Assessment of practicality of remote sensing techniques for a study of the effects of strip mining in Alabama

Because of the volume of coal produced by strip mining, the proximity of mining operations, and the diversity of mining methods (e.g. contour stripping, area stripping, multiple seam stripping, and augering, as well as underground mining), the Warrior Coal Basin seemed best suited for initial studies on the physical impact of strip mining in Alabama. Two test sites, (Cordova and Searles) representative of the various strip mining techniques and environmental problems, were chosen for intensive studies of the correlation between remote sensing and ground truth data. Efforts were eventually concentrated in the Searles Area, since it is more accessible and offers a better opportunity for study of erosional and depositional processes than the Cordova Area

Diagnostic potential of near-infrared Raman spectroscopy in the stomach: differentiating dysplasia from normal tissue

Raman spectroscopy is a molecular vibrational spectroscopic technique that is capable of optically probing the biomolecular changes associated with diseased transformation. The purpose of this study was to explore near-infrared (NIR) Raman spectroscopy for identifying dysplasia from normal gastric mucosa tissue. A rapid-acquisition dispersive-type NIR Raman system was utilised for tissue Raman spectroscopic measurements at 785 nm laser excitation. A total of 76 gastric tissue samples obtained from 44 patients who underwent endoscopy investigation or gastrectomy operation were used in this study. The histopathological examinations showed that 55 tissue specimens were normal and 21 were dysplasia. Both the empirical approach and multivariate statistical techniques, including principal components analysis (PCA), and linear discriminant analysis (LDA), together with the leave-one-sample-out cross-validation method, were employed to develop effective diagnostic algorithms for classification of Raman spectra between normal and dysplastic gastric tissues. High-quality Raman spectra in the range of 800–1800 cm−1 can be acquired from gastric tissue within 5 s. There are specific spectral differences in Raman spectra between normal and dysplasia tissue, particularly in the spectral ranges of 1200–1500 cm−1 and 1600–1800 cm−1, which contained signals related to amide III and amide I of proteins, CH3CH2 twisting of proteins/nucleic acids, and the C=C stretching mode of phospholipids, respectively. The empirical diagnostic algorithm based on the ratio of the Raman peak intensity at 875 cm−1 to the peak intensity at 1450 cm−1 gave the diagnostic sensitivity of 85.7% and specificity of 80.0%, whereas the diagnostic algorithms based on PCA-LDA yielded the diagnostic sensitivity of 95.2% and specificity 90.9% for separating dysplasia from normal gastric tissue. Receiver operating characteristic (ROC) curves further confirmed that the most effective diagnostic algorithm can be derived from the PCA-LDA technique. Therefore, NIR Raman spectroscopy in conjunction with multivariate statistical technique has potential for rapid diagnosis of dysplasia in the stomach based on the optical evaluation of spectral features of biomolecules

The Impacts of Infrastructure in Development: A Selective Survey

Development economists have considered physical infrastructure to be a precondition for industrialization and economic development. Yet, two issues remain to be addressed in the literature. First, while proper identification of the causal effectiveness of infrastructure in reducing poverty is important, experimental evaluation, such as randomized control trials (RCT)-based evaluation, is difficult in the context of large-scale infrastructure. Second, while micro studies so far have focused on the nexus between infrastructure and certain types of poverty outcomes such as income, poverty, health, education, and other individual socio-economic outcomes, to better interpret a wide variety of micro-level infrastructure evaluation results using either experimental or non-experimental methods, the role of infrastructure should be placed in a broader context. To bridge these gaps, we augment the existing review articles on the same topic, such as Estache (2010), Hansen, Andersen, and White, (2012), and World Bank (2012) by addressing these two remaining issues. First, while forming a counterfactual is often difficult for impact evaluation of infrastructure, engineering constraints beyond human manipulation can allow people to adopt quasi-experimental methods of impact evaluation. Second, evaluators can adopt, for example, a hybrid method of natural and artefactual field experiments to elicit the role of infrastructure in facilitating the complementarity of the market, state, and community mechanisms

Forgotten Sources of Capital for the Family-Owned Business

The recent scandals on Wall Street in the banking and savings and loan industries have created a financial crisis for many family businesses, particularly those in smaller towns and cities. The long-standing personal relationships with financial intermediaries have been altered by the loss of these financial organizations and by heightened government intervention and regulation. To manage the finances of a family business successfully, the owners must reassess forgotten sources of capital for their businesses. This article examines these sources of capital for family businesses in the United States.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Examining mindfulness and its relation to self-differentiation and alexithymia

Published online first in 10 July 2013Research supports the association between mindfulness, emotion regulation, stress reduction, and interpersonal/relational wellness. The present study evaluated the potential effect of mindfulness on some indicators of psychological imbalance such as low self-differentiation and alexithymia. In this cross-sectional study, a sample of 168 undergraduates (72 % women) completed measures of perceived mindfulness (CAMS-R and PHLMS), self-differentiation (SIPI), and alexithymia (TAS-20). Results revealed positive correlations between the different dimensions of mindfulness and negative correlations between those dimensions, selfdifferentiation, and alexithymia. The dimensions of quality of mindfulness and acceptance were mediators in the relationship between self-differentiation and alexithymia. A nonsignificant interaction between gender and alexithymia was found. All mindfulness dimensions, but self-differentiation, contributed to explain the allocation of the non-alexithymic group. These results indicate that mindfulness seems to be a construct with great therapeutic and research potential at different levels, suggesting that some aspects of mindfulness seem to promote a better self-differentiation and prevent alexithymia

Application of Probabilistic Neural Networks in Modelling Structural Deterioration of Stormwater Pipes

In Australia, when stormwater systems were first introduced over 100 years ago, they were constructed independently of the sewer systems, and they are normally the responsibility of the third level of government, i.e., local government or city councils. Because of the increasing age of these stormwater systems and their worsening performance, there are serious concerns in a significant number of city councils regarding their deterioration. A study has been conducted on the structural deterioration of concrete pipes that make up the bulk of the stormwater pipe systems in these councils. In an attempt to look for a reliable deterioration model, a probabilistic neural network (PNN) model was developed using the data set supplied from participating councils. The PNN model was validated with snapshot-based sample data, which makes up the data set. The predictive performance of the PNN model was compared with a traditional parametric model using discriminant analysis on the same data set. Structural deterioration was hypothesised to be influenced by a set of explanatory factors, including pipe design and construction factors—such as pipe size, buried depth—and site factors— such as soil type, moisture index, tree root intrusion, etc. The results show that the PNN model has a better predictive power and uses significantly more input variables (i.e., explanatory factors) than the discriminant model. More importantly, the key factors for prediction in the PNN model are difficult to interpret, suggesting that besides prediction accuracy, model interpretation is an important issue for further investigation