Development and evaluation of training in culturally specific screening and brief intervention for hospital patients with alcohol-related injuries

Objective To evaluate health practitioners’ confidence and knowledge of alcohol screening, brief intervention and referral after training in a culturally adapted intervention on alcohol misuse and well-being issues for trauma patients. Design Mixed methods, involving semi-structured interviews at baseline and a post-workshop questionnaire. Setting: Targeted acute care within a remote area major tertiary referral hospital. Participants Ten key informants and 69 questionnaire respondents from relevant community services and hospital-based health care professionals. Intervention Screening and brief intervention training workshops and resources for 59 hospital staff. Main outcome measures Self-reported staff knowledge of alcohol screening, brief intervention and referral, and satisfaction with workshop content and format. Results After training, 44% of participants reported being motivated to implement alcohol screening and intervention. Satisfaction with training was high, and most participants reported that their knowledge of screening and brief intervention was improved. Conclusion Targeted educational interventions can improve the knowledge and confidence of inpatient staff who manage patients at high risk of alcohol use disorder. Further research is needed to determine the duration of the effect and influence on practice behaviour. Ongoing integrated training, linked with systemic support and established quality improvement processes, is required to facilitate sustained change and widespread dissemination.

Choice of the End Functional Groups in Tri(p-phenylenevinylene) Derivatives Controls Its Physical Gelation Abilities

New supramolecular organogels based on all-trans-tri(p-phenylenevinylene) (TPV) systems possessing different terminal groups, e.g., oxime, hydrazone, phenylhydrazone, and semicarbazone have been synthesized. The self-assembly properties of the compounds that gelate in specific organic solvents and the aggregation motifs of these molecules in the organogels were investigated using UV−vis, fluorescence, FT-IR, and 1H NMR spectroscopy, electron microscopy, differential scanning calorimetry (DSC), and rheology. The temperature variable UV−vis and fluorescence spectroscopy in different solvents clearly show the aggregation pattern of the self-assemblies promoted by hydrogen bonding, aromatic π-stacking, and van der Waals interactions among the individual TPV units. Gelation could be controlled by variation in the number of hydrogen-bonding donors and acceptors in the terminal functional groups of this class of gelators. Also wherever gelation is observed, the individual fibers in gels change to other types of networks in their aggregates depending on the number of hydrogen-bonding sites in the terminal functions. Comparison of the thermal stability of the gels obtained from DSC data of different gelators demonstrates higher phase transition temperature and enthalpy for the hydrazone-based gelator. Rheological studies indicate that the presence of more hydrogen-bonding donors in the periphery of the gelator molecules makes the gel more viscoelastic solidlike. However, in the presence of more numbers of hydrogen-bonding donor/acceptors at the periphery of TPVs such as with semicarbazone a precipitation as opposed to gelation was observed. Clearly, the choice of the end functional groups and the number of hydrogen-bonding groups in the TPV backbone holds the key and modulates the effective length of the chromophore, resulting in interesting optical properties.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

A note on the choice of interfacial friction angle

Estimates of interfacial friction angle (delta) are necessary for the design of retaining structures and deep foundations, Recommendations in the literature regarding delta values are often contradictory and are therefore not easy to apply in geotechnical design, A critical examination of past studies in terms of data generation techniques used and conclusions drawn indicates that two distinctly different test procedures/techniques have been evolved. The interfacial situation in practice can also be categorized into two broad types, These two types of interface problems in geotechnical engineering are (a) the structure is placed on the free surface of prepared fill (type A situation) and (b) the fill is placed against the material surface which functions as a confined boundary (type B situation), The friction angle delta depends on the surface roughness of the construction material, But in the type A situation, it is independent of density and its limiting maximum value (delta(lim)) is the critical state friction angle phi(cv). In the type B situation, it is dependent on density of the fill and its limiting maximum value is the peak angle of internal friction phi(p) of the fill.

Choice of Classifiers in Hierarchical Recognition of Online Handwritten Kannada and Tamil Aksharas

In this paper, we propose a novel dexterous technique for fast and accurate recognition of online handwritten Kannada and Tamil characters. Based on the primary classifier output and prior knowledge, the best classifier is chosen from set of three classifiers for second stage classification. Prior knowledge is obtained through analysis of the confusion matrix of primary classifier which helped in identifying the multiple sets of confused characters. Further, studies were carried out to check the performance of secondary classifiers in disambiguating among the confusion sets. Using this technique we have achieved an average accuracy of 92.6% for Kannada characters on the MILE lab dataset and 90.2% for Tamil characters on the HP Labs dataset.

Data-resolution based optimal choice of minimum required measurements for image-guided diffuse optical tomography

Image-guided diffuse optical tomography has the advantage of reducing the total number of optical parameters being reconstructed to the number of distinct tissue types identified by the traditional imaging modality, converting the optical image-reconstruction problem from underdetermined in nature to overdetermined. In such cases, the minimum required measurements might be far less compared to those of the traditional diffuse optical imaging. An approach to choose these optimally based on a data-resolution matrix is proposed, and it is shown that such a choice does not compromise the reconstruction performance. (C) 2013 Optical Society of America

A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography

Purpose: Developing a computationally efficient automated method for the optimal choice of regularization parameter in diffuse optical tomography. Methods: The least-squares QR (LSQR)-type method that uses Lanczos bidiagonalization is known to be computationally efficient in performing the reconstruction procedure in diffuse optical tomography. The same is effectively deployed via an optimization procedure that uses the simplex method to find the optimal regularization parameter. The proposed LSQR-type method is compared with the traditional methods such as L-curve, generalized cross-validation (GCV), and recently proposed minimal residual method (MRM)-based choice of regularization parameter using numerical and experimental phantom data. Results: The results indicate that the proposed LSQR-type and MRM-based methods performance in terms of reconstructed image quality is similar and superior compared to L-curve and GCV-based methods. The proposed method computational complexity is at least five times lower compared to MRM-based method, making it an optimal technique. Conclusions: The LSQR-type method was able to overcome the inherent limitation of computationally expensive nature of MRM-based automated way finding the optimal regularization parameter in diffuse optical tomographic imaging, making this method more suitable to be deployed in real-time. (C) 2013 American Association of Physicists in Medicine. http://dx.doi.org/10.1118/1.4792459]

Induction of Supramolecular Chirality in the Self-Assemblies of Lipophilic Pyrimidine Derivatives by Choice of the Amino Acid-Based Chiral Spacer

A new family of supramolecular organogelators, based on chiral amino acid derivatives of 2,4,6-trichloro-pyrimidine-5-carbaldehyde, has been synthesized. L-alanine was incorporated as a spacer between the pyrimidine core and long hydrocarbon tails to compare the effect of chirality and hydrogen bonding to that of the achiral analogue. The role of aromatic moiety on the chiral spacer was also investigated by introducing L-phenyl alanine moieties. The presence of intermolecular hydrogen-bonding leading to the chiral self-assembly was probed by concentration-dependent FTIR and UV/Vis spectroscopies, in addition to circular dichroism (CD) studies. Temperature and concentration-dependent CD spectroscopy ascribed to the formation of -sheet-type H-bonded networks. The morphology and the arrangements of the molecules in the freeze-dried gels were examined by scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), and X-ray diffraction (XRD) techniques. Calculation of the length of each molecular system by energy minimization in its extended conformation and comparison with the small-angle XRD pattern reveals that this class of gelator molecules adopts a lamellar organization. Polarized optical microscopy (POM) and differential scanning calorimetry (DSC) indicate that the solid state phase behavior of these molecules is totally dependent on the choice of their amino acid spacers. Structure-induced aggregation properties based on the H-bonding motifs and the packing of the molecule in three dimensions leading to gelation was elucidated by rheological studies. However, viscoelasticity was shown to depend only marginally on the H-bonding interactions; rather it depends on the packing of the gelators to a greater extent.

Effect of choice of basis functions in neural network for capturing unknown function for dynamic inversion control

The basic requirement for an autopilot is fast response and minimum steady state error for better guidance performance. The highly nonlinear nature of the missile dynamics due to the severe kinematic and inertial coupling of the missile airframe as well as the aerodynamics has been a challenge for an autopilot that is required to have satisfactory performance for all flight conditions in probable engagements. Dynamic inversion is very popular nonlinear controller for this kind of scenario. But the drawback of this controller is that it is sensitive to parameter perturbation. To overcome this problem, neural network has been used to capture the parameter uncertainty on line. The choice of basis function plays the major role in capturing the unknown dynamics. Here in this paper, many basis function has been studied for approximation of unknown dynamics. Cosine basis function has yield the best response compared to any other basis function for capturing the unknown dynamics. Neural network with Cosine basis function has improved the autopilot performance as well as robustness compared to Dynamic inversion without Neural network.

Choice of Flexible Production Technologies Under Strategic Delegation

This work analyzes a managerial delegation model in which firms can choose between a flexible production technology which allows them to produce two different products and a dedicated production technology which limits production to only one product. We analyze whether the incentives to adopt the flexible technology are smaller or greater in a managerial delegation model than under strict profit maximization. We obtain that the asymmetric equilibrium in which only one firm adopts the flexible technology can be sustained under strategic delegation but not under strict profit maximization when products are substitutes. We extend the analysis to consider welfare implications.

Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.