Moreover, the study of the state-of-the-art DL models for object detection, semantic segmentation, and human pose estimation is carried out. In the end, the authors presented a thorough examination of the training process and analysis of the results for one of the most promising convolutional neural network models (DeepLabv3+) used for semantic segmentation

Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model

The recent development of faster and more accurate deep learning models has enabled researchers to utilize the potential of deep learning in robotics. Convolutional neural networks used for the process of semantic segmentation are being applied to improve the traditional robotic tasks by adding an additional level of intelligence, through the execution of context-aware tasks. Having that in mind, visual servoing can now be performed in a completely new manner, by exploiting only semantic and geometric knowledge about the environment. To carry out visual servoing, the mathematical model of the error between the images generated at the current and the desired mobile robot pose (i.e. position and orientation) in the image space needs to be adequately defined. In this paper, we propose the novel mathematical model for the weighted fitness function evaluation, which is utilized for the image registration process within the visual servoing framework. By weighting the classes by their importance in the desired image, the convergence domain of the initial error in the visual servoing process can be greatly extended. The experimental evaluation is carried out on the mobile robot RAICO (Robot with Artificial Intelligence based COgnition), where it is shown that weighted fitness function enables more robust intelligent visual servoing systems with a lower possibility of failure, easier real-world implementation, and feasible object driven navigation

Fulvic acid characterization in an alluvial sediment sequence: differences between clay and sand environments

Fulvic acids (FAs) from the whole Quaternary sequence (to a depth of 26 m) of the alluvial sediment of the Sava river (taken from a site in Novi Beograd, Belgrade, Yugoslavia) were investigated with the aim of examining the effect of the environment (clay or sand) on their characteristics. Characterization of the FAs was carried out by correlating the results obtained by various instrumental techniques (u.v.-vis. and i.r. spectroscopy, fluorimetry). Differences were noticed between the FAs, depending on whether they originated from sand or clay, which indicates that the hydrogeological environment represents an important factor in determining their characteristics, though the role of the precursor material may be significant. FAs found in clay layers are less aliphatic, have a greater non-aliphatic hydroxyl group content and exhibit more similar fluorescence ntensities compared to FAs originating from sand. The greater mutual similarities of the clay FAs in comparison to those from sands is a result of diagenesis occurring in a more "closed" system