A finite-state approach to arabic broken noun morphology

In this paper, a finite-state computational approach to Arabic broken plural noun morphology is introduced. The paper considers the derivational aspect of the approach, and how generalizations about dependencies in the broken plural noun derivational system of Arabic are captured and handled computationally in this finite-state approach. The approach will be implemented using Xerox finite-state tool

A Multi-Layer Approach For Detection Of Selective Forwarding Attacks In Wireless Sensor Networks

Wireless sensor networks (WSNs) are increasingly used due to their broad range of important applications in both military and civilian domains. Security is a major threat in WSNs. WSNs are prone to several types of security attacks. Sensor nodes have limited capacities and are deployed in dangerous locations; therefore, they are vulnerable to different types of attacks, including wormhole, sinkhole, and selective forwarding attacks. Security attacks are classified as data traffic and routing attacks. These security attacks could affect the most significant applications of WSNs, namely, military surveillance, traffic monitoring, and healthcare. Therefore, many approaches were suggested in literature to detect security attacks on the network layer in WSNs. The network layer is of paramount significance to the security of WSNs to prevent exploitation of their confidentiality, privacy, availability, integrity, and authenticity. Reliability, energy efficiency, and scalability are strong constraints on sensor nodes that affect the security of WSNs. Because sensor nodes have limited capabilities in most of these areas, selective forwarding attacks cannot be easily detected in networks. In this dissertation, an approach to selective forwarding detection (SFD) is suggested. The approach has three layers: MAC pool IDs, rule-based processing, and anomaly detection. It maintains the safety of data transmission between a source node and base station while detecting selective forwarding attacks. Furthermore, the approach is reliable, energy efficient, and scalable

Multi-Layer Approach for the Detection of Selective Forwarding Attacks

Security breaches are a major threat in wireless sensor networks (WSNs). WSNs are increasingly used due to their broad range of important applications in both military and civilian domains. WSNs are prone to several types of security attacks. Sensor nodes have limited capacities and are often deployed in dangerous locations; therefore, they are vulnerable to different types of attacks, including wormhole, sinkhole, and selective forwarding attacks. Security attacks are classified as data traffic and routing attacks. These security attacks could affect the most significant applications of WSNs, namely, military surveillance, traffic monitoring, and healthcare. Therefore, there are different approaches to detecting security attacks on the network layer in WSNs. Reliability, energy efficiency, and scalability are strong constraints on sensor nodes that affect the security of WSNs. Because sensor nodes have limited capabilities in most of these areas, selective forwarding attacks cannot be easily detected in networks. In this paper, we propose an approach to selective forwarding detection (SFD). The approach has three layers: MAC pool IDs, rule-based processing, and anomaly detection. It maintains the safety of data transmission between a source node and base station while detecting selective forwarding attacks. Furthermore, the approach is reliable, energy efficient, and scalable.https://doi.org/10.3390/s15112933

Application of Ultrasonic Atomization on a Micro Jet Engine Using Biofuel for Improving Performance

Jet engines are commonly used in aeronautical applications, and are one of the types of gas turbine engines. The circulation of air releases heat energy to expand the volume of hot fluids and impact the turbine wheel to generate power of hot gases. The present study investigates the potential of using ultrasonic atomization technology to assist in the combustion process. An experimental rig was set up to determine the performance of jet engines using ultrasonic droplets. A gas analyzer was used to measure various greenhouse emissions of exhaust gas. The performance of the engine was tested under three load levels (high, medium, low), starting from 10 psi at a steady state, to the minimum value. A significant result was tested for a low value of nitrogen monoxide at the three levels of load, and a specific result was tested for an efficiency value of 2% at the three levels of load. Carbon dioxide was found to decrease at the low load level. The use of an ultrasonic atomization device to assist in the combustion process was useful in achieving engine efficiency of 1% and a reduction of 25% in carbon dioxide exhaust gas

Molecular layer deposition and protein interface patterning for guided cell growth

This thesis describes the design, assembly and structural and functional characterization, of bio-(medical) applicable interfacial layers with molecular controlled architectures on solid substrates. The interaction between the living world of cells, tissue, or whole organisms and the (organic orinorganic) materials world of technical devices such as implants, sensors, or medical parts requires a proper construction and detailed structural (and functional) control of this organism-machine interface. Therefore, a possible way how to get from an optimal molecular layer deposition (MLD) to guided cell growth is developed in this work. By integrating a heater to an already existing MLD setup and an optimization of the deposition temperature we could improve the gas phase deposition process of GLYMO (3-Glycidyloxypropyl)-trimethoxysilane) yielding a faster formation of self-assembled monolayers(SAMs) and a better quality of GLYMO SAMs. This was confirmed by ex-situ analysis, e.g. fluorescence microscopy, referenced ellipsometry, and surface potential measurements. With the gas phase MLD, lithography, and lift-off processes functionalization of SiO$_{2}$ surfaces with GLYMO SAMs and patterned ploy-L-lysine proteins (PLL) could be achieved. This enables to generate various micropatterns that support cell adhesion, neurite outgrowth, and the formation of a geometrically defined networks of neurons. Finally, guided growth was demonstrated via rat cortical neuron cultures on the GLYMO-PLL patterned surfaces. On first sight, the neuron growth was clearly guided, i.e. neurons grow on PLL but not on GLYMO. However, we also noticed that on certain areas which should be coated with PLL, no cells were present. It seemed that in these areas during the lithography PMMA is cracked due to the e-beam exposure and partially binds to the GLYMO. This cracked PMMA hinders the PLL to bind to GLYMO and therefore only in places, where PLL dries out during the coating, PLL is present in the GLYMO-PLL pattern. These effects are observed via fluorescence imaging for the PLL coating and for the cell growth. In conclusion, the modified deposition process at elevated temperatures in combination with the developed interface pattering process via a combination of a molecular layer of GLYMO and the protein PLL might be suitable for guidance of neuronal growth, despite the problem of a PMMA blocking layer which seem to be generated during the lithography. This shortcoming could be overcome by an additional step in which the blocking layer is remove

Monomer structure fingerprints: an extension of the monomer composition version for peptide databases

International audiencePreviously a fingerprint based on monomer composition (MCFP) of nonribosomal peptides (NRPs) has been introduced. MCFP is a novel method for obtaining a representative description of NRP structures from their monomer composition in a fingerprint form. An effective screening and prediction of biological activities has been obtained from Norine NRPs database. In this paper, we present an extension of the MCFP fingerprint. This extension is based on adding few columns into the fingerprint; representing monomer clusters, 2D structures, peptide categories, and peptide diversity. All these data have been extracted from the NRP structure. Experiments with Norine NRPs database showed that the extended MCFP, that can be called Monomer Structure FingerPrint (MSFP) produced high prediction accuracy (> 95%) together with a high recall rate (86%) obtained when MSFP was used for prediction and similarity searching. From this study it appeared that MSFP mainly built from monomer composition can substantially be improved by adding more columns representing useful information about monomer composition and 2D structure of NRPs

Impact of Plasma Combustion Technology on Micro Gas Turbines Using Biodiesel Fuels

The adoption of biorenewable alternative fuel resources from biofuels (ethanol or biodiesel) has produced promising solutions to reduce some toxic greenhouse gas (GHG) emissions from gas turbine engines (GTEs). Despite the reduced hydrocarbon associated with adopting alternative bio-renewable fuel resources, GTE operations still emit toxic gases due to inefficient engine performance. In this study, we assess the impact of the integration of plasma combustion technology on a micro-GTE using biodiesel fuel from animal fat with the aim of addressing performance, fuel consumption, and GHG emission reduction limitations. Laboratory design, fabrication, assembly, testing, and results evaluation were conducted at Kuwait’s Public Authority for Applied Education and Training. The result indicates the lowest toxic emissions of sulfur, nitrogen oxide (NO), NO2, and CO were from the biodiesel blended fuels. The improved thermal efficiency of GTE biodiesel due to the volume of hydrogen plasma injected improves the engine’s overall combustion efficiency. Hence, this increases the compressor inlet and outlet firing temperature by 13.3 °C and 6.1 °C, respectively. The Plasma technology produced a thrust increment of 0.2 kgf for the highest loading condition, which significantly impacted horsepower and GTE engine efficiency and reduced the cost of fuel consumption

Impact of plasma combustion technology on micro gas turbines using biodiesel fuels

The adoption of biorenewable alternative fuel resources from biofuels (ethanol or biodiesel) has produced promising solutions to reduce some toxic greenhouse gas (GHG) emissions from gas turbine engines (GTEs). Despite the reduced hydrocarbon associated with adopting alternative bio-renewable fuel resources, GTE operations still emit toxic gases due to inefficient engine performance. In this study, we assess the impact of the integration of plasma combustion technology on a micro-GTE using biodiesel fuel from animal fat with the aim of addressing performance, fuel consumption, and GHG emission reduction limitations. Laboratory design, fabrication, assembly, testing, and results evaluation were conducted at Kuwait’s Public Authority for Applied Education and Training. The result indicates the lowest toxic emissions of sulfur, nitrogen oxide (NO), NO2, and CO were from the biodiesel blended fuels. The improved thermal efficiency of GTE biodiesel due to the volume of hydrogen plasma injected improves the engine’s overall combustion efficiency. Hence, this increases the compressor inlet and outlet firing temperature by 13.3 °C and 6.1 °C, respectively. The Plasma technology produced a thrust increment of 0.2 kgf for the highest loading condition, which significantly impacted horsepower and GTE engine efficiency and reduced the cost of fuel consumption