Design and Development of Constant Stress Composite Cantilever Beam

Composite materials, due to their unique properties such as high strength to weight ratio, corrosion resistance, and impact resistance have huge potential as structural materials in automotive, construction and transportation applications. However, these properties often come at higher cost owing to complex design methods, difficult manufacturing processes and raw material cost. Traditionally, tapered laminated composite structures are manufactured using autoclave manufacturing process by ply drop off technique. Autoclave manufacturing though very powerful suffers from high capital investment and higher energy consumption. As per the current trends in composite manufacturing, Out of Autoclave (OoA) processes are looked as emerging technologies for manufacturing the structural composite components for aerospace and defense applications. However, there is a need for improvement among these processes to make them reliable and consistent. In this paper, feasibility of using out of autoclave process to manufacture the variable thickness cantilever beam is discussed. The minimum weight design for the composite beam is obtained using constant stress beam concept by tailoring the thickness of the beam. Ply drop off techniques was used to fabricate the variable thickness beam from glass/epoxy prepregs. Experiments were conducted to measure bending stresses along the span of the cantilever beam at different intervals by applying the concentrated load at the free end. Experimental results showed that the stresses in the bean at different intervals were constant. This proves the ability of OoA process to manufacture the constant stress beam. Finite element model for the constant stress beam was developed using commercial finite element simulation software. It was observed that the simulation results agreed very well with the experimental results and thus validated design and manufacturing approach used.

Predicting Application Layer DDoS Attacks Using Machine Learning Algorithms

A Distributed Denial of Service (DDoS) attack is a major threat to cyber security. It originates from the network layer or the application layer of compromised/attacker systems which are connected to the network. The impact of this attack ranges from the simple inconvenience to use a particular service to causing major failures at the targeted server. When there is heavy traffic flow to a target server, it is necessary to classify the legitimate access and attacks. In this paper, a novel method is proposed to detect DDoS attacks from the traces of traffic flow. An access matrix is created from the traces. As the access matrix is multi dimensional, Principle Component Analysis (PCA) is used to reduce the attributes used for detection. Two classifiers Naive Bayes and K-Nearest neighborhood are used to classify the traffic as normal or abnormal. The performance of the classifier with PCA selected attributes and actual attributes of access matrix is compared by the detection rate and False Positive Rate (FPR).

Field Study for Evaluating Winter Thermal Performance of Auckland School Buildings

Auckland has a temperate climate with comfortable warm, dry summers and mild, wet winters. An Auckland school normally does not need air conditioning for cooling during the summer and only needs heating during the winter. The Auckland school building thermal design should more focus on winter thermal performance and indoor thermal comfort for energy efficiency. This field study of testing indoor and outdoor air temperatures, relative humidity and indoor surface temperatures of three classrooms with different envelopes were carried out in the Avondale College during the winter months in 2013. According to the field study data, this study is to compare and evaluate winter thermal performance and indoor thermal conditions of school buildings with different envelopes.

Sustainable Energy Supply in Social Housing

The final energy use can be divided mainly in four sectors: commercial, industrial, residential, and transportation. The trend in final energy consumption by sector plays as a most straightforward way to provide a wide indication of progress for reducing energy consumption and associated environmental impacts by different end use sectors. The average share of end use energy for residential sector in the world was nearly 20% until 2011, in Germany a higher proportion is between 25% and 30%. However, it remains less studied than energy use in other three sectors as well its impacts on climate and environment. The reason for this involves a wide range of fields, including the diversity of residential construction like different housing building design and materials, living or energy using behavioral patterns, climatic condition and variation as well other social obstacles, market trend potential and financial support from government. This paper presents an extensive and in-depth analysis of the manner by which projects researched and operated by authors in the fields of energy efficiency primarily from the perspectives of both technical potential and initiative energy saving consciousness in the residential sectors especially in social housing buildings.

On the Network Packet Loss Tolerance of SVM Based Activity Recognition

In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces  high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Nano Composite of Clay and Modified Ketonic Resin as Fire Retardant Polyol for Polyurethane

In situ modified cyclohexanone-formaldehyde resins were prepared by addition of alendronic acid during resin preparation. Clay nanocomposites in ketonic resins were achieved by adding clay into the flask at the beginning of the resin preparation. The prepared resins were used for the synthesis of fire resistant polyurethanes foam. Both phosphorous containing modifier compound alendronic acid and nanoclay increases fire resistance of the cyclohexanone-formaldehyde resin thus polyurethane produced from these resins. The effect of the concentrations of alendronic acid and clay on the fire resistance and physical properties of polyurethanes was studied.

Blind Identification and Equalization of CDMA Signals Using the Levenvberg-Marquardt Algorithm

In this paper we describe the Levenvberg-Marquardt (LM) algorithm for identification and equalization of CDMA signals received by an antenna array in communication channels. The synthesis explains the digital separation and equalization of signals after propagation through multipath generating intersymbol interference (ISI). Exploiting discrete data transmitted and three diversities induced at the reception, the problem can be composed by the Block Component Decomposition (BCD) of a tensor of order 3 which is a new tensor decomposition generalizing the PARAFAC decomposition. We optimize the BCD decomposition by Levenvberg-Marquardt method gives encouraging results compared to classical alternating least squares algorithm (ALS). In the equalization part, we use the Minimum Mean Square Error (MMSE) to perform the presented method. The simulation results using the LM algorithm are important.

Cost of Governance in Nigeria: In Whose Interest?

Cost of governance in Nigeria has become a challenge to development and concern to practitioners and scholars alike in the field of business and social science research. In the 2010 national budget of NGN4.6 trillion or USD28.75billion for instance, only a pantry sum of NGN1.8trillion or USD11.15billion was earmarked for capital expenditure. Similarly, in 2013, out of a total national budget of NGN4.92trillion or USD30.75billion, only the sum of NGN1.50trllion or USD9.38billion was voted for capital expenditure. Therefore, based on the data sourced from the Nigerian Office of Statistics, Central bank of Nigeria Statistical Bulletin as well as from the United Nations Development Programme, this study examined the causes of high cost of governance in Nigeria. It found out that the high cost of governance in the country is in the interest of the ruling class, arising from their unethical behaviour – corrupt practices and the poor management of public resources. As a result, the study recommends the need to intensify the war against corruption and mismanagement of public resources by government officials as possible solution to overcome the high cost of governance in Nigeria. This could be achieved by strengthening the constitutional powers of the various anti-corruption agencies in the area of arrest, investigation and prosecution of offenders without the interference of the executive arm of government either at the local, state or federal level.

Designing Back-stepping Sliding Mode Controller for a Class of 4Y Octorotor

This paper presents a combination of both robust nonlinear controller and nonlinear controller for a class of nonlinear 4Y Octorotor UAV using Back-stepping and sliding mode controller. The robustness against internal and external disturbance and decoupling control are the merits of the proposed paper. The proposed controller decouples the Octorotor dynamical system. The controller is then applied to a 4Y Octortor UAV and its feature will be shown.

An Analytical Study on Rotational Capacity of Beam-Column Joints in Unit Modular Frames

Modular structural systems are constructed using a method that they are assembled with prefabricated unit modular frames on-site. This provides a benefit that can significantly reduce building construction time. The structural design is usually carried out under the assumption that their load-carrying mechanism is similar to that of traditional steel moment-resisting systems. However, both systems are different in terms of beam-column connection details which may strongly influence the lateral structural behavior. Specially, the presence of access holes in a beam-column joint of a unit modular frame could cause undesirable failure during strong earthquakes. Therefore, this study carried out finite element analyses (FEMs) of unit modular frames to investigate the cyclic behavior of beam-column joints with the access holes. Analysis results show that the unit modular frames present stable cyclic response with large deformation capacities and their joints are classified into semi-rigid connections even if there are access holes.

Simulation Based VLSI Implementation of Fast Efficient Lossless Image Compression System Using Adjusted Binary Code & Golumb Rice Code

The Simulation based VLSI Implementation of FELICS (Fast Efficient Lossless Image Compression System) Algorithm is proposed to provide the lossless image compression and is implemented in simulation oriented VLSI (Very Large Scale Integrated). To analysis the performance of Lossless image compression and to reduce the image without losing image quality and then implemented in VLSI based FELICS algorithm. In FELICS algorithm, which consists of simplified adjusted binary code for Image compression and these compression image is converted in pixel and then implemented in VLSI domain. This parameter is used to achieve high processing speed and minimize the area and power. The simplified adjusted binary code reduces the number of arithmetic operation and achieved high processing speed. The color difference preprocessing is also proposed to improve coding efficiency with simple arithmetic operation. Although VLSI based FELICS Algorithm provides effective solution for hardware architecture design for regular pipelining data flow parallelism with four stages. With two level parallelisms, consecutive pixels can be classified into even and odd samples and the individual hardware engine is dedicated for each one. This method can be further enhanced by multilevel parallelisms.

Investigations on the Influence of Optimized Charge Air Cooling for a Diesel Passenger Car

Starting in 2020, an EU-wide CO2-limitation of 95 g/km is scheduled for the average of an OEMs passenger car fleet. Taking that into consideration additional improvement measures of the Diesel cycle are necessary in order to reduce fuel consumption and emissions while boosting, or at the least, keeping performance values at the same time. The present article deals with the possibilities of an optimized air/water charge air cooler, also called iCAC (indirect Charge Air Cooler) for a Diesel passenger car amongst extreme-boundary conditions. In this context, the precise objective was to show the impact of improved intercooling with reference to the engine working process (fuel consumption and NOx-emissions). Several extremeboundaries - e.g. varying ambient temperatures or mountainous routes - that will become very important in the near future regarding RDE (Real Driving emissions) were subject of the investigation. With the introduction of RDE in 2017 (EU6c measure), the controversial NEDC (New European Driving Cycle) will belong to the past and the OEMs will have to avoid harmful emissions in any conceivable real life situation. This is certainly going to lead to optimization-measurements at the powertrain, which again is going to make the implementation of iCACs, presently solely used for the premium class, more and more attractive for compact class cars. The investigations showed a benefit in FC between 1 and 3% for the iCAC in real world conditions.

Experimental Investigations of a Modified Taylor-Couette Flow

In this study the instability problem of a modified Taylor-Couette flow between two vertical coaxial cylinders of radius R1, R2 is considered. The modification is based on the wavy shape of the inner cylinder surface, where inner cylinders with different surface amplitude and wavelength are used. The study aims to discover the effect of the inner surface geometry on the instability phenomenon that undergoes Taylor-Couette flow. The study reveals that the transition processes depends strongly on the amplitude and wavelength of the inner cylinder surface and resulting in flow instabilities that are strongly different from that encountered in the case of the classical Taylor-Couette flow.

An Experimental Investigation of Bond Properties of Reinforcements Embedded in Geopolymer Concrete

Geopolymer concretes are new class of construction materials that have emerged as an alternative to Ordinary Portland cement concrete. Considerable researches have been carried out on material development of geopolymer concrete; however, a few studies have been reported on the structural use of them. This paper presents the bond behaviors of reinforcement embedded in fly ash based geopolymer concrete. The development lengths of reinforcement for various compressive strengths of concrete, 20, 30 and 40 MPa, and reinforcement diameters, 10, 16 and 25 mm, are investigated. Total 27 specimens were manufactured and pull-out test according to EN 10080 was applied to measure bond strength and slips between concrete and reinforcements. The average bond strengths decreased from 23.06MPa to 17.26 MPa, as the diameters of reinforcements increased from 10mm to 25mm. The compressive strength levels of geopolymer concrete showed no significant influence on bond strengths in this study. Also, the bond-slip relations between geopolymer concrete and reinforcement are derived using non-linear regression analysis for various experimental conditions.

Recognition of Grocery Products in Images Captured by Cellular Phones

In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using well-known geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

The Legal Procedure of Attestation of Public Servants

The main purpose of this research is to comprehensively explore and identify the problems of attestation of the public servants and to propose solutions for these issues through deeply analyzing laws and the legal theoretical literature. For the detailed analysis of the above-mentioned problems we will use some research methods, the implementation of which has a goal to ensure the objectivity and clarity of scientific research and its results.

Phelipanche ramosa (L. - Pomel) Control in Field Tomato Crop

The tomato is a very important crop, whose cultivation in the Mediterranean basin is severely affected by the phytoparasitic weed Phelipanche ramosa. The semiarid regions of the world are considered the main areas where this parasitic weed is established causing heavy infestation as it is able to produce high numbers of seeds (up to 500,000 per plant), which remain viable for extended period (more than 20 years). In this paper the results obtained from eleven treatments in order to control this parasitic weed including chemical, agronomic, biological and biotechnological methods compared with the untreated test under two plowing depths (30 and 50 cm) are reported. The split-plot design with 3 replicates was adopted. In 2014 a trial was performed in Foggia province (southern Italy) on processing tomato (cv Docet) grown in the field infested by Phelipanche ramosa. Tomato seedlings were transplant on May 5, on a clay-loam soil. During the growing cycle of the tomato crop, at 56-78 and 92 days after transplantation, the number of parasitic shoots emerged in each plot was detected. At tomato harvesting, on August 18, the major quantity-quality yield parameters were determined (marketable yield, mean weight, dry matter, pH, soluble solids and color of fruits). All data were subjected to analysis of variance (ANOVA) and the means were compared by Tukey's test. Each treatment studied did not provide complete control against Phelipanche ramosa. However, among the different methods tested, some of them which Fusarium, gliphosate, radicon biostimulant and Red Setter tomato cv (improved genotypes obtained by Tilling technology) under deeper plowing (50 cm depth) proved to mitigate the virulence of the Phelipanche ramose attacks. It is assumed that these effects can be improved combining some of these treatments each other, especially for a gradual and continuing reduction of the “seed bank” of the parasite in the soil.

A Four Method Framework for Fighting Software Architecture Erosion

Software Architecture is the basic structure of software that states the development and advancement of a software system. Software architecture is also considered as a significant tool for the construction of high quality software systems. A clean design leads to the control, value and beauty of software resulting in its longer life while a bad design is the cause of architectural erosion where a software evolution completely fails. This paper discusses the occurrence of software architecture erosion and presents a set of methods for the detection, declaration and prevention of architecture erosion. The causes and symptoms of architecture erosion are observed with the examples of prescriptive and descriptive architectures and the practices used to stop this erosion are also discussed by considering different types of software erosion and their affects. Consequently finding and devising the most suitable approach for fighting software architecture erosion and in some way reducing its affect is evaluated and tested on different scenarios.

Aerodynamic Analysis of a Frontal Deflector for Vehicles

This work was one of the tasks of the Manufacturing2Client project, whose objective was to develop a frontal deflector to be commercialized in the automotive industry, using new project and manufacturing methods. In this task, in particular, it was proposed to develop the ability to predict computationally the aerodynamic influence of flow in vehicles, in an effort to reduce fuel consumption in vehicles from class 3 to 8. With this aim, two deflector models were developed and their aerodynamic performance analyzed. The aerodynamic study was done using the Computational Fluid Dynamics (CFD) software Ansys CFX and allowed the calculation of the drag coefficient caused by the vehicle motion for the different configurations considered. Moreover, the reduction of diesel consumption and carbon dioxide (CO2) emissions associated with the optimized deflector geometry could be assessed.

Comparative Study Using Weka for Red Blood Cells Classification

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithms tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital - Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.