Composition, potential emissions and agricultural value of pig slurry from Spanish commercial farms

Pig slurry is a valuable fertilizer for crop production but at the same time its management may pose environmental risks. Slurry samples were collected from 77 commercial farms of four animal categories (gestating and lactating sows, nursery piglets and growing pigs) and analyzed for macronutrients, micronutrients, heavy metals and volatile fatty acids. Emissions of ammonia (NH3) and biochemical methane potential (BMP) were quantified. Slurry electrical conductivity, pH, dry matter content and ash content were also determined. Data analysis included an analysis of correlations among variables, the development of prediction models for gaseous emissions and the analysis of nutritional content of slurries for crop production. Descriptive information is provided in this work and shows a wide range of variability in all studied variables. Animal category affected some physicochemical parameters, probably as a consequence of different slurry management and use of cleaning water. Slurries from gestating sows and growing pigs tended to be more concentrated in nutrients, whereas the slurry from lactating sows and nursery piglets tended to be more diluted. Relevant relationships were found among slurry characteristics expressed in fresh basis and gas emissions. Predictive models using on-farm measurable parameters were obtained for NH3 (R2 = 0.51) and CH4

Mutation detection by highly sensitive methods indicates that p53 gene mutations in breast cancer can have important prognostic value.

Human cancer cells with a mutated p53 tumor-suppressor gene have a selective growth advantage and may exhibit resistance to ionizing radiation and certain chemotherapeutic agents. To examine the prognostic value of mutations in the p53 gene, a cohort of 90 Midwestern Caucasian breast cancer patients were analyzed with methodology that detects virtually 100% of all mutations. The presence of a p53 gene mutation was by far the single most predictive indicator for recurrence and death (relative risks of 4.7 and 23.2, respectively). Direct detection of p53 mutations had substantially greater prognostic value than immunohistochemical detection of p53 overexpression. Analysis of p53 gene mutations may permit identification of a subset of breast cancer patients who, despite lack of conventional indicators of poor prognosis, are at high risk of early recurrence and death.

Dynamic D-Vine copula model with applications to Value-at-Risk (VaR)

Regular vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a nonlinear restricted ARMA(1,m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We also evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets.

Predictive genetic test decisions for Huntington's disease: context, appraisal and new moral imperatives

Predictive testing is one of the new genetic technologies which, in conjunction with developing fields such as pharmacogenomics, promises many benefits for preventive and population health. Understanding how individuals appraise and make genetic test decisions is increasingly relevant as the technology expands. Lay understandings of genetic risk and test decision-making, located within holistic life frameworks including family or kin relationships, may vary considerably from clinical representations of these phenomena. The predictive test for Huntington's disease (HD), whilst specific to a single-gene, serious, mature-onset but currently untreatable disorder, is regarded as a model in this context. This paper reports upon a qualitative Australian study which investigated predictive test decision-making by individuals at risk for HD, the contexts of their decisions and the appraisals which underpinned them. In-depth interviews were conducted in Australia with 16 individuals at 50% risk for HD, with variation across testing decisions, gender, age and selected characteristics. Findings suggested predictive testing was regarded as a significant life decision with important implications for self and others, while the right not to know genetic status was staunchly and unanimously defended. Multiple contexts of reference were identified within which test decisions were located, including intra- and inter-personal frameworks, family history and experience of HID, and temporality. Participants used two main criteria in appraising test options: perceived value of, or need for the test information, for self and/or significant others, and degree to which such information could be tolerated and managed, short and long-term, by self and/or others. Selected moral and ethical considerations involved in decision-making are examined, as well as the clinical and socio-political contexts in which predictive testing is located. The paper argues that psychosocial vulnerabilities generated by the availability of testing technologies and exacerbated by policy imperatives towards individual responsibility and self-governance should be addressed at broader societal levels. (C) 2003 Elsevier Science Ltd. All rights reserved.

Relative accuracy and predictive ability of direct valuation methods, price to aggregate earnings method and a hybrid approach

In this paper, we assess the relative performance of the direct valuation method and industry multiplier models using 41 435 firm-quarter Value Line observations over an 11 year (1990–2000) period. Results from both pricingerror and return-prediction analyses indicate that direct valuation yields lower percentage pricing errors and greater return prediction ability than the forward price to aggregated forecasted earnings multiplier model. However, a simple hybrid combination of these two methods leads to more accurate intrinsic value estimates, compared to either method used in isolation. It would appear that fundamental analysis could benefit from using one approach as a check on the other.

An investigation of the relationships between service quality, customer satisfaction, perceived value and behavioural intentions

Customer satisfaction and service quality are two important concepts in the marketing literature. However, there has been some confusion about the conceptualisation and measurement of these two concepts and the nature of the relationship between them. The primary objective of this research was to develop a more thorough understanding of these concepts, and a model that could help to explain the links between them and their relationships with post-purchase behaviour. A preliminary theoretical model was developed, based on an exhaustive review of the literature. Following exploratory research, the model was revised by incorporating "Perceived Value" and "Perceived Sacrifice" to help explain customer's post-purchase behaviour. A longitudinal survey was conducted in the context of the restaurant industry, and the data were analysed using structural equation modelling. The results provided evidence to support the main research hypotheses. However, the effect of "Normative Expectations" on "Encounter Quality" was insignificant, and "Perceived Value" had a direct effect on "Behavioural Intentions" despite expectations that such an effect would be mediated through "Customer Satisfaction". It was also found that "Normative Expectations" were relatively more stable than "Predictive Expectations". It is argued that the present research significantly contributes to the marketing literature, and in particular the role of perceived value in the formation of customers' post-purchase behaviour. Further research efforts in this area are warranted.

Advanced predictive-analysis-based decision support for collaborative logistics networks

Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

The market value implications of pension asset allocation

Pension funds have been part of the private sector since the 1850's. Defined Benefit pension plans [DB], where a company promises to make regular contributions to investment accounts held for participating employees in order to pay a promised lifelong annuity, are significant capital markets participants, amounting to 2.3 trillion dollars in 2010 (Federal Reserve Board, 2013). In 2006, Statement of Financial Accounting Standards No.158 (SFAS 158), Employers' Accounting for Defined Benefit Pension and Other Postemployment Plans, shifted information concerning funding status and pension asset/liability composition from disclosure in the footnotes to recognition in the financial statements. I add to the literature by being the first to examine the effect of recent pension reform during the financial crisis of 2008-09. This dissertation is comprised of three related essays. In my first essay, I investigate whether investors assign different pricing multiples to the various classes of pension assets when valuing firms. The pricing multiples on all classes of assets are significantly different from each other, but only investments in bonds and equities were value-relevant during the recent financial crisis. Consistent with investors viewing pension liabilities as liabilities of the firm, the pricing multiples on pension liabilities are significantly larger than those on non-pension liabilities. The only pension costs significantly associated with firm value are actual rate of return and interest expense. In my second essay, I investigate the role of accruals in predicting future cash flows, extending the Barth et al. (2001a) model of the accrual process. Using market value of equity as a proxy for cash flows, the results of this study suggest that aggregate accounting amounts mask how the components of earnings affect investors' ability to predict future cash flows. Disaggregating pension earnings components and accruals results in an increase in predictive power. During the 2008-2009 financial crisis, however, investors placed a greater (and negative) weight on the incremental information contained in the individual components of accruals. The inferences are robust to alternative specifications of accruals. Finally, in my third essay I investigate how investors view under-funded plans. On average, investors: view deficits arising from under-funded plans as belonging to the firm; reward firms with fully or over-funded pension plans; and encourage those funds with unfunded pension plans to become funded. Investors also encourage conservative pension asset allocations to mitigate firm risk, and smaller firms are perceived as being better able to handle the risk associated with underfunded plans. During the financial crisis of 2008-2009 underfunded status had a lower negative association with market value. In all three models, there are significant differences in pre- and post- SFAS 158 periods. These results are robust to various scenarios of the timing of the financial crisis and an alternative measure of funding.

An investigation of lower limb joint powers as a predictive measure of countermovement vertical jump height.

Measuring and tracking athletic performance is crucial to an athlete’s development and the countermovement vertical jump is often used to measure athletic performance, particularly lower limb power. The linear power developed in the lower limb is estimated through jump height. However, the relationship between angular power, produced by the joints of the lower limb, and jump height is not well understood. This study examined the contributions of the kinetic value of angular power, and its kinematic component, angular velocity, of the lower limb joints to jump height in the countermovement vertical jump. Kinematic and kinetic data were gathered from twenty varsity-level basketball and volleyball athletes as they performed six maximal effort jumps in four arm swing conditions: no-arm involvement, single-non-dominant arm swing, single-dominant arm swing, and two-arm swing. The displacement of the whole body centre of mass, peak joint powers, peak angular velocity, and locations of the peaks as a percentage of the jump’s takeoff period, were computed. Linear regressions assessed the relationship of the variables to jump height. Results demonstrated that knee peak power (p = 0.001, ß = 0.363, r = 0.363), its location within takeoff period (p = 0.023, ß = -0.256, r = 0.256), and peak knee peak angular velocity (p = 0.005, ß = 0.310, r = 0.310) were moderately linked to increased jump height. Additionally, the location, within the takeoff period, of the peak angular velocities of the hip (p = 0.003, ß = -0.318, r = 0.419) and ankle (p = 0.011, ß = 0.270, r = 0.419) were positively linked to jump height. These results highlight the importance of training the velocity and timing of joint motion beyond traditional power training protocols as well as the importance of further investigation into appropriate testing protocol that is sensitive to the contributions by individual joints in maximal effort jumping.

Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression

Assessing the fit of a model is an important final step in any statistical analysis, but this is not straightforward when complex discrete response models are used. Cross validation and posterior predictions have been suggested as methods to aid model criticism. In this paper a comparison is made between four methods of model predictive assessment in the context of a three level logistic regression model for clinical mastitis in dairy cattle; cross validation, a prediction using the full posterior predictive distribution and two “mixed” predictive methods that incorporate higher level random effects simulated from the underlying model distribution. Cross validation is considered a gold standard method but is computationally intensive and thus a comparison is made between posterior predictive assessments and cross validation. The analyses revealed that mixed prediction methods produced results close to cross validation whilst the full posterior predictive assessment gave predictions that were over-optimistic (closer to the observed disease rates) compared with cross validation. A mixed prediction method that simulated random effects from both higher levels was best at identifying the outlying level two (farm-year) units of interest. It is concluded that this mixed prediction method, simulating random effects from both higher levels, is straightforward and may be of value in model criticism of multilevel logistic regression, a technique commonly used for animal health data with a hierarchical structure.

Prognostic value of fat mass and skeletal muscle mass determined by computed tomography in patients who underwent transcatheter aortic valve implantation

Body composition (fat mass [FM] and skeletal muscle mass [SMM]) predicts clinical outcomes. In particular, loss of SMM (sarcopenia) is associated with frailty and mortality. There are no data on the prevalence and impact of FM and SMM in patients undergoing transcatheter aortic valve implantation (TAVI). The objective of this study is to determine body composition from pre-TAVI computed tomography (CT) and evaluate its association with clinical outcomes in patients who underwent TAVI. A total of 460 patients (mean age 81 ± 8 years, men: 51%) were included. Pre-TAVI CTs of the aorto-ilio-femoral axis were analyzed for FM and SMM cross-sectional area at the level of the third lumbar vertebrae (L3). Regression equations correlating cross-sectional area at L3 to total body FM and SMM were used to determine prevalence of sarcopenia, obesity, and sarcopenic obesity in patients (64%, 65%, and 46%, respectively). Most TAVI procedures were performed through a transfemoral approach (59%) using a balloon-expandable valve (94%). The 30-day and mid-term (median 12 months [interquartile range 6 to 27]) mortality rates were 6.1% and 29.6%, respectively. FM had no association with clinical outcomes, but sarcopenia predicted cumulative mortality (hazard ratio 1.55, 95% confidence interval 1.02 to 2.36, p = 0.04). In conclusion, body composition analysis from pre-TAVI CT is feasible. Sarcopenia, obesity, and sarcopenic obesity are prevalent in the TAVI population, with sarcopenia predictive of cumulative mortality.