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Examines the outcomes of an evaluation exercise undertaken by tutors responsible for the delivery of contextual material to first year students on the socio‐demographic environment of business and public services. Working within the precepts of the critical paradigm of curriculum evaluation establishes the extent to which students perceive the course content to be vocational as well as their views on other aspects of the design and delivery of the unit. Describes key elements of the evaluation, namely that it was summative, tutor led, positivist and determinist, structured and quantitative. Assesses the findings which suggest that while, in general, the course is well received more could be done to strengthen its vocational orientation. Outlines how tutors have adjusted the learning and teaching strategy to make the unit more explicitly vocational and steps being taken to ensure that evaluation is a learning experience for students as well as tutors.

Describes a small research project designed to assess the contribution to this process of focus group research in the context of one undergraduate unit on a Business Studies Programme. Problems with the delivery of this unit prompted a request to the Centre for Business Education Research (a small research centre within the School of Business and Finance) by the unit leader for a review and, as a consequence, the establishment of a focus group. The process of setting up and running the focus group and an assessment of its potential contribution as a supplementary source of information about the quality of teaching, learning and assessment in higher education represents the core of the paper. There were compelling reasons for choosing a focus group methodology and the results tend to confirm the validity of this choice. Nevertheless, a number of issues surfaced during the research.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

Ann Taylor was founded in 1954, and its classic black dress and woman's power suit were staples for years. In 1995 Ann Taylor LOFT was launched to appeal to a more casual, costconscious consumer. Under Kay Krill's leadership, the division began to outperform the original flagship. When Krill was promoted to President/CEO of Ann Taylor Stores Corporation in 2005, she was challenged with rebuilding the Ann Taylor brand - (i.e., meeting the “wardrobing needs of the updated classic consumer”) while maintaining the image and market share of LOFT. By mid-2008, an additional problem appeared: the macroeconomic climate was posing considerable uncertainty, especially for retail businesses. Krill was firmly committed to long-term growth. However, given the 2008 situation, what could she do to unleash what she believed was the firm's “significant untapped potential”?

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

The purpose of this study is to examine the effects of using discrete and continuous porous layers on the convective heat transfer improvement for multiple slot jet impingement onto a flat surface under magnetic field.

In the domains which are separated by the porous layers, uniform magnetic field with different strengths is used and as the solution technique finite element method is used. The numerical study is conducted considering different values of parameters: Reynolds number (250–1000), strength of magnetic field in different domains (Hartmann number between 0 and 20), permeability of discrete or continuous layers (Darcy number between 105 and 102) and number of layers in discrete case (2–10). Artificial neural network is used for performance estimation of systems equipped with different types of porous layers.

It is observed that significant differences occur in the local Nu between the discrete and continuous layer case, especially at lower Re, while peak Nu value is 77% higher in discrete layer configurations as compared to continuous one at Re = 250. Upper domain magnetic field results in average Nu enhancement, while the trend is opposite for the lower domain magnetic field strength. The increment amount becomes 10%, while the reduction amount is obtained as 38% at the highest magnetic field strengths. The permeability of layers in both cases and number of layers in discrete porous layer case provide effective solution for the cooling performance control. A modeling approach based on artificial neural networks provides fast thermal performance estimations of multiple impinging jets equipped with discrete and continuous porous layers.

Outcomes of the study are useful in development and optimization of new cooling systems in many thermal engineering systems encountered in photovoltaic panels, micro-electro-mechanical systems, metal processing and many others.

This study aims to improve the stiffness of as-printed handles by finding appropriate printing orientations.

First, a series of benchmark handles is designed using Taguchi method. Then, for each uniformly sampled printing orientation, every benchmark handle is sliced and undergoes stiffness evaluation (i.e. displacement and mean stress) by using finite element analysis (FEA). This generates a substantial batch of handle-orientation-stiffness samples. With the data, an effective stiffness-prediction network is developed based on the artificial neural network. Finally, using the developed network, the particle swarm optimization is adapted to determine the optimized printing orientation for each input handle, aiming to improve its stiffness.

Compared with the common slicing software, the printing orientations proposed in this study, based on FEA, result in varying degrees of improvement in stiffness for four handles. Specifically, the displacement and mean stress are reduced by 16.86% and 18.14% on average. The experiments show that the approach has the potential to effectively improve the stiffness of a handle.

Although the anisotropic property in mechanics is unavoidable and difficult to formally describe in 3D printing, the proposed approach can effectively characterize the relationship between the stiffness and the printing orientation for each handle. And, it also can determine an optimized printing orientation for each handle to enhance its stiffness after printing.

Separately, nonlinear finite element analysis, artificial neural networks (ANNs) and continuous damage mechanics (CDM) attracted many investigators to model masonry infilled frames. The purpose of this paper is to pursue four phases to develop a versatile model for partially and fully low-rise infilled RC frames using these tools.

The first phase included the study of the behavior of 1,620 low-rise infilled reinforced concrete frames using macro-scale nonlinear pushover finite element analysis. The approach helped to explore the effects of imposing different masonry infill distributions for one of the typical models of school buildings in Egypt. The outputs of this phase were used in the second phase for the development of an ANN model where input neurons included number of stories, continuity conditions, frame geometry, infill distribution and properties of RC sections. The third phase included the employment of the notions of CDM on the structural scale. Monitoring frames’ stiffness degradation allowed for damage variables identification. In the fourth phase, the simpler equivalent static lateral load (ESLL) for elastic analysis was employed in conjunction with ANN and CDM to obtain the capacity curves for partially and fully low-rise infilled RC frames.

The obtained capacity curves were compared with the nonlinear finite element results. The close agreement of all curves indicated how rigorous, yet simple, the suggested solution procedure is.

The study is concerned with an important type of service buildings. These are the school buildings of Egypt.

The paper presents a combination of four phases that include FE analysis, ANNs, ESLL, and CDM to obtain the capacity curves for partially and fully low-rise infilled RC frames. Such a combination of approaches in tackling a practical problem related to service buildings is innovative and deserves research interest.

Elevated temperature material properties are essential in predicting structural member's behavior in high-temperature exposures such as fire. Even though experimental methodologies are available to determine these properties, advanced equipment with high costs is required to perform those tests. Therefore, performing those experiments frequently is not feasible, and the development of numerical techniques is beneficial. A numerical technique is proposed in this study to determine the temperature-dependent thermal properties of the material using the fire test results based on the Artificial Neural Network (ANN)-based Finite Element (FE) model.

An ANN-based FE model was developed in the Matlab program to determine the elevated temperature thermal diffusivity, thermal conductivity and the product of specific heat and density of a material. The temperature distribution obtained from fire tests is fed to the ANN-based FE model and material properties are predicted to match the temperature distribution.

Elevated temperature thermal properties of normal-weight concrete (NWC), gypsum plasterboard and lightweight concrete were predicted using the developed model, and good agreement was observed with the actual material properties measured experimentally. The developed method could be utilized to determine any materials' elevated temperature material properties numerically with the adequate temperature distribution data obtained during a fire or heat transfer test.

Temperature-dependent material properties are important in predicting the behavior of structural elements exposed to fire. This research study developed a numerical technique utilizing ANN theories to determine elevated temperature thermal diffusivity, thermal conductivity and product of specific heat and density. Experimental methods are available to evaluate the material properties at high temperatures. However, these testing equipment are expensive and sophisticated; therefore, these equipment are not popular in laboratories causing a lack of high-temperature material properties for novel materials. However conducting a fire test to evaluate fire performance of any novel material is the common practice in the industry. ANN-based FE model developed in this study could utilize those fire testing results of the structural member (temperature distribution of the member throughout the fire tests) to predict the material's thermal properties.

Doug Scovanner, CFO of Target Corporation, was about to present his proposal at the November 2008 Board meeting. He was prepared to discuss immediate strategic actions which would provide support for working capital for the discount retailer. The retail community was about to suffer their worst fourth quarter in recent memory. Consumer spending had contracted, unemployment was rising and the deflated housing market had driven the economy into a recession. Although discount retailers had fared better than other industries during the second and third quarters, they were not immune to the overall economic downturn which had become a global crisis. To further complicate matters, Target's largest competitor, Wal-Mart, just posted third quarter growth even though Target was bracing for a busy holiday season. Scovanner anticipated further strain on working capital before year-end as cash flow tightened and the capital markets remained at a virtual stand-still.