Favorability conditions in the adoption of agile method practices for software development in a public banking

The main purpose of this paper is to propose and test a model to assess the degree of conditions favorability in the adoption of agile methods to develop software where traditional methods predominate. In order to achieve this aim, a survey was applied on software developers of a Brazilian public retail bank. Two different statistical techniques were used in order to assess the quantitative data from the closed questions in the survey. The first, exploratory factorial analysis validated the structure of perspectives related to the agile model of the proposed assessment. The second, frequency distribution analysis to categorize the answers. Qualitative data from the survey opened question were analyzed with the technique of qualitative thematic content analysis. As a result, the paper proposes a model to assess the degree of favorability conditions in the adoption of Agile practices within the context of the proposed study.

An Islanding Detection Method based on Impedance Estimation and Grid Identification for Grid-Tie Distributed Power Generation Systems

Power system policies are broadly on track to escalate the use of renewable energy resources in electric power generation. Integration of dispersed generation to the utility network not only intensifies the benefits of renewable generation but also introduces further advantages such as power quality enhancement and freedom of power generation for the consumers. However, issues arise from the integration of distributed generators to the existing utility grid are as significant as its benefits. The issues are aggravated as the number of grid-connected distributed generators increases. Therefore, power quality demands become stricter to ensure a safe and proper advancement towards the emerging smart grid. In this regard, system protection is the area that is highly affected as the grid-connected distributed generation share in electricity generation increases. Islanding detection, amongst all protection issues, is the most important concern for a power system with high penetration of distributed sources. Islanding occurs when a portion of the distribution network which includes one or more distributed generation units and local loads is disconnected from the remaining portion of the grid. Upon formation of a power island, it remains energized due to the presence of one or more distributed sources. This thesis introduces a new islanding detection technique based on an enhanced multi-layer scheme that shows superior performance over the existing techniques. It provides improved solutions for safety and protection of power systems and distributed sources that are capable of operating in grid-connected mode. The proposed active method offers negligible non-detection zone. It is applicable to micro-grids with a number of distributed generation sources without sacrificing the dynamic response of the system. In addition, the information obtained from the proposed scheme allows for smooth transition to stand-alone operation if required. The proposed technique paves the path towards a comprehensive protection solution for future power networks. The proposed method is converter-resident and all power conversion systems that are operating based on power electronics converters can benefit from this method. The theoretical analysis is presented, and extensive simulation results confirm the validity of the analytical work.

Suicide among young people and adults in Ireland: method characteristics, toxicological analysis and substance abuse histories compared

Objective: Information on factors associated with suicide among young individuals in Ireland is limited. The aim of this study was to identify socio-demographic characteristics and circumstances of death associated with age among individuals who died by suicide. Methods: The study examined 121 consecutive suicides (2007–2012) occurring in the southern eastern part of Ireland (Cork city and county). Data were obtained from coroners, family informants, and health care professionals. A comparison was made between 15-24-year-old and 25-34-year-old individuals. Socio-demographic characteristics of the deceased, methods of suicide, history of alcohol and drug abuse, and findings from toxicological analysis of blood and urine samples taken at post mortem were included. Pearson’s χ2 tests and binary logistic regression analysis were performed. Results: Alcohol and/or drugs were detected through toxicological analysis for the majority of the total sample (79.5%), which did not differentiate between 15-24-year-old and 25-34-year-old individuals (74.1% and 86.2% respectively). Compared to 25-34-year-old individuals, 15-24-year-old individuals were more likely to engage in suicide by hanging (88.5%). Younger individuals were less likely to die by intentional drug overdose and carbon monoxide poisoning compared to older individuals. Younger individuals who died between Saturday and Monday were more likely to have had alcohol before dying. Substance abuse histories were similar in the two age groups. Conclusion: Based on this research it is recommended that strategies to reduce substance abuse be applied among 25-34-year-old individuals at risk of suicide. The wide use of hanging in young people should be taken into consideration for future means restriction strategies.

Person-centred information to parents in paediatric oncology (the PIFBO study) : A study protocol of an ongoing RCT

Background: Parents of children with cancer experience a demanding situation and often suffer from psychological problems such as stress. Trying to coping with the complex body of information about their child's disease is one factor that contributes to this stress. The aim of this study is to evaluate an intervention for person-centred information to parents of children with cancer that consists of four sessions with children's nurses trained in the intervention method. Methods/Design: This is a multi-centre RCT with two parallel arms and a 1:1 allocation ratio. The primary outcome is illness-related parental stress. Secondary outcomes are post-traumatic stress symptoms, anxiety, depression, satisfaction with information, expected and received knowledge, and experiences with health care providers. A process evaluation is performed to describe experiences and contextual factors. Data are collected using web questionnaires or paper forms according to the parents' preference, audio recording of the intervention sessions, and qualitative interviews with parents and the intervention nurses. Discussion: Few studies have evaluated information interventions for parents of children with cancer using large multi-centre RCTs. This intervention is designed to be performed by regular staff children's nurses, which will facilitate implementation if the intervention proves to be effective. Trial registration: Clinical trials NCT02332226 (December 11, 2014).

The Power of the Synthetic Control Method

The synthetic control method (SCM) is a new, popular method developed for the purpose of estimating the effect of an intervention when only one single unit has been exposed. Other similar, unexposed units are combined into a synthetic control unit intended to mimic the evolution in the exposed unit, had it not been subject to exposure. As the inference relies on only a single observational unit, the statistical inferential issue is a challenge. In this paper, we examine the statistical properties of the estimator, study a number of features potentially yielding uncertainty in the estimator, discuss the rationale for statistical inference in relation to SCM, and provide a Web-app for researchers to aid in their decision of whether SCM is powerful for a specific case study. We conclude that SCM is powerful with a limited number of controls in the donor pool and a fairly short pre-intervention time period. This holds as long as the parameter of interest is a parametric specification of the intervention effect, and the duration of post-intervention period is reasonably long, and the fit of the synthetic control unit to the exposed unit in the pre-intervention period is good.

The experience of anxiety in colonoscopy outpatients: a mixed-method study.

Colonoscopy is commonly used to investigate gastrointestinal symptoms such as pain or changes in bowel habits and may either induce patient anxiety or assist in patient reassurance. Currently, 2 studies investigating negative colonoscopy, reassurance, and anxiety came to conflicting conclusions on this issue. Furthermore, it is possible that differences in coping styles may influence patient anxiety. A mixed-methods study was conducted with 26 precolonoscopy and 24 postcolonoscopy patients to address the conflicting, limited literature regarding colonoscopy, coping, and anxiety. Participants completed postal surveys and interviews were conducted with 16 participants. There was no significant difference between pre- and postcolonoscopy groups on any anxiety measures; however, this was possibly because of individual differences. Significant positive correlations were found between maladaptive coping and state anxiety indicating that healthcare professionals should consider screening for maladaptive coping in patients needing invasive procedures. Neither problem- nor emotion-focused coping showed any significant relationship with state anxiety. Interviews revealed that clinicians and endoscopy nurses should be aware that some patients are not absorbing correct information about colonoscopy, specifically that they may be conscious or experience pain during the procedure. Because of this, clinicians should ensure that patients understand standard practice at their hospital. In addition, interview data suggested that more attention should be given to pain management as it currently may not be adequate during conscious sedation.

Asset identification in information security risk assessment: A business practice approach

Organizations apply information security risk assessment (ISRA) methodologies to systematically and comprehensively identify information assets and related security risks. We review the ISRA literature and identify three key deficiencies in current methodologies that stem from their traditional accountancy-based perspective and a limited view of organizational "assets". In response, we propose a novel rich description method (RDM) that adopts a less formal and more holistic view of information and knowledge assets that exist in modern work environments. We report on an in-depth case study to explore the potential for improved asset identification enabled by the RDM compared to traditional ISRAs. The comparison shows how the RDM addresses the three key deficiencies of current ISRAs by providing: 1) a finer level of granularity for identifying assets, 2) a broader coverage of assets that reflects the informal aspects of business practices, and 3) the identification of critical knowledge assets.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

Constraint-Informed Information Systems in Space Management Utilization

Declarative techniques such as Constraint Programming can be very effective in modeling and assisting management decisions. We present a method for managing university classrooms which extends the previous design of a Constraint-Informed Information System to generate the timetables while dealing with spatial resource optimization issues. We seek to maximize space utilization along two dimensions: classroom use and occupancy rates. While we want to maximize the room use rate, we still need to satisfy the soft constraints which model students’ and lecturers’ preferences. We present a constraint logic programming-based local search method which relies on an evaluation function that combines room utilization and timetable soft preferences. Based on this, we developed a tool which we applied to the improvement of classroom allocation in a University. Comparing the results to the current timetables obtained without optimizing space utilization, the initial versions of our tool manages to reach a 30% improvement in space utilization, while preserving the quality of the timetable, both for students and lecturers.

Improving understandability in consumer health information search: Uevora @ 2016 fire chis

This paper presents our work at 2016 FIRE CHIS. Given a CHIS query and a document associated with that query, the task is to classify the sentences in the document as relevant to the query or not; and further classify the relevant sentences to be supporting, neutral or opposing to the claim made in the query. In this paper, we present two different approaches to do the classification. With the first approach, we implement two models to satisfy the task. We first implement an information retrieval model to retrieve the sentences that are relevant to the query; and then we use supervised learning method to train a classification model to classify the relevant sentences into support, oppose or neutral. With the second approach, we only use machine learning techniques to learn a model and classify the sentences into four classes (relevant & support, relevant & neutral, relevant & oppose, irrelevant & neutral). Our submission for CHIS uses the first approach.

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.