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.

Teacher praise and feedback and students’ perceptions of the classroom environment

The relationships between teacher praise and feedback, and students’ perceptions of the classroom environment were investigated in six rural elementary schools (n 5 747). The Teacher Feedback Scale and My Classroom Scale were developed as part of this study and used to collect the data. Structural equation modelling was used to test a hypothesised model. The results indicated that negative teacher feedback and effort feedback were both related to students’ relationships with their teachers, while ability feedback was associated with perceptions of the classroom environment. Praise was not related to classroom environment or teacher–student relationships. Significant age and gender differences were found. Additionally, differences were found between students who were satisfied with their classroom and those who were dissatisfied. Satisfied students received more general praise, general ability feedback, effort feedback and less negative teacher feedback when compared to dissatisfied students. Research studies have emphasised the influence of signicicant adults (teachers and parents) on students’ personal development (Porlier et al., 1999) and the importance of significant others’ verbal statements when directed at children (Burnett, 1996a). The relationships between negative and positive statements made by teachers, parents, peers and siblings and children’s self-talk have been investigated (Burnett, 1996a) and positive statements (praise) have been found to be more beneficial than verbal criticism (Burnett, 1999). The quality of life in the classroom in recent times has been considered of great importance to students (Thorp et al., 1994) and this is recognised by Baker (1999) who reported a relationship between students’ satisfaction with the learning environment, and differential teacher feedback and praise. This study investigated the relationships between teacher praise and feedback, and how students perceived their classroom and their relationship with their teacher.

Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation

To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we specially exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments show that image annotation results are improved in the LabelMe dataset.

A Penalty-based Genetic Algorithm for QoS-AwareWeb Service Composition with Inter-Service Dependencies and Conflicts

In Web service based systems, new value-added Web services can be constructed by integrating existing Web services. A Web service may have many implementations, which are functionally identical, but have different Quality of Service (QoS) attributes, such as response time, price, reputation, reliability, availability and so on. Thus, a significant research problem in Web service composition is how to select an implementation for each of the component Web services so that the overall QoS of the composite Web service is optimal. This is so called QoS-aware Web service composition problem. In some composite Web services there are some dependencies and conflicts between the Web service implementations. However, existing approaches cannot handle the constraints. This paper tackles the QoS-aware Web service composition problem with inter service dependencies and conflicts using a penalty-based genetic algorithm (GA). Experimental results demonstrate the effectiveness and the scalability of the penalty-based GA.

Mainstreamed students with learning difficulties : failing and underachieving in the secondary school

Despite the rhetoric that students with learning difficulties are adequately supported within schools, the evidence suggests that they continue to experience school failure with devastating consequences. Students with learning difficulties are disproportionately represented as juvenile delinquents, as the unemployed and in mental health statistics. However, the defining of this group remains confused and imprecise and has not been a national priority. This has repercussions for both secondary schools and for the students themselves. This paper highlights research related to teaching practices, policies and school structure and their effects on the academic outcomes and emotional well being of students with learning difficulties. Finally, it makes a number of recommendations to change the status quo for these students.

Adjuvant use of nutritional and herbal medicines with antidepressants, mood stabilizers and benzodiazepines

Adjuvant use of nutritional and herbal medicines has potential to increase the efficacy of synthetic pharmaceuticals, and perhaps also decrease their side-effects by allowing lower doses to be prescribed. We evaluated current evidence for adjuvant use of nutritional and herbal medicines with antidepressants, mood stabilizers and benzodiazepines; and explored novel future areas of research. The paper also critiques current evidence for co-administration of St. John’s wort with synthetic antidepressants. We performed a systematic search of MEDLINE, CINAHL, PsycINFO, The Cochrane database, China National Knowledge Infrastructure and the Chinese Science Citation Database. Search results were supplemented by a review of reference lists and a forward search using the Web of Science. Where possible we calculated effect sizes. Encouraging evidence exists for the use of omega-3 fatty acids, SAMe, folic acid and l-tryptophan adjuvantly with antidepressants to enhance response and improve efficacy. Various nutrients also have emerging evidence as effective adjuncts with antipsychotics and mood stabilizers. While some evidence supports nutritional adjuvancy with various psychopharmacotherapies, adjuvant use of herbal therapies has not been sufficiently studied to warrant standard clinical application. This remains a promising area of research via robust, safety-conscious studies.

Good clinical endpoints with denosumab in osteoporosis and cancer

Background: Bone loss associated with low oestrogen levels in postmenopausal women, and with androgen deprivation therapy in men with hormone-sensitive prostate cancer, result in an increased incidence of fractures. Denosumab has been shown to increase bone mineral density in these two conditions. Objectives/methods: The objective of this evaluation is to review the clinical trials that have studied clinical endpoints in these conditions. Results: FREEDOM (Fracture Reduction Evaluation of Denosumab in Osteoporosis Every 6 Months) was an International Phase III clinical trial that measured the clinical endpoints with denosumab in postmenopausal women with osteoporosis. At 36 months, new vertebral fractures had occurred in 7.2% of subjects in the placebo group and this was lowered to 2.3% of subjects treated with denosumab. HALT (Denosumab Hormone Ablation Bone Loss Trial) studied the clinical endpoints in men with non-metastatic prostate cancer receiving androgen-deprivation therapy. The incidence of vertebral fractures was significantly lower in the denosumab group (1.5%) than in the placebo group (3.9%). The incidence of adverse effects with denosumab in both clinical trials was low. Conclusions: Denosumab reduces the incidence of fractures in postmenopausal women with osteoporosis and in men with non-metastatic prostate cancer receiving androgen-deprivation therapy. Denosumab is well tolerated.

Thai word segmentation with hidden Markov Model and decision tree

The Thai written language is one of the languages that does not have word boundaries. In order to discover the meaning of the document, all texts must be separated into syllables, words, sentences, and paragraphs. This paper develops a novel method to segment the Thai text by combining a non-dictionary based technique with a dictionary-based technique. This method first applies the Thai language grammar rules to the text for identifying syllables. The hidden Markov model is then used for merging possible syllables into words. The identified words are verified with a lexical dictionary and a decision tree is employed to discover the words unidentified by the lexical dictionary. Documents used in the litigation process of Thai court proceedings have been used in experiments. The results which are segmented words, obtained by the proposed method outperform the results obtained by other existing methods.

Novel ruthenium bipyridyl dyes with s-donor ligands and their application in dye-sensitized solar cells

Two series of novel ruthenium bipyridyl dyes incorporating sulfur-donor bidentate ligands with general formula \[Ru(R-bpy)2C2N2S2] and \[Ru(R-bpy)2(S2COEt)]\[NO3] (where R =H, CO2Et, CO2H; C2N2S2 = cyanodithioimidocarbonate and S2COEt = ethyl xanthogenate) have been synthesized and characterized spectroscopically, electrochemically and computationally. The acid derivatives in both series (C2N2S2 3 and S2COEt 6) were used as a photosensitizer in a dye-sensitized solar cell (DSSC) and the incident photo-to-current conversion efficiency (IPCE), overall efficiency (_) and kinetics of the dye/TiO2 system were investigated. It was found that 6 gave a higher efficiency cell than 3 despite the latter dye’s more favorable electronic properties, such as greater absorption range, higher molar extinction coefficient and large degree of delocalization of the HOMO. The transient absorption spectroscopy studies revealed that the recombination kinetics of 3 were unexpectedly fast, which was attributed to the terminal CN on the ligand binding to the TiO2, as evidenced by an absorption study of R =H and CO2Et dyes sensitized on TiO2, and hence leading to a lower efficiency DSSC.

Fingerprint analysis using wavelet transform with application to compression and feature extraction

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.