Bacterial Cellulose Production from Beet Molasses

10245. The yield of the bacterial cellulose (BC) produced from beet molasses was higher than that using glucose as a sole carbon source. The structure of BC produced in presence of beet molasses was studied using IR spectroscopy and X-ray diffractometry. IR spectra show the relative absorbance of CO-C ether linkage (at 1120 cm-1) in BC using glucose has a relatively lower value than that from molasses. This indicates that BC produced from glucose has a relatively higher degree of polymerization. From X-ray pattern, no remarkable differences in crystallinity index of cellulose between the two media were recorde

Propuesta de un modelo gerencial aplicado a proyectos de Infraestructura vial adjudicados por el Instituto de Desarrollo Urbano (IDU), a partir de la metodología PMBOK

Trabajo de investigaciónBasados en la experiencia en la ejecución de contratos de mantenimiento de la infraestructura Vial del Instituto de Desarrollo Urbano (IDU) en la ciudad de Bogotá y en las constantes problemáticas que surgen en las etapas constructivas de los proyectos de mantenimiento. Es necesario buscar alternativas desde el punto de vista de dirección de proyectos para mitigar las circunstancias que no permiten cumplir las expectativas tanto técnicas como económicas de estos proyectos. Enfocados en una correcta planeación, ejecución y control, con el fin de suplir cada una de las necesidades del IDU y de las diferentes tipologías de contratos de mantenimiento ofrecidas por dicha entidad, es por eso que surge la necesidad de generar una herramienta que basada en la experiencia en situaciones atípicas presentadas en la ejecución de proyectos de mantenimiento y en la metodología PMBOK logre mostrar un norte para enfrentarse de una manera coherente a la gerencia de obra en uno de estos contratos.Introducción 1 Generalidades 2 Marcos de referencia 3 Metodología 4 Productos a entregar 5 Descripción de resultados esperados e impacto 6 Desarrollo del proyecto 7 Conclusiones 8 Bibliografía 9 AnexosEspecializaciónEspecialista en Gerencia de Obras Civile

A hybrid swarm intelligence feature selection approach based on time-varying transition parameter

Feature selection aims to reduce the dimensionality of a dataset by removing superfluous attributes. This paper proposes a hybrid approach for feature selection problem by combining particle swarm optimization (PSO), grey wolf optimization (GWO), and tournament selection (TS) mechanism. Particle swarm enhances the diversification at the beginning of the search mechanism, grey wolf enhances the intensification at the end of the search mechanism, while tournament selection maintains diversification not only at the beginning but also at the end of the search process to achieve local optima avoidance. A time-varying transition parameter and a random variable are used to select either particle swarm, grey wolf, or tournament selection techniques during search process. This paper proposes different variants of this approach based on S-shaped and V-shaped transfer functions (TFs) to convert continuous solutions to binaries. These variants are named hybrid tournament grey wolf particle swarm (HTGWPS), followed by S or V letter to indicate the TF type, and followed by the TF’s number. These variants were evaluated using nine high-dimensional datasets. The results revealed that HTGWPS-V1 outperformed other V’s variants, PSO, and GWO on 78% of the datasets based on maximum classification accuracy obtained by a minimal feature subset. Also, HTGWPS-V1 outperformed six well-known-metaheuristics on 67% of the datasets

Privacy Preservation Intrusion Detection Technique for SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems face the absence of a protection technique that can beat different types of intrusions and protect the data from disclosure while handling this data using other applications, specifically Intrusion Detection System (IDS). The SCADA system can manage the critical infrastructure of industrial control environments. Protecting sensitive information is a difficult task to achieve in reality with the connection of physical and digital systems. Hence, privacy preservation techniques have become effective in order to protect sensitive/private information and to detect malicious activities, but they are not accurate in terms of error detection, sensitivity percentage of data disclosure. In this paper, we propose a new Privacy Preservation Intrusion Detection (PPID) technique based on the correlation coefficient and Expectation Maximisation (EM) clustering mechanisms for selecting important portions of data and recognizing intrusive events. This technique is evaluated on the power system datasets for multiclass attacks to measure its reliability for detecting suspicious activities. The experimental results outperform three techniques in the above terms, showing the efficiency and effectiveness of the proposed technique to be utilized for current SCADA systems

Protection of data privacy based on artificial intelligence in Cyber-Physical Systems

With the rapid evolution of cyber attack techniques, the security and privacy of Cyber-Physical Systems (CPSs) have become key challenges. CPS environments have several properties that make them unique in efforts to appropriately secure them when compared with the processes, techniques and processes that have evolved for traditional IT networks and platforms. CPS ecosystems are comprised of heterogeneous systems, each with long lifespans. They use multitudes of operating systems and communication protocols and are often designed without security as a consideration. From a privacy perspective, there are also additional challenges. It is hard to capture and filter the heterogeneous data sources of CPSs, especially power systems, as their data should include network traffic and the sensing data of sensors. Protecting such data during the stages of collection, analysis and publication still open the possibility of new cyber threats disrupting the operational loops of power systems. Moreover, while protecting the original data of CPSs, identifying cyberattacks requires intrusion detection that produces high false alarm rates. This thesis significantly contributes to the protection of heterogeneous data sources, along with the high performance of discovering cyber-attacks in CPSs, especially smart power networks (i.e., power systems and their networks). For achieving high data privacy, innovative privacy-preserving techniques based on Artificial Intelligence (AI) are proposed to protect the original and sensitive data generated by CPSs and their networks. For cyber-attack discovery, meanwhile applying privacy-preserving techniques, new anomaly detection algorithms are developed to ensure high performances in terms of data utility and accuracy detection. The first main contribution of this dissertation is the development of a privacy preservation intrusion detection methodology that uses the correlation coefficient, independent component analysis, and Expectation Maximisation (EM) clustering algorithms to select significant data portions and discover cyber attacks against power networks. Before and after applying this technique, machine learning algorithms are used to assess their capabilities to classify normal and suspicious vectors. The second core contribution of this work is the design of a new privacy-preserving anomaly detection technique protecting the confidential information of CPSs and discovering malicious observations. Firstly, a data pre-processing technique filters and transforms data into a new format that accomplishes the aim of preserving privacy. Secondly, an anomaly detection technique using a Gaussian mixture model which fits selected features, and a Kalman filter technique that accurately computes the posterior probabilities of legitimate and anomalous events are employed. The third significant contribution of this thesis is developing a novel privacy-preserving framework for achieving the privacy and security criteria of smart power networks. In the first module, a two-level privacy module is developed, including an enhanced proof of work technique-based blockchain for accomplishing data integrity and a variational autoencoder approach for changing the data to an encoded data format to prevent inference attacks. In the second module, a long short-term memory deep learning algorithm is employed in anomaly detection to train and validate the outputs from the two-level privacy modules

THE ISLAMIC DECORATIONS ON SAFAVID COPPER COINS BETWEEN THE UNITY AND DIVERSITY IN ITS ILLUSTRATED DECORATIOS ''SELECTED MODELS FROM FUJAIRAH MUSEUM''

The Museum of Fujairah in the United Arab Emirates contains a collection which includes of 13 copper coins dating to the period of weakness and crisis at the end of Safavid Dynasty, during the reigns of Shah Suleiman "Safi II" (1077-1105 H./1666-1694 C.) and Shah Sultan Hussein I (1105-1135 A.H/1694-1722). The Study aims to discusses the illustrated drawings on these coins throughout the Iranian cities, the theme of the study, and other cities during the Safavid period in general; and this historical period especially which was the beginning of the end of Safavid Daynasty; with the origins of these drawings of the Safavid copper coins "Fulus" through the early Islamic Coins, and through its inclusion on the applied artifacts, such as pottery, textile, metal, wood, carpets, stone and glass during the Islamic Age. The Study shows that the Safavid artists included the decorative elements which represent Sasanian traditions, because the Safavid Dynasty attempted to revive Persian nationalism and regain its previous glory. The Study ends with the results which shows that the decorative elements on theses coins are considered an expansion of the decorative elements which adorned different applied artifacts with drawings on pottery; or carving on wood, stones or marble; or enameling on glass; or printing on textile, that occurred in all Muslim countries since the first century A.H, either in Andalusia, Egypt, Iraq, Levant, and Turkey assuring the unity of decorative elements on coins and different applied artifacts in the frame of Islamic art with the diversity in the decoration methods.&nbsp

Preparation and characterization of starch /cellulose composite

Novel preparative method for starch/cellulose composite in different ratios has been reported. The composite is obtained by mixing cellulose extracted from paper garbage and corn starch in PEG/NaOH solution. Starch/cellulose composite is characterized by different techniques, namely XRD, TGA and optical absorption measurements. From XRD data, it is observed that cellulose would lead to expand the d-spacing of (1ī0 plane and decrease the d-spacing of (110) plane of starch. The UV absorption peaks are shifted towards the longest wavelengths in the visible region for starch/cellulose composite. However, the refractive index value decreases from 1.44 to 1.08 and consequently the dielectric constant value decreases from 2.07 to 1.16 with increasing the starch ratio. Furthermore, the glass transition temperature increases from 107 °C for starch to 115 °C for starch/cellulose composite. However, the excess in starch ratio leads to increase in local crosslink density in the composite network