Students’ views on the incorporation of commercial awareness in real estate education

Purpose – The purpose of this paper is to review how real estate students perceive and define commercial awareness, which is one of the most important employability skills. This paper also examines students’ perceptions about how their courses support and develop their commercial awareness. In addition, it compares students’ and academics’ views on commercial awareness and identifies whether there are any gaps.

Design/methodology/approach – This paper presents the research findings of a questionnaire survey and e-mail discussions with students who are currently studying Royal Institution of Chartered Surveyors (RICS)-accredited real estate courses in the UK. The questionnaire aimed to gather students’ views on the definitions and components of commercial awareness and identify what skills and attributes are required for its development. It also evaluates how commercial awareness has been embedded in the real estate courses. The aim of each discussion was to gain deeper insight on how components of commercial awareness are embedded in real estate courses, and 17 discussions were conducted. The contents of the e-mail discussions were analysed and similar themes were identified and coded. The frequency of the answer in the questionnaire and comments from interviewees is presented. The findings from students’ views have been compared to published research reporting UK RICS-accredited real estate course providers’ views on commercial awareness. In addition to descriptive statistics, Fisher’s exact test was used to identify the statistical significance between the academics’ and students’ views on commercial awareness.

Findings – The UK real estate students agreed that the most important definition of commercial awareness is a “person’s ability to understand the economics of business”. They agreed that “financial” component is the most important component of commercial awareness and it is the largest portion of their courses. The most important skill and attribute for commercial awareness development are “critical thinking” and “ability and willingness to update professional knowledge”, respectively. Although the descriptive analysis shows students and academics have different views on the definition and components of commercial awareness and its incorporation within real estate courses, the Fisher exact test shows that only a few elements are different enough to be statistically significant. This analysis shows that while students and academics have slightly different views on commercial awareness they are not very different.  Commercial awareness is an important employability skill, thus, it is still necessary for real estate academics to re-visit the curriculum and to ensure learning outcomes related to commercial awareness have been clearly explained and communicated to students. Furthermore, it is vital for students to obtain practical experience in order to fully develop their commercial awareness. 

Originality/value – This paper is a pioneer study focused on reviewing real estate students’ views on commercial awareness, including identifying its definition, components and evaluating the extent to which commercial awareness has been embedded in their courses. It also identifies the skills and attributes that students thought were required for the development of commercial awareness.  Furthermore, it discusses students’ preferred ways of enhancing their commercial awareness as part of the course they are studying. It is the first study identifying the statistical difference between students’ and academics’ views on commercial awareness. The understanding of students’ views on commercial awareness, their preferred delivery method and the divergence between students’ and academics’ views on commercial awareness can provide useful insights for course directors on the development and renewal of real estate course curriculum

Increased left hemisphere impairment in high-functioning autism : A tract based spatial statistics study

There is evidence emerging from Diffusion Tensor Imaging (DTI) research that autism spectrum disorders (ASD) are associated with greater impairment in the left hemisphere. Although this has been quantified with volumetric region of interest analyses, it has yet to be tested with white matter integrity analysis. In the present study, tract based spatial statistics was used to contrast white matter integrity of 12 participants with high-functioning autism or Aspergers syndrome (HFA/AS) with 12 typically developing individuals. Fractional Anisotropy (FA) was examined, in addition to axial, radial and mean diffusivity (AD, RD and MD). In the left hemisphere, participants with HFA/AS demonstrated significantly reduced FA in predominantly thalamic and fronto-parietal pathways and increased RD. Symmetry analyses confirmed that in the HFA/AS group, WM disturbance was significantly greater in the left compared to right hemisphere. These findings contribute to a growing body of literature suggestive of reduced FA in ASD, and provide preliminary evidence for RD impairments in the left hemisphere.

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice.

EEG data classification using wavelet features selected by Wilcoxon statistics

This paper introduces a method to classify EEG signals using features extracted by an integration of wavelet transform and the nonparametric Wilcoxon test. Orthogonal Haar wavelet coefficients are ranked based on the Wilcoxon test’s statistics. The most prominent discriminant wavelets are assembled to form a feature set that serves as inputs to the naïve Bayes classifier. Two benchmark datasets, named Ia and Ib, downloaded from the brain–computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed combination of Haar wavelet features and naïve Bayes classifier considerably dominates the competitive classification approaches and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II. Application of naïve Bayes also provides a low computational cost approach that promotes the implementation of a potential real-time BCI system.

Teaching and research: establishing the vital link with learning

Much time and effort has gone into trying to demonstrate an empirical link between research activity and teaching performance. In general, the correlations between these factors have been shown to be low. This paper argues that the attempt to find such a link will always be confounded by different conceptions of the two enterprises. The debate about the relationships between teaching and research as presently conceived is not fruitful. It there is a link between the two it operates through that which teaching and research have in common; both are concerned with the act of learning, though in different contexts. Greater emphasis needs to be placed on the ways in which knowledge is generated and communicated. Those aspects of teaching which lead to learning and the learning which occurs through research provide the vital link. This is important if the debate is to progress beyond a political defence of the status quo and be of practical use to considerations of whether, in higher education, teaching without research is to be encouraged.

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

The influence of rare earth mercaptoacetate on the initiation of corrosion on AA2024-T3 Part I: average statistics of each intermetallic composition

De-alloying of S-phase in AA2024-T3 in the presence chlorides, is well-known. However, it is unclear how rare earth mercaptoacetate inhibitors affect this process when immersed in a 0.1. M NaCl solution. This paper analyses data obtained using EPMA on AA2024-T3 surfaces before and after a 16. min immersion period. Cerium and praseodymium mercaptoacetate inhibited the de-alloying process of S-phase particles. Although no significant change in composition was observed for cathodic intermetallics, each appeared to participate in local corrosion reactions as evidenced by the development of surface oxides. Clustering between S-phase and one of the Cu-containing intermetallic domains was also evident.

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Distribution Of Hydrological Losses For Varying Rainfall And Antecedent Wetness Conditions

Hydrological loss is a vital component in many hydrological models, which are usedin forecasting floods and evaluating water resources for both surface and subsurface flows. Due to the complex and random nature of the rainfall runoff process, hydrological losses are not yet fully understood. Consequently, practitioners often use representative values of the losses for design applications such as rainfall-runoff modelling which has led to inaccurate quantification of water quantities in the resulting applications. The existing hydrological loss models must be revisited and modellers should be encouraged to utilise other available data sets. This study is based on three unregulated catchments situated in Mt. Lofty Ranges of South Australia (SA). The paper focuses on conceptual models for: initial loss (IL), continuing loss (CL) and proportional loss (PL) with rainfall characteristics (total rainfall (TR) and storm duration (D)), and antecedent wetness (AW) conditions. The paper introduces two methods that can be implemented to estimate IL as a function of TR, D and AW. The IL distribution patterns and parameters for the study catchments are determined using multivariate analysis and descriptive statistics. The possibility of generalising the methods and the limitations of this are also discussed. This study will yield improvements to existing loss models and will encourage practitioners to utilise multiple data sets to estimate losses, instead of using hypothetical or representative values to generalise real situations.

Memórias de trabalhadores do Vital Brazil: um diálogo entre a saúde e a comunidade

O estudo teve como proposta identificar na comunidade do Morro do Vital Brazil referências que indicassem sentido de coletividade e continuidade. Essa comunidade, como pretendido apontar, teria sua origem, nas décadas de 1920 e 1930, no entorno do Instituto de Hygiene, Sorotherapia e Veterinária do Estado do Rio de Janeiro, hoje, Instituto Vital Brazil. Por ser fábrica farmacêutica, a produção necessitaria de mão-de-obra, que no caso estudado, passou a morar no morro atrás do Instituto, mas ainda em seu território. Dessa origem, surge uma comunidade com características de cooperação, união e associativismo. Com a prosperidade da fábrica, cresce o número de moradores e inicia-se um conjunto de domicílios e famílias também possuidores de aspectos em comum. Essas identidades possibilitam um encontro com o poder público na forma de políticos e políticas, como o Programa Médico de Família. Com esse último, interesse inicial da pesquisa, nasceu a relação entre a pesquisadora em questão, médica no posto PMF Vital Brazil, e evidenciou performances dos moradores que indicavam um pertencimento e lugar de fala diferenciado. O estudo apontou características da comunidade e dos atores que contribuíram na criação desse coletivo, utilizou como metodologia a história oral.

Mensuração de Capacidade Tecnológica no Contexto de Industrialização Recente: Uma Breve Reflexão Crítica Sobre Taxonomias e Evidências de Pesquisas Recentes

This dissertation aims at examining empirical evidences obtained in the light of taxonomies and strategies for measuring firms technological capabilities and innovation in the context of developing countries, motivated by the fact that debates and studies directed to innovation has been intensified, for the last thirty years, by the recognition of its vital and growing importance to the technological, economic, competitive and industrial development of firms and countries. Two main tendencies can be identified on this debate. At one side, it¿s the literature related to the developed countries logic, whose companies are, in majority, positioned at the technological frontier, characterized by the domain of innovative advanced capabilities, directed to its sustaining, deepening and renewal. At the other side, there are the perspectives directed to the developing countries reality, where there is a prevalence of companies with deficiency of resources, still in process of accumulating basic and intermediate technological capabilities, with characteristics and technological development trajectories distinct or even reverse from those of developing countries. From this last tradition of studies, the measuring approaches based in C&T indicators and in types and levels of technological capabilities stand out. The first offers a macro level, aggregated perspective, through the analysis of a representative sample of firms, seeking to the generation of internationally comparable data, without addressing the intraorganizational specificities and nuances of the paths of technological accumulation developed by the firms, using, mostly, R&D statistics, patents, individual qualifications, indicators that carry their own limitations. On the other hand, studies that examine types and levels of technological capabilities are scarce, usually directed to a small sample of firms and/or industrial sectors. Therefore, in the light of the focus and potentialities of each of the perspectives, this scenario exposes a lack of studies that examine, in a parallel and complementary way, both types of strategies, seeking to offer more realistic, consistent and concrete information about the technological reality of developing countries. In order to close this gap, this dissertation examines (i) strategies of innovation measurement in the contexts of developing countries based on traditional approaches and C&T indicators, represented by four innovation surveys - ECIB, PINTEC, PAEP and EAI, and, (ii) from the perspective of technological capabilities as an intrinsic resource of the firm, the development of which occurs in a cumulative way and based on learning, presents and extracts generalizations of empirical applications of a metric that identifies types and levels of technological capabilities, through a dynamic and intra-firm perspective. The exam of the empirical evidences of the two approaches showed what each one of the metrics are capable to offer and the way they can contribute to the generation of information that reflect the technological development of specific industrial sectors in developing countries. In spite of the fact that the focus, objective, perspective, inclusion, scope and lens used are substantially distinct, generating, on a side, an aggregated view, and of other, an intra-sector, intra-organizational and specific view, the results suggest that the use of one doesn't implicate discarding or abdicating the other. On the contrary, using both in a complementary way means the generation of more complete, rich and relevant evidences and analysis that offer a realistic notion of the industrial development and contribute in a more direct way to the design of corporate strategies and government policies, including those directed to the macro level aspects just as those more specific and focused, designed to increment and foment firms in-house innovative efforts.