Psychosocial variables associated with the co-occurance of risky sexual behavior with alcohol consumption among university students

This study examined the association of theoretically guided and empirically identified psychosocial variables on the co-occurrence of risky sexual behavior with alcohol consumption among university students. The study utilized event analysis to determine whether risky sex occurred during the same event in which alcohol was consumed. Relevant conceptualizations included alcohol disinhibition, self-efficacy, and social network theories. Predictor variables included negative condom attitudes, general risk taking, drinking motives, mistrust, social group membership, and gender. Factor analysis was employed to identify dimensions of drinking motives. Measured risky sex behaviors were (a) sex without a condom, (b) sex with people not known very well, (c) sex with injecting drug users (IDUs), (d) sex with people without knowing whether they had a STD, and (e) sex with using drugs. A purposive sample was used and included 222 male and female students recruited from a major urban university. Chi-square analysis was used to determine whether participants were more likely to engage in risky sex behavior in different alcohol use contexts. These contexts were only when drinking, only when not drinking, and when drinking or not. The chi-square findings did not support the hypothesis that university students who use alcohol with sex will engage in riskier sex. These results added to the literature by extending other similar findings to a university student sample. For each of the observed risky sex behaviors, discriminant analysis methodology was used to determine whether the predictor variables would differentiate the drinking contexts, or whether the behavior occurred. Results from discriminant analyses indicated that sex with people not known very well was the only behavior for which there were significant discriminant functions. Gender and enhancement drinking motives were important constructs in the classification model. Limitations of the study and implications for future research, social work practice and policy are discussed.

Zmienność genetyczna, specyficzność żywicielska i zdolność do przenoszenia wirusa mozaiki smugowatej pszenicy (WSMV) w obrębie kompleksu gatunków Aceria tosichella

Wydział Biologii: Instytut Biologii Środowiska

Diabetes mellitus among tuberculosis patients: a cross sectional study from Pakistan

Background: The co-occurrence of diabetes mellitus (DM) and tuberculosis (TB) is largely associated with high frequency of morbidity. Objective: To determine the prevalence of DM among TB patients and describe the socio-demographic and behavioral factors associated with TB-DM co-occurrence . Methods: We enrolled 500 TB patients from September, 2014 to August 2015 at four major public sector hospitals of Lahore, Pakistan. A questionnaire was used to collect information regarding associated socio-demographic and behavioral factors of the patients. We monitored the fasting blood sugar of each patient by using a semi automated clinical chemistry analyzer followed by an HbA1c level check of all hyperglycemic patients. Results: The prevalence of TB-DM co-occurrence was 14.8%. The prevalence of TB-DM was higher (62.2%) among males. The >57 year age group had the highest proportion of patients (35.1%), with co-existent TB-DM. Most were illiterate (73.0%) and unemployed (48%). Moreover, among the 74 patients positive for TB-DM had a history of smoking. Age and education level were significantly associated with DM-TB while gender, occupation and smoking were not associated. Conclusion: The study revealed a 14.8% prevalence of DM among TB patients. This was associated with several socio-demographic factors, including age, unemployment, literacy and polluted environment. Thus, poor and unhealthy lifestyles were the factors associated with DM among immunologically compromised individuals due to TB.

Topic extraction from microblog posts using conversation structures

Conventional topic models are ineffective for topic extraction from microblog messages since the lack of structure and context among the posts renders poor message-level word co-occurrence patterns. In this work, we organize microblog posts as conversation trees based on reposting and replying relations, which enrich context information to alleviate data sparseness. Our model generates words according to topic dependencies derived from the conversation structures. In specific, we differentiate messages as leader messages, which initiate key aspects of previously focused topics or shift the focus to different topics, and follower messages that do not introduce any new information but simply echo topics from the messages that they repost or reply. Our model captures the different extents that leader and follower messages may contain the key topical words, thus further enhances the quality of the induced topics. The results of thorough experiments demonstrate the effectiveness of our proposed model.

Co-Infestation and Spatial Distribution of Bactrocera carambolae and Anastrepha spp. (Diptera: Tephritidae) in Common Guava in the Eastern Amazon.

Field infestation and spatial distribution of introduced Bactrocera carambolae Drew and Hancock and native species of Anastrepha in common guavas [Psidium guajava (L.)] were investigated in the eastern Amazon. Fruit sampling was carried out in the municipalities of Calc¸oene and Oiapoque in the state of Amapa, Brazil. The frequency distribution of larvae in fruit was fitted to the negative binomial distribution. Anastrepha striata was more abundant in both sampled areas in comparison to Anastrepha fraterculus (Wiedemann) and B. carambolae. The frequency distribution analysis of adults revealed an aggregated pattern for B. carambolae as well as for A. fraterculus and Anastrepha striata Schiner, described by the negative binomial distribution. Although the populations of Anastrepha spp. may have suffered some impact due to the presence of B. carambolae, the results are still not robust enough to indicate effective reduction in the abundance of Anastrepha spp. caused by B. carambolae in a general sense. The high degree of aggregation observed for both species suggests interspecific co-occurrence with the simultaneous presence of both species in the analysed fruit. Moreover, a significant fraction of uninfested guavas also indicated absence of competitive displacement.

Substance Treatment Options in Psychosis (STOP) : a new intervention for dual diagnosis

Despite the high co-occurrence of psychosis and substance abuse, there is very little research on the development of effective treatments for this problem. This paper describes a new intervention that facilitates reaching functional goals through collaboration between therapists, participants and families. Substance Treatment Options in Psychosis (STOP) integrates pharmacological and psycho-logical treatments for psychotic symptoms, with cognitive-behavioural approaches to substance abuse. STOP is tailored to participants' problems and abilities, and recognises that control of consumption and even engagement may take several attempts. Training in relevant skills is augmented by bibliotherapy, social support and environmental change. A case description illustrates the issues and challenges in implementation.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distri- butions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

Treatment of comorbidity

Cormorbity means the co-occurrence of one or more diseases or disorders in an individual. The National Comorbity Project aims to highlight this type of comorbity and identify appropriate strategies and policies responses.

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.

Substance use disorders

Basic competencies in assessing and treating substance use disorders should be core to the training of any clinical psychologist, because of the high frequency of risky or problematic substance use in the community, and its high co-occurrence with other problems. Skills in establishing trust and a therapeutic alliance are particularly important in addiction, given the stigma and potential for legal sanctions that surround it. The knowledge and skills of all clinical practitioners should be sufficient to allow valid screening and diagnosis of substance use disorders, accurate estimation of consumption and a basic functional analysis. Practitioners should also be able to undertake brief interventions including motivational interviews, and appropriately apply generic interventions such as problem solving or goal setting to addiction. Furthermore, clinical psychologists should have an understanding of the nature, evidence base and indications for biochemical assays, pharmacotherapies and other medical treatments, and ways these can be integrated with psychological practice. Specialists in addiction should have more sophisticated competencies in each of these areas. They need to have a detailed understating of current addiction theories and basic and applied research, be able to undertake and report on a detailed psychological assessment, and display expert competence in addiction treatment. These skills should include an ability to assess and manage complex or co-occurring problems, to adapt interventions to the needs of different groups, and to assist people who have not responded to basic treatments. They should also be able to provide consultation to others, undertake evaluations of their practice, and monitor and evaluate emerging research data in the field.

Post-operative CT assessment of interbody fusion two years after thoracoscopic scoliosis surgery

The relationship between radiologic union and clinical outcome in thoracoscopic scoliosis surgery is not clear, as apparent non-union of a spinal fusion does not always correspond to a poor clinical result. The aim of this study was to evaluate CT fusion rates 24 months after thoracoscopic anterior scoliosis surgery, and to explore the relationship between fusion scores and; (i) rod diameter, (ii) graft type, (iii) fusion level, (iv) occurrence of post-operative implant failure, and (v) lateral position of the fusion mass in the intervertebral disc space. We propose that moderate fusion scores on the Sucato scale secure successful clinical outcomes in thoracoscopic scoliosis surgery.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any ‘bag of words’ approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document’s initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur’s search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

Anticipating user experience with a desired product : the AUX Framework

Positive user experience (UX) has become a key factor in designing interactive products. It acts as a differentiator which can determine a product’s success on the mature market. However, current UX frameworks and methods do not fully support the early stages of product design and development. During these phases, assessment of UX is challenging as no actual user-product interaction can be tested. This qualitative study investigated anticipated user experience (AUX) to address this problem. Using the co-discovery method, participants were asked to imagine a desired product, anticipate experiences with it, and discuss their views with another participant. Fourteen sub-categories emerged from the data, and relationships among them were defined through co-occurrence analysis. These data formed the basis of the AUX framework which consists of two networks which elucidate 1) how users imagine a desired product and 2) how they anticipate positive experiences with that product. Through this AUX framework, important factors in the process of imagining future products and experiences were learnt, including the way in which these factors interrelate. Focusing on and exploring each component of the two networks in the framework will allow designers to obtain a deeper understanding of the required pragmatic and hedonic qualities of product, intended uses of product, user characteristics, potential contexts of experience, and anticipated emotions embedded within the experience. This understanding, in turn, will help designers to better foresee users’ underlying needs and to focus on the most important aspects of their positive experience. Therefore, the use of the AUX framework in the early stages of product development will contribute to the design for pleasurable UX.

Identification and evaluation of service bundles for governmental one-stop portals

Recent advances in the area of ‘Transformational Government’ position the citizen at the centre of focus. This paradigm shift from a department-centric to a citizen-centric focus requires governments to re-think their approach to service delivery, thereby decreasing costs and increasing citizen satisfaction. The introduction of franchises as a virtual business layer between the departments and their citizens is intended to provide a solution. Franchises are structured to address the needs of citizens independent of internal departmental structures. For delivering services online, governments pursue the development of a One-Stop Portal, which structures information and services through those franchises. Thus, each franchise can be mapped to a specific service bundle, which groups together services that are deemed to be of relevance to a specific citizen need. This study focuses on the development and evaluation of these service bundles. In particular, two research questions guide the line of investigation of this study: Research Question 1): What methods can be used by governments to identify service bundles as part of governmental One-Stop Portals? Research Question 2): How can the quality of service bundles in governmental One-Stop Portals be evaluated? The first research question asks about the identification of suitable service bundle identification methods. A literature review was conducted, to, initially, conceptualise the service bundling task, in general. As a consequence, a 4-layer model of service bundling and a morphological box were created, detailing characteristics that are of relevance when identifying service bundles. Furthermore, a literature review of Decision-Support Systems was conducted to identify approaches of relevance in different bundling scenarios. These initial findings were complemented by targeted studies of multiple leading governments in the e-government domain, as well as with a local expert in the field. Here, the aim was to identify the current status of online service delivery and service bundling in practice. These findings led to the conceptualising of two service bundle identification methods, applicable in the context of Queensland Government: On the one hand, a provider-driven approach, based on service description languages, attributes, and relationships between services was conceptualised. As well, a citizen-driven approach, based on analysing the outcomes from content identification and grouping workshops with citizens, was also conceptualised. Both methods were then applied and evaluated in practice. The conceptualisation of the provider-driven method for service bundling required the initial specification of relevant attributes that could be used to identify similarities between services called relationships; these relationships then formed the basis for the identification of service bundles. This study conceptualised and defined seven relationships, namely ‘Co-location’, ‘Resource’, ‘Co-occurrence’, ‘Event’, ‘Consumer’, ‘Provider’, and ‘Type’. The relationships, and the bundling method itself, were applied and refined as part of six Action Research cycles in collaboration with the Queensland Government. The findings show that attributes and relationships can be used effectively as a means for bundle identification, if distinct decision rules are in place to prescribe how services are to be identified. For the conceptualisation of the citizen-driven method, insights from the case studies led to the decision to involve citizens, through card sorting activities. Based on an initial list of services, relevant for a certain franchise, participating citizens grouped services according to their liking. The card sorting activity, as well as the required analysis and aggregation of the individual card sorting results, was analysed in depth as part of this study. A framework was developed that can be used as a decision-support tool to assist with the decision of what card sorting analysis method should be utilised in a given scenario. The characteristic features associated with card sorting in a government context led to the decision to utilise statistical analysis approaches, such as cluster analysis and factor analysis, to aggregate card sorting results. The second research question asks how the quality of service bundles can be assessed. An extensive literature review was conducted focussing on bundle, portal, and e-service quality. It was found that different studies use different constructs, terminology, and units of analysis, which makes comparing these models a difficult task. As a direct result, a framework was conceptualised, that can be used to position past and future studies in this research domain. Complementing the literature review, interviews conducted as part of the case studies with leaders in e-government, indicated that, typically, satisfaction is evaluated for the overall portal once the portal is online, but quality tests are not conducted during the development phase. Consequently, a research model which appropriately defines perceived service bundle quality would need to be developed from scratch. Based on existing theory, such as Theory of Reasoned Action, Expectation Confirmation Theory, and Theory of Affordances, perceived service bundle quality was defined as an inferential belief. Perceived service bundle quality was positioned within the nomological net of services. Based on the literature analysis on quality, and on the subsequent work of a focus group, the hypothesised antecedents (descriptive beliefs) of the construct and the associated question items were defined and the research model conceptualised. The model was then tested, refined, and finally validated during six Action Research cycles. Results show no significant difference in higher quality or higher satisfaction among users for either the provider-driven method or for the citizen-driven method. The decision on which method to choose, it was found, should be based on contextual factors, such as objectives, resources, and the need for visibility. The constructs of the bundle quality model were examined. While the quality of bundles identified through the citizen-centric approach could be explained through the constructs ‘Navigation’, ‘Ease of Understanding’, and ‘Organisation’, bundles identified through the provider-driven approach could be explained solely through the constructs ‘Navigation’ and ‘Ease of Understanding’. An active labelling style for bundles, as part of the provider-driven Information Architecture, had a larger impact on ‘Quality’ than the topical labelling style used in the citizen-centric Information Architecture. However, ‘Organisation’, reflecting the internal, logical structure of the Information Architecture, was a significant factor impacting on ‘Quality’ only in the citizen-driven Information Architecture. Hence, it was concluded that active labelling can compensate for a lack of logical structure. Further studies are needed to further test this conjecture. Such studies may involve building alternative models and conducting additional empirical research (e.g. use of an active labelling style for the citizen-driven Information Architecture). This thesis contributes to the body of knowledge in several ways. Firstly, it presents an empirically validated model of the factors explaining and predicting a citizen’s perception of service bundle quality. Secondly, it provides two alternative methods that can be used by governments to identify service bundles in structuring the content of a One-Stop Portal. Thirdly, this thesis provides a detailed narrative to suggest how the recent paradigm shift in the public domain, towards a citizen-centric focus, can be pursued by governments; the research methodology followed by this study can serve as an exemplar for governments seeking to achieve a citizen-centric approach to service delivery.

The 4C spectrum of fundamental behavioral relations for concurrent systems

The design of concurrent software systems, in particular process-aware information systems, involves behavioral modeling at various stages. Recently, approaches to behavioral analysis of such systems have been based on declarative abstractions defined as sets of behavioral relations. However, these relations are typically defined in an ad-hoc manner. In this paper, we address the lack of a systematic exploration of the fundamental relations that can be used to capture the behavior of concurrent systems, i.e., co-occurrence, conflict, causality, and concurrency. Besides the definition of the spectrum of behavioral relations, which we refer to as the 4C spectrum, we also show that our relations give rise to implication lattices. We further provide operationalizations of the proposed relations, starting by proposing techniques for computing relations in unlabeled systems, which are then lifted to become applicable in the context of labeled systems, i.e., systems in which state transitions have semantic annotations. Finally, we report on experimental results on efficiency of the proposed computations.