Adding Olive Oil into Diluents for Improving Semen Quality and Storage Ability of Roosters' Semen during Liquid Storage

The aim of this study was to investigate the effects of supplementing the diluent of roosters' semen with different levels of olive oil on motility, viability, morphology and acrosome integrity of chicken spermatozoa after in vitro storage for up to 72 h. Semen was collected from 60 White Layer males (62 wk of age) kept in separated floor pens and randomly divided into six treatment groups (10 males in each group). Experimental groups were as follows: T1 :fresh semen, T2 : semen extended 1:1 with Al – Daraji 2 diluent (AD2D) alone, T3 – T6 :semen samples extended 1:1 with AD2D supplemented with 2 ml, 4 ml, 6 ml or 8 ml of olive oil / 100 ml of diluent, respectively. Semen samples were then stored at 5 °C for 24 h, 48 h or 72 h. There was a clear influence of diluent supplementation with olive oil on the spermatozoa motility profile; olive oil groups (T3, T4, T5 and T6) recorded the highest scores of mass activity and individual motility during all storage periods compared to T1 and T2 groups. In addition, the inclusion of olive oil into semen diluent (T3, T4, T5 and T6) gave significantly higher percentages of viable spermatozoa, normal morphologically spermatozoa and intact acrosomes irrespective of storage period. These results clearly show that supplementation the diluent of roosters' semen with olive oil can improve semen quality when semen samples in vitro stored at 5 °C for up to 72 h.

Effect of Utilization of Geosynthetic on Reducing the Required Thickness of Subbase Layer of a Two Layered Soil

This paper tries to study the effect of geosynthetic inclusion on the improvement of the load-settlement characters of two layered soil. In addition, the effect of geogrid and geotextile in reduction of the required thickness of subbase layer in unpaved roads is studied. Considering the vast application of bearing ratio tests in road construction projects, this test is used in present investigation. Bearing ratio tests were performed on two layered soil including a granular soil layer at the top (as the subbase layer) and a weak clayey soil placed at the bottom (as the subgrade layer). These tests were performed for different conditions including unreinforced and reinforced by geogrid and geotextile and three thicknesses for top layer soil (subbase layer). In the reinforced condition the reinforcing element was placed on the interface of the top granular layer and the beneath clayey layer to study the separation effect of geosynthetics. In all tests the soils (both granular and clayey soil layers) were compacted according to optimum water content. At the end, the diagrams were plotted and were compared with each other. Furthermore, a comparison between geogrids and geotextiles behaviors on two layer soil is done in this paper. The results show an increase in compression strength of reinforced specimen in comparison with unreinforced soil sample. The effect of geosynthetic inclusion reduces by increasing the subbase thickness. In addition it was found that geogrids have more desirable behavior rather than geotextiles due to interlocking with the subbase layer aggregates.

An Examination of Backing Effects on Ratings for Masonry Arch Bridges

Many single or multispan arch bridges are strengthened with the addition of some kind of structural support between adjacent arches of multispan or beside the arch barrel of a single span to increase the strength of the overall structure. It was traditionally formed by either placing loose rubble masonry blocks between the arches and beside the arches or using mortar or concrete to construct a more substantial structural bond between the spans. On the other hand backing materials are present in some existing bridges. Existing arch assessment procedures generally ignore the effects of backing materials. In this paper an investigation of the effects of backing on ratings for masonry arch bridges is carried out. It is observed that increasing the overall lateral stability of the arch system through the inclusion of structural backing results in an enhanced failure load by reducing the likelihood of any tension occurring at the top of the arch.

Maydis stigma Improves Physical Traits and Unchanged Sensory Properties of Beef and Chicken Patties

The proximate composition, physical traits and sensory properties of beef and chicken patties incorporated with various level of dried cornsilk (Maydis stigma) were studied. The beef and chicken patties were formulated with either 2%, 4% or 6% of cornsilk. Both cooked beef and chicken patties incorporated with 6% cornsilk recorded the highest protein concentration at 23.3% and 28.42%, respectively. Both cooked beef and chicken patties containing 6% cornsilk significantly recorded the lowest concentration of fat at 11.4% and 14.60%, respectively. Beef and chicken patties formulated with 6% cornsilk recorded the highest cooking yield at 80.13% and 83.03% compared to other treatments. The inclusion of cornsilk did not change the sensory properties and consumer acceptability of cornsilk-based beef and chicken patties. Cornsilk fibre has been effective in improving cooking yield, moisture and fat retention of beef and chicken patties

On the Coupled Electromechanical Behavior of Artificial Materials with Chiral-Shell Elements

In the present work we investigate both the elastic and electric properties of a chiral material. We consider a composite structure made from a polymer matrix and anisotropic inclusions of GaAs taking into account piezoelectric and dielectric properties of the composite material. The principal task of the work is the estimation of the functional properties of the composite material.

Evaluation of Ultrasonic C-Scan Images by Fractal Dimension

In this paper, quantitative evaluation of ultrasonic Cscan images through estimation of their Fractal Dimension (FD) is discussed. Necessary algorithm for evaluation of FD of any 2-D digitized image is implemented by developing a computer code. For the evaluation purpose several C-scan images of the Kevlar composite impacted by high speed bullet and glass fibre composite having flaw in the form of inclusion is used. This analysis automatically differentiates a C-scan image showing distinct damage zone, from an image that contains no such damage.

Semantic Modeling of Management Information: Enabling Automatic Reasoning on DMTF-CIM

CIM is the standard formalism for modeling management information developed by the Distributed Management Task Force (DMTF) in the context of its WBEM proposal, designed to provide a conceptual view of the managed environment. In this paper, we propose the inclusion of formal knowledge representation techniques, based on Description Logics (DLs) and the Web Ontology Language (OWL), in CIM-based conceptual modeling, and then we examine the benefits of such a decision. The proposal is specified as a CIM metamodel level mapping to a highly expressive subset of DLs capable of capturing all the semantics of the models. The paper shows how the proposed mapping can be used for automatic reasoning about the management information models, as a design aid, by means of new-generation CASE tools, thanks to the use of state-of-the-art automatic reasoning systems that support the proposed logic and use algorithms that are sound and complete with respect to the semantics. Such a CASE tool framework has been developed by the authors and its architecture is also introduced. The proposed formalization is not only useful at design time, but also at run time through the use of rational autonomous agents, in response to a need recently recognized by the DMTF.

A New Source Code Auditing Algorithm for Detecting LFI and RFI in PHP Programs

Static analysis of source code is used for auditing web applications to detect the vulnerabilities. In this paper, we propose a new algorithm to analyze the PHP source code for detecting LFI and RFI potential vulnerabilities. In our approach, we first define some patterns for finding some functions which have potential to be abused because of unhandled user inputs. More precisely, we use regular expression as a fast and simple method to define some patterns for detection of vulnerabilities. As inclusion functions could be also used in a safe way, there could occur many false positives (FP). The first cause of these FP-s could be that the function does not use a usersupplied variable as an argument. So, we extract a list of usersupplied variables to be used for detecting vulnerable lines of code. On the other side, as vulnerability could spread among the variables like by multi-level assignment, we also try to extract the hidden usersupplied variables. We use the resulted list to decrease the false positives of our method. Finally, as there exist some ways to prevent the vulnerability of inclusion functions, we define also some patterns to detect them and decrease our false positives.

A Novel Slip Correction Factor for Spherical Aerosol Particles

A 3D simulation study for an incompressible slip flow around a spherical aerosol particle was performed. The full Navier-Stokes equations were solved and the velocity jump at the gas-particle interface was treated numerically by imposition of the slip boundary condition. Analytical solution to the Stokesian slip flow past a spherical particle was used as a benchmark for code verification, and excellent agreement was achieved. The Simulation results showed that in addition to the Knudsen number, the Reynolds number affects the slip correction factor. Thus, the Cunningham-based slip corrections must be augmented by the inclusion of the effect of Reynolds number for application to Lagrangian tracking of fine particles. A new expression for the slip correction factor as a function of both Knudsen number and Reynolds number was developed.

Powerful Tool to Expand Business Intelligence: Text Mining

With the extensive inclusion of document, especially text, in the business systems, data mining does not cover the full scope of Business Intelligence. Data mining cannot deliver its impact on extracting useful details from the large collection of unstructured and semi-structured written materials based on natural languages. The most pressing issue is to draw the potential business intelligence from text. In order to gain competitive advantages for the business, it is necessary to develop the new powerful tool, text mining, to expand the scope of business intelligence. In this paper, we will work out the strong points of text mining in extracting business intelligence from huge amount of textual information sources within business systems. We will apply text mining to each stage of Business Intelligence systems to prove that text mining is the powerful tool to expand the scope of BI. After reviewing basic definitions and some related technologies, we will discuss the relationship and the benefits of these to text mining. Some examples and applications of text mining will also be given. The motivation behind is to develop new approach to effective and efficient textual information analysis. Thus we can expand the scope of Business Intelligence using the powerful tool, text mining.

Using Serious Games to Improve the Preparation of Pre-Service Teachers in Bulgaria

This paper presents the outcomes of a qualitative study which aims to investigate the pedagogical potentials of serious games in the preparation of future teachers. The authors discuss the existing problems and barriers associated with the organization of teaching practices in Bulgaria as part of the pre-service teacher training, as well as the attitudes and perceptions of the interviewed academics, teachers and trainees concerning the integration of serious games in the teaching practicum. The study outcomes strongly confirm the positive attitudes of the respondents to the introduction of virtual learning environments for the development of professional skills of future teachers as a supplement to the traditional forms of education. Through the inclusion of serious games it is expected to improve the quality of practical training of pre-service teachers as they overcome many of the problems identified in the existing teaching practices. The outcomes of the study will inform the design of the educational simulation software which is part of the project SimAula Tomorrow's Teachers Training.

Effect of Natural Fibres Inclusion in Clay Bricks: Physico-Mechanical Properties

In spite of the advent of new materials, clay bricks remain, arguably, the most popular construction materials today. Nevertheless the low cost and versatility of clay bricks cannot always be associated with high environmental and sustainable values, especially in terms of raw material sources and manufacturing processes. At the same time, the worldwide agricultural footprint is fast growing, with vast agricultural land cultivation and active expansion of the agro-based industry. The resulting large quantities of agricultural wastes, unfortunately, are not always well managed or utilised. These wastes can be recycled, such as by retrieving fibres from disposed leaves and fruit bunches, and then incorporated in brick-making. This way the clay bricks are made a 'greener' building material and the discarded natural wastes can be reutilised, avoiding otherwise wasteful landfill and harmful open incineration. This study examined the physical and mechanical properties of clay bricks made by adding two natural fibres to a clay-water mixture, with baked and non-baked conditions. The fibres were sourced from pineapple leaves (PF) and oil palm fruit bunch (OF), and added within the range of 0.25-0.75 %. Cement was added as a binder to the mixture at 5-15 %. Although the two fibres had different effects on the bricks produced, cement appeared to dominate the compressive strength. The non-baked bricks disintegrated when submerged in water, while the baked ones displayed cement-dependent characteristics in water-absorption and density changes. Interestingly, further increase in fibre content did not cause significant density decrease in both the baked and non-baked bricks.

A Robust Approach to the Load Frequency Control Problem with Speed Regulation Uncertainty

The load frequency control problem of power systems has attracted a lot of attention from engineers and researchers over the years. Increasing and quickly changing load demand, coupled with the inclusion of more generators with high variability (solar and wind power generators) on the network are making power systems more difficult to regulate. Frequency changes are unavoidable but regulatory authorities require that these changes remain within a certain bound. Engineers are required to perform the tricky task of adjusting the control system to maintain the frequency within tolerated bounds. It is well known that to minimize frequency variations, a large proportional feedback gain (speed regulation constant) is desirable. However, this improvement in performance using proportional feedback comes about at the expense of a reduced stability margin and also allows some steady-state error. A conventional PI controller is then included as a secondary control loop to drive the steadystate error to zero. In this paper, we propose a robust controller to replace the conventional PI controller which guarantees performance and stability of the power system over the range of variation of the speed regulation constant. Simulation results are shown to validate the superiority of the proposed approach on a simple single-area power system model.

Defining a Semantic Web-based Framework for Enabling Automatic Reasoning on CIM-based Management Platforms

CIM is the standard formalism for modeling management information developed by the Distributed Management Task Force (DMTF) in the context of its WBEM proposal, designed to provide a conceptual view of the managed environment. In this paper, we propose the inclusion of formal knowledge representation techniques, based on Description Logics (DLs) and the Web Ontology Language (OWL), in CIM-based conceptual modeling, and then we examine the benefits of such a decision. The proposal is specified as a CIM metamodel level mapping to a highly expressive subset of DLs capable of capturing all the semantics of the models. The paper shows how the proposed mapping provides CIM diagrams with precise semantics and can be used for automatic reasoning about the management information models, as a design aid, by means of newgeneration CASE tools, thanks to the use of state-of-the-art automatic reasoning systems that support the proposed logic and use algorithms that are sound and complete with respect to the semantics. Such a CASE tool framework has been developed by the authors and its architecture is also introduced. The proposed formalization is not only useful at design time, but also at run time through the use of rational autonomous agents, in response to a need recently recognized by the DMTF.

Performance Evaluation of Neural Network Prediction for Data Prefetching in Embedded Applications

Embedded systems need to respect stringent real time constraints. Various hardware components included in such systems such as cache memories exhibit variability and therefore affect execution time. Indeed, a cache memory access from an embedded microprocessor might result in a cache hit where the data is available or a cache miss and the data need to be fetched with an additional delay from an external memory. It is therefore highly desirable to predict future memory accesses during execution in order to appropriately prefetch data without incurring delays. In this paper, we evaluate the potential of several artificial neural networks for the prediction of instruction memory addresses. Neural network have the potential to tackle the nonlinear behavior observed in memory accesses during program execution and their demonstrated numerous hardware implementation emphasize this choice over traditional forecasting techniques for their inclusion in embedded systems. However, embedded applications execute millions of instructions and therefore millions of addresses to be predicted. This very challenging problem of neural network based prediction of large time series is approached in this paper by evaluating various neural network architectures based on the recurrent neural network paradigm with pre-processing based on the Self Organizing Map (SOM) classification technique.

On Submaximality in Intuitionistic Topological Spaces

In this study, a minimal submaximal element of LIT(X) (the lattice of all intuitionistic topologies for X, ordered by inclusion) is determined. Afterwards, a new contractive property, intuitionistic mega-connectedness, is defined. We show that the submaximality and mega-connectedness are not complementary intuitionistic topological invariants by identifying those members of LIT(X) which are intuitionistic mega-connected.

Inclusive Housing in Australia – A Voluntary Response

The lack of inclusive housing in Australia contributes to the marginalization and exclusion of people with disability and older people from family and community life. The Australian government has handed over the responsibility of increasing the supply of inclusive housing to the housing industry through an agreed national access standard and a voluntary strategy. Voluntary strategies have not been successful in other constituencies and little is known about what would work in Australia today. Findings from a research project into the voluntariness of the housing industry indicate that a reliable and consistent supply is unlikely without an equivalent increase in demand. The strategy has, however, an important role to play in the task of changing housing industry practices towards building more inclusive communities.

An Enhanced Artificial Neural Network for Air Temperature Prediction

The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. An improved model for temperature prediction in Georgia was developed by including information on seasonality and modifying parameters of an existing artificial neural network model. Alternative models were compared by instantiating and training multiple networks for each model. The inclusion of up to 24 hours of prior weather information and inputs reflecting the day of year were among improvements that reduced average four-hour prediction error by 0.18°C compared to the prior model. Results strongly suggest model developers should instantiate and train multiple networks with different initial weights to establish appropriate model parameters.

Weld Defect Detection in Industrial Radiography Based Digital Image Processing

Industrial radiography is a famous technique for the identification and evaluation of discontinuities, or defects, such as cracks, porosity and foreign inclusions found in welded joints. Although this technique has been well developed, improving both the inspection process and operating time, it does suffer from several drawbacks. The poor quality of radiographic images is due to the physical nature of radiography as well as small size of the defects and their poor orientation relatively to the size and thickness of the evaluated parts. Digital image processing techniques allow the interpretation of the image to be automated, avoiding the presence of human operators making the inspection system more reliable, reproducible and faster. This paper describes our attempt to develop and implement digital image processing algorithms for the purpose of automatic defect detection in radiographic images. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of global and local preprocessing and segmentation methods must be appropriated.

The Application of Real Options to Capital Budgeting

Real options theory suggests that managerial flexibility embedded within irreversible investments can account for a significant value in project valuation. Although the argument has become the dominant focus of capital investment theory over decades, yet recent survey literature in capital budgeting indicates that corporate practitioners still do not explicitly apply real options in investment decisions. In this paper, we explore how real options decision criteria can be transformed into equivalent capital budgeting criteria under the consideration of uncertainty, assuming that underlying stochastic process follows a geometric Brownian motion (GBM), a mixed diffusion-jump (MX), or a mean-reverting process (MR). These equivalent valuation techniques can be readily decomposed into conventional investment rules and “option impacts", the latter of which describe the impacts on optimal investment rules with the option value considered. Based on numerical analysis and Monte Carlo simulation, three major findings are derived. First, it is shown that real options could be successfully integrated into the mindset of conventional capital budgeting. Second, the inclusion of option impacts tends to delay investment. It is indicated that the delay effect is the most significant under a GBM process and the least significant under a MR process. Third, it is optimal to adopt the new capital budgeting criteria in investment decision-making and adopting a suboptimal investment rule without considering real options could lead to a substantial loss in value.