794 resultados para Anger assessment


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We compared student performance on large-scale take-home assignments and small-scale invigilated tests that require competency with exactly the same programming concepts. The purpose of the tests, which were carried out soon after the take home assignments were submitted, was to validate the students' assignments as individual work. We found widespread discrepancies between the marks achieved by students between the two types of tasks. Many students were able to achieve a much higher grade on the take-home assignments than the invigilated tests. We conclude that these paired assessments are an effective way to quickly identify students who are still struggling with programming concepts that we might otherwise assume they understand, given their ability to complete similar, yet more complicated, tasks in their own time. We classify these students as not yet being at the neo-Piagetian stage of concrete operational reasoning.

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Purpose In the oncology population where malnutrition prevalence is high, more descriptive screening tools can provide further information to assist triaging and capture acute change. The Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF) is a component of a nutritional assessment tool which could be used for descriptive nutrition screening. The purpose of this study was to conduct a secondary analysis of nutrition screening and assessment data to identify the most relevant information contributing to the PG-SGA SF to identify malnutrition risk with high sensitivity and specificity. Methods This was an observational, cross-sectional study of 300 consecutive adult patients receiving ambulatory anti-cancer treatment at an Australian tertiary hospital. Anthropometric and patient descriptive data were collected. The scored PG-SGA generated a score for nutritional risk (PG-SGA SF) and a global rating for nutrition status. Receiver operating characteristic curves (ROC) were generated to determine optimal cut-off scores for combinations of the PG-SGA SF boxes with the greatest sensitivity and specificity for predicting malnutrition according to scored PG-SGA global rating. Results The additive scores of boxes 1–3 had the highest sensitivity (90.2 %) while maintaining satisfactory specificity (67.5 %) and demonstrating high diagnostic value (AUC = 0.85, 95 % CI = 0.81–0.89). The inclusion of box 4 (PG-SGA SF) did not add further value as a screening tool (AUC = 0.85, 95 % CI = 0.80–0.89; sensitivity 80.4 %; specificity 72.3 %). Conclusions The validity of the PG-SGA SF in chemotherapy outpatients was confirmed. The present study however demonstrated that the functional capacity question (box 4) does not improve the overall discriminatory value of the PG-SGA SF.

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Toxic chemical pollutants such as heavy metals (HMs) are commonly present in urban stormwater. These pollutants can pose a significant risk to human health and hence a significant barrier for urban stormwater reuse. The primary aim of this study was to develop an approach for quantitatively assessing the risk to human health due to the presence of HMs in stormwater. This approach will lead to informed decision making in relation to risk management of urban stormwater reuse, enabling efficient implementation of appropriate treatment strategies. In this study, risks to human health from heavy metals were assessed as hazard index (HI) and quantified as a function of traffic and land use related parameters. Traffic and land use are the primary factors influencing heavy metal loads in the urban environment. The risks posed by heavy metals associated with total solids and fine solids (<150µm) were considered to represent the maximum and minimum risk levels, respectively. The study outcomes confirmed that Cr, Mn and Pb pose the highest risks, although these elements are generally present in low concentrations. The study also found that even though the presence of a single heavy metal does not pose a significant risk, the presence of multiple heavy metals could be detrimental to human health. These findings suggest that stormwater guidelines should consider the combined risk from multiple heavy metals rather than the threshold concentration of an individual species. Furthermore, it was found that risk to human health from heavy metals in stormwater is significantly influenced by traffic volume and the risk associated with stormwater from industrial areas is generally higher than that from commercial and residential areas.

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The Social Water Assessment Protocol (SWAP) is a tool consisting of a series of questions on fourteen themes designed to capture the social context of water around a mine site. A pilot study of the SWAP, conducted in Prestea-Huni Valley, Ghana, showed that some communities were concerned about whether the groundwater was potable. The mining company’s concern was that there was a cycle of dependency amongst communities that received treated water from the mining company. The pilot identified potential data sources and stakeholder groups for each theme, gaps in themes and suggested refinements to questions to improve the SWAP.

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Context In-training assessment (ITA) has established its place alongside formative and summative assessment at both the undergraduate and postgraduate level. In this paper the authors aimed to identify those characteristics of ITA that could enhance clinical teaching. Methods A literature review and discussions by an expert working group at the Ninth Cambridge Conference identified the aspects of ITA that could enhance clinical teaching. Results The features of ITA identified included defining the specific benefits to the learner, teacher and institution, and highlighting the patient as the context for ITA and clinical teaching. The ‘mapping’ of a learner’s progress towards the clinical teaching objectives by using multiple assessments over time, by multiple observers in both a systematic and opportunistic way correlates with the incremental nature of reaching clinical competence. Conclusions The importance of ITA based on both direct and indirect evidence of what the learner actually does in the real clinical setting is emphasized. Particular attention is given to addressing concerns in the more controversial areas of assessor training, ratings and documentation for ITA. Areas for future research are also identified.

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Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

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While the majority of violent threats – defined as an expression of intent to do harm or act out violently against someone or something – do not progress to actual violence, a small proportion of threateners do go on to enact violence. Most researchers argue that violence risk assessments are inadequate for assessing threats of violence, which raises the question: how should a threat assessment (TA) be conducted? To begin to understand available frameworks for assessing threats, a systematic review of TA research literature was conducted. Most TA literature pertains to a specific domain (schools, public figure threats, workplaces) and target audience (clinicians, school personnel, law enforcement). TA guidelines are typically based on literature reviews with some based on empirical measures and others having no strong evidential basis. The most common concepts in TA are exploration of the threatener's mental health, the motivation for the threat and the presence of any plans. Rather than advocating for the development of a protocol for conducting TA, this article outlines the common areas of inquiry in assessing threats and highlights the limitations of current TA guidelines.

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The cultural appropriateness of human service processes is a major factor in determining the effectiveness of their delivery. Sensitivity to issues of culture is particularly critical in dealing with family disputes, which are generally highly emotive and require difficult decisions to be made regarding children, material assets and ongoing relationships. In this article we draw on findings from an evaluation of the Family Relationship Centre at Broadmeadows (FRCB) to offer some insights into and suggestions about managing cultural matters in the current practice of family dispute resolution (FDR) in Australia. The brief for the original research was to evaluate the cultural appropriateness of FDR services offered to culturally and linguistically diverse (CALD) communities living within the FRCB’s catchment area, specifically members of the Lebanese, Turkish and Iraqi communities. The conclusions of the evaluations were substantially positive. The work of the Centre was found to illustrate many aspects of best practice but also raised questions worthy of future exploration. The current article reports on issues of access, retention and outcomes obtained by CALD clients at various stages of the FRCB service.

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The cultural appropriateness of human service processes is a major factor in determining the effectiveness of their delivery. Sensitivity to issues of culture is particularly critical in dealing with family disputes, which are generally highly emotive and require difficult decisions to be made regarding children, material assets and ongoing relationships. In this article we draw on findings from an evaluation of the Family Relationship Centre at Broadmeadows (FRCB) to offer some insights into and suggestions about managing cultural matters in the current practice of family dispute resolution (FDR) in Australia. The brief for the original research was to evaluate the cultural appropriateness of FDR services offered to culturally and linguistically diverse (CALD) communities living within the FRCB’s catchment area, specifically members of the Lebanese, Turkish and Iraqi communities. The conclusions of the evaluations were substantially positive. The work of the Centre was found to illustrate many aspects of best practice but also raised questions worthy of future exploration. The current article reports on overall cultural appropriateness, particularly identifying barriers which may inhibit access and how acculturation may play a role in reducing perception of barriers. An earlier article reported on access, retention and outcomes for these CALD groups (Akin Ojelabi et al., 2011).

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This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

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The most difficult operation in flood inundation mapping using optical flood images is to map the ‘wet’ areas where trees and houses are partly covered by water. This can be referred to as a typical problem of the presence of mixed pixels in the images. A number of automatic information extracting image classification algorithms have been developed over the years for flood mapping using optical remote sensing images, with most labelling a pixel as a particular class. However, they often fail to generate reliable flood inundation mapping because of the presence of mixed pixels in the images. To solve this problem, spectral unmixing methods have been developed. In this thesis, methods for selecting endmembers and the method to model the primary classes for unmixing, the two most important issues in spectral unmixing, are investigated. We conduct comparative studies of three typical spectral unmixing algorithms, Partial Constrained Linear Spectral unmixing, Multiple Endmember Selection Mixture Analysis and spectral unmixing using the Extended Support Vector Machine method. They are analysed and assessed by error analysis in flood mapping using MODIS, Landsat and World View-2 images. The Conventional Root Mean Square Error Assessment is applied to obtain errors for estimated fractions of each primary class. Moreover, a newly developed Fuzzy Error Matrix is used to obtain a clear picture of error distributions at the pixel level. This thesis shows that the Extended Support Vector Machine method is able to provide a more reliable estimation of fractional abundances and allows the use of a complete set of training samples to model a defined pure class. Furthermore, it can be applied to analysis of both pure and mixed pixels to provide integrated hard-soft classification results. Our research also identifies and explores a serious drawback in relation to endmember selections in current spectral unmixing methods which apply fixed sets of endmember classes or pure classes for mixture analysis of every pixel in an entire image. However, as it is not accurate to assume that every pixel in an image must contain all endmember classes, these methods usually cause an over-estimation of the fractional abundances in a particular pixel. In this thesis, a subset of adaptive endmembers in every pixel is derived using the proposed methods to form an endmember index matrix. The experimental results show that using the pixel-dependent endmembers in unmixing significantly improves performance.

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There is on-going international interest in the relationships between assessment instruments, students’ understanding of science concepts and context-based curriculum approaches. This study extends earlier research showing that students can develop connections between contexts and concepts – called fluid transitions – when studying context-based courses. We provide an in-depth investigation of one student’s experiences with multiple contextual assessment instruments that were associated with a context-based course. We analyzed the student’s responses to context-based assessment instruments to determine the extent to which contextual tests, reports of field investigations, and extended experimental investigations afforded her opportunities to make connections between contexts and concepts. A system of categorizing student responses was developed that can inform other educators when analyzing student responses to contextual assessment. We also refine the theoretical construct of fluid transitions that informed the study initially. Implications for curriculum and assessment design are provided in light of the findings.