An algorithm for architecture partitioning in high-speed networks with bursty traffic

This paper analyzes the performance of a switching architecture. The performance measures include the elapsed time of packet transfer and the waiting time to begin transfer. The architecture is partitioned depending on the type of network used and the expected traffic in the network. Every partition has a switch with a buffer that can absorb surges of bursty traffic within the network partition. The buffer size depends on the type of the network and incoming traffic. The partition size depends on the network bandwidth, network traffic, packet size and buffer size. Examples of different networks are used to show the applications of the model. The results show that the elapsed time of packet switch transfer depends exponentially on the number of partitions in the network.     

Neural network Reinforcement Learning for visual control of robot manipulators

It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.     

Particle Swarm Optimization and Pure Adaptive Search in Finish Goods’ Inventory Management

Abstract

Inventory management deals with a tradeoff between the benefits of keeping stocks of goods that allows fulfillment of the customer’s demand, and the cost of carrying inventory. Inventory control techniques are very important components and the most organizations can substantially reduce their costs associated with the flow of materials. This paper presents new inventory management model based on particle swarm optimization and pure adaptive search global optimization algorithm in production-inventory system. The proposed model is focusing on planned level of demand for finished goods, production and raw materials cost, production capacity as the norm, change of the production cost and inventory capital cost, all of which are typical factors in automobile manufacture industry. The model determines different factors such as the minimizing inventory quantity, minimizing inventory value, and minimizing production cost based on demand for production items. The model is tested with original real-world dataset obtained from the automotive company Lear from US and its factory in Novi Sad, Serbia.     

ITEA—interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data

Widespread use of GPS and similar technologies makes it possible to collect extensive amounts of trajectory data. These data sets are essential for reasonable decision making in various application domains. Additional information, such as events taking place along a trajectory, makes data analysis challenging, due to data size and complexity. We present an integrated solution for interactive visual analysis and exploration of events along trajectories data. Our approach supports analysis of event sequences at three different levels of abstraction, namely spatial, temporal, and events themselves. Customized views as well as standard views are combined to form a coordinated multiple views system. In addition to trajectories and events, we include on-the-fly derived data in the analysis. We evaluate our integrated solution using the IEEE VAST 2015 Challenge data set. A successful detection and characterization of malicious activity indicate the usefulness and efficiency of the presented approach.     

Semantic integration of enterprise information systems using meta-metadata ontology

This paper proposes a non-domain-specific metadata ontology as a core component in a semantic model-based document management system (DMS), a potential contender towards the enterprise information systems of the next generation. What we developed is the core semantic component of an ontology-driven DMS, providing a robust semantic base for describing documents’ metadata. We also enabled semantic services such as automated semantic translation of metadata from one domain to another. The core semantic base consists of three semantic layers, each one serving a different view of documents’ metadata. The core semantic component’s base layer represents a non-domain-specific metadata ontology founded on ebRIM specification. The main purpose of this ontology is to serve as a meta-metadata ontology for other domain-specific metadata ontologies. The base semantic layer provides a generic metadata view. For the sake of enabling domain-specific views of documents’ metadata, we implemented two domain-specific metadata ontologies, semantically layered on top of ebRIM, serving domain-specific views of the metadata. In order to enable semantic translation of metadata from one domain to another, we established model-to-model mappings between these semantic layers by introducing SWRL rules. Having the semantic translation of metadata automated not only allows for effortless switching between different metadata views, but also opens the door for automating the process of documents long-term archiving. For the case study, we chose judicial domain as a promising ground for improving the efficiency of the judiciary by introducing the semantics in this field.     

Investigation of technological approaches for reduction of methanol formation in plum wines

Methanol is natural ingredient of alcoholic beverages and soft drinks; however, the products of its metabolic transformations (formaldehyde and formic acid) are toxic to humans. The aim of this research was to assess the application of different physico‐chemical treatments of pomace in order to find the most efficient method for reducing the methanol during fermentation with the least effect on the sensory properties of the wine. The following procedures were studied: addition of tannins in pomace, addition of phenolic acids, addition of d ‐galacturonic and pectic acid, use of bentonite and zeolite, heat treatment of pomace, thermosonication and treatment of pomace with microwaves. Fruit wine used in this study was produced from plums (Prunus domestica L.). Applied treatments showed variable efficiency in reduction of methanol formation in plum wine. It may be noted that the procedures that involved some form of thermal treatment were characterized by a significant decrease in the production of methanol (up to 60–70%) but mostly tended to have a negative impact on the sensory properties of the produced wines. However, exposure of pomace to microwaves for a short time, owing to the contribution of mechanisms of non‐thermal nature (kinetic and chemical), allowed for a significant reduction in methanol formation with a negligible impact on the sensory properties. The decrease in methanol formation during fermentation using tannins, pectic acid, bentonite and zeolite was poor, with only a reduction of up to 15%. Obtained results are significant to the fruit wine and spirit industry, considering the common problem related to an increased content of methanol in these beverages. Copyright © 2016 The Institute of Brewing & Distilling     

Chemical composition of currant seed extracts and their protective effect on human lymphocytes DNA

On the basis of LC/UV/MS analysis, 35 compounds from the extracts of seeds of black, red, and white currants (2 cultivars of each) were identified. Black currants cultivars contained protocatechuic acid and p-hydroxybenzoic acid, and traces of nitril containing phenolic acids. The presence of synapoyl glucoside was characteristic for cv. Malling Jewel. Sesquiterpenoid glycosides and carboxymethylindol glycosides were present mainly in white and red currant cultivars. Blackcurrant seeds contained higher amounts of flavonoids, especially rutin, isoquercetin, and taxifolin. The currant seed extracts were tested for in vitro protective effect on chromosome aberrations in peripheral human lymphocytes using cytochalasin-B blocked micronucleus (CBMN) assay. The frequency of MN was scored in binucleated cells, and the cultivars of black currants showed the best antioxidant potential. At a concentration of only 0.17 mg/mL, extract of the seed cv. Malling Jewel effected a decrease in the frequency of MN of 60% compared with control cell cultures. PRACTICAL APPLICATION: Our results provide evidence of protective effects of currant seed extracts and isolated pure compounds on cytogenetic damages in human lymphocytes. Thus, currant seed extracts could exert beneficial effects in quite a few diseases, for many of the biological actions have been attributed to their antioxidant properties.     

Nodes on ropes: a comprehensive data and control flow for steering ensemble simulations

Flood disasters are the most common natural risk and tremendous efforts are spent to improve their simulation and management. However, simulation-based investigation of actions that can be taken in case of flood emergencies is rarely done. This is in part due to the lack of a comprehensive framework which integrates and facilitates these efforts. In this paper, we tackle several problems which are related to steering a flood simulation. One issue is related to uncertainty. We need to account for uncertain knowledge about the environment, such as levee-breach locations. Furthermore, the steering process has to reveal how these uncertainties in the boundary conditions affect the confidence in the simulation outcome. Another important problem is that the simulation setup is often hidden in a black-box. We expose system internals and show that simulation steering can be comprehensible at the same time. This is important because the domain expert needs to be able to modify the simulation setup in order to include local knowledge and experience. In the proposed solution, users steer parameter studies through the World Lines interface to account for input uncertainties. The transport of steering information to the underlying data-flow components is handled by a novel meta-flow. The meta-flow is an extension to a standard data-flow network, comprising additional nodes and ropes to abstract parameter control. The meta-flow has a visual representation to inform the user about which control operations happen. Finally, we present the idea to use the data-flow diagram itself for visualizing steering information and simulation results. We discuss a case-study in collaboration with a domain expert who proposes different actions to protect a virtual city from imminent flooding. The key to choosing the best response strategy is the ability to compare different regions of the parameter space while retaining an understanding of what is happening inside the data-flow system.     

Adsorption–desorption noise in plasmonic chemical/biological sensors for multiple analyte environment

We investigated intrinsic noise in plasmonic sensors caused by adsorption and desorption of gaseous analytes on the sensor surface. We analyzed a general situation when there is a larger number of different analyte species. We applied our model to calculate various analyte mixtures, including some environmental pollutants, toxic and dangerous substances. The spectral density of mean square refractive index fluctuations follows a dependence similar to that of generation-recombination noise in photodetectors, flat at lower frequencies and sharply decreasing at higher. Some of the calculated noise levels are well within the detection range of conventional surface plasmon resonance sensors. An AD noise peak is observed in temperature dependence of mean square refractive index fluctuations, thus sensor operating temperature may be optimized to obtain larger signal to noise ratio. A significant property of AD noise is its rise with the decreasing plasmon sensor area, which means that it will be even more pronounced in modern nanoplasmonic devices. Our consideration is valid both for conventional surface plasmon resonance devices and for general nanoplasmonic devices.     

Collaborative spaces for GIS-based multimedia cartography in blended environments

The interaction spaces between instructors and learners in the traditional face-to-face classroom environment are being changed by the diffusion and adoption of many forms of computer-based pedagogy. An integrated understanding of these evolving interaction spaces together with how they interconnect and leverage learning are needed to develop meaningful strategies for effective teaching and learning. The 18i collaborative interaction spaces model was designed based on constructivist principles, and describes 18 mixed instructor–learner spaces contextualized at a finer operational scale that makes explicit a wider range of interactions. The model was implemented during the life cycle of an undergraduate GIS-based multimedia cartography course. One output was the generation of a repository of rule-based trajectory plans for rapid planning and problem solving. The model provides an integrated workflow to manage course contents, products, interactions, individuality, and learning styles in blended environments.