During the planning and controlling of the construction process, most attention is focu sed on risk analysis, especially in the context of final costs and deadlines of the investment. In this analysis, the primary and most significant concern is the proper identification and quantification of events, which on a certain level of probability may affect the development process. This paper presents the result of a risk analysis for a particular building object, made after completion of the investment and accepting it for use. Knowledge of the planned values and the actual investment process allowed for the identification of the events and their effects that in this case have significantly disrupted the investment process. The limited total cost of the investment project in question had a considerable impact on the progress of the project execution. Despite three transitions of administrative procedures, the opening date of the shopping centre was delayed by only three weeks.
Hass avocado cultivation in Colombia has grown rapidly in area in recent years. It is being planted in marginal areas, which leads to low yields, and in many cases is related to diseases. Ecological niche modeling (ENM) can offer a view of the potential geographic and environmental distribution of diseases, and thus identify areas with suitable or unsuitable conditions for their development. The aim of the study was to assess current and potential distribution of the major diseases on Hass avocado in Colombia. Areas planted with Hass avocado in Antioquia, Colombia were sampled for diseases including the following pathogens: Phytophthora cinnamomi, Verticillium sp., Lasiodiplodia theobromae, Phytophthora palmivora, Colletotrichum gloeosporioides sensu lato, Pestalotia sp., and Capnodium sp., and one disorder hypoxia-anoxia. These pathogens were selected based on their relevance (incidence-severity) and capacity to cause damage in different tissues of avocado plants. Severity and incidence of each disease were related to environmental information from vegetation indices and topographic variables using maximum entropy modeling approaches (MaxEnt). Models were calibrated only across areas sampled, and then transferred more broadly to areas currently planted, and to potential zones for planting. Combinations of best performance and low omission rates were the basis for model selection. Results show that Hass avocado has been planted in areas highly conducive for many pathogens, particularly for Phytophthora cinnamomi and hypoxia-anoxia disorder. Ecological niche modeling approaches offer an alternative toolset for planning and making assessments that can be incorporated into disease management plans.
Morocco is basically an agricultural country; almost 40% of the workforce is employed in this sector. Xylella fastidiosa is a xylem-inhabiting pathogen which can infect more than 300 plant species, although most host species are symptomless. Until relatively recently, X. fastidiosa was primarily limited to North and South America, but in 2013 a widespread epidemic of olive quick decline syndrome caused by this fastidious pathogen appeared in southeastern Italy, and later several cases of X. fastidiosa outbreaks have been reported in other European countries (France, Germany and Spain). Following these recently confirmed findings of X. fastidiosa in the European Union, this bacterium has become a serious threat to the Moroccan flora. The national phytosanitary authorities have adopted several measures to prevent the introduction of X. fastidiosa into the national territory by deciding, inter alia, to suspend importation of host plant species to the bacterium from infected areas. This paper presents the phytosanitary risk of this bacterium in Morocco.
Overseas mining investment generally faces considerable risk due to a variety of complex risk factors. Therefore, indexes are often based on conditions of uncertainty and cannot be fully quantified. Guided by set pair analysis (SPA) theory, this study constructs a risk evaluation index system based on an analysis of the risk factors of overseas mining investment and determines the weights of factors using entropy weighting methods. In addition, this study constructs an identity-discrepancycontrary risk assessment model based on the 5-element connection number. Both the certainty and uncertainty of the various risks are treated uniformly in this model and it is possible to mathematically describe and quantitatively express complex system decisions to evaluate projects. Overseas mining investment risk and its changing trends are synthetically evaluated by calculating the adjacent connection number and analyzing the set pair potential. Using an actual overseas mining investment project as an example, the risk of overseas mining investment can be separated into five categories according to the risk field, and then the evaluation model is quantified and specific risk assessment results are obtained. Compared to the field investigation, the practicability and effectiveness of the evaluation method are illustrated. This new model combines static and dynamic factors and qualitative and quantitative information, which improves the reliability and accuracy of risk evaluation. Furthermore, this evaluation method can also be applied to other similar evaluations and has a certain scalability.
In the study we introduce an extension to a stochastic volatility in mean model (SV-M), allowing for discrete regime switches in the risk premium parameter. The logic behind the idea is that neglecting a possibly regimechanging nature of the relation between the current volatility (conditional standard deviation) and asset return within an ordinary SV-M specication may lead to spurious insignicance of the risk premium parameter (as being ‛averaged out’ over the regimes). Therefore, we allow the volatility-in-mean eect to switch over dierent regimes according to a discrete homogeneous two-state Markov chain. We treat the new specication within the Bayesian framework, which allows to fully account for the uncertainty of model parameters, latent conditional variances and hidden Markov chain state variables. Standard Markov Chain Monte Carlo methods, including the Gibbs sampler and the Metropolis-Hastings algorithm, are adapted to estimate the model and to obtain predictive densities of selected quantities. Presented methodology is applied to analyse series of the Warsaw Stock Exchange index (WIG) and its sectoral subindices. Although rare, once spotted the switching in-mean eect substantially enhances the model t to the data, as measured by the value of the marginal data density.
Background: Recoarctation (reCoA) of the aorta is a common complication after the Norwood procedure. Untreated, it can lead to failure of the systemic ventricle and death. The main goal of the study is to defi ne risk factors of reCoA after the Norwood procedure in hypoplastic left heart syndrome (HLHS). Methods: We retrospectively analyzed the pre-, intra- and postoperative data of 96 successive patients who underwent the Norwood procedure between 2007 and 2011. In case of reCoA balloon angioplasty was performed. We analyzed and compared the data of the patients with reCoA and without reCoA using the StatSoft STATISTICA™ 10 soft ware. Results: ReCoA was noted in 23 patients (33.3%). Th is complication was diagnosed 95.1 days (49–156 days) on the average aft er the Norwood procedure. Balloon angioplasty successfully allowed for decreasing the mean gradient across the site of the narrowing from the average 27.5 mmHg to the average 9.7 mmHg (p = 0.008) and enlarged the neo-isthmus by the average of 2 mm (p <0.05). Th e risks factors seemed to be the diameter of the ascending aorta OR = 7.82 (p = 0.001), atresia of the mitral valve OR = 7.00 (p = 0.003) and atresia of the aortic valve — OR = 6.22 (p = 0.002). Conclusion: Balloon angioplasty seems to be an eff ective intervention in case of reCoA. A low diameter of the native ascending aorta (≤3mm) and the presence of atresia of the mitral and/or aortic valve should intensify the vigilance of a cardiologist in the search for signs of reCoA of the aorta.
The s-period ahead Value-at-Risk (VaR) for a portfolio of dimension n is considered and its Bayesian analysis is discussed. The VaR assessment can be based either on the n-variate predictive distribution of future returns on individual assets, or on the univariate Bayesian model for the portfolio value (or the return on portfolio). In both cases Bayesian VaR takes into account parameter uncertainty and non-linear relationship between ordinary and logarithmic returns. In the case of a large portfolio, the applicability of the n-variate approach to Bayesian VaR depends on the form of the statistical model for asset prices. We use the n-variate type I MSF-SBEKK(1,1) volatility model proposed specially to cope with large n. We compare empirical results obtained using this multivariate approach and the much simpler univariate approach based on modelling volatility of the value of a given portfolio.
The study aims at a statistical verification of breaks in the risk-return relationship for shares of individual companies quoted at the Warsaw Stock Exchange. To this end a stochastic volatility model incorporating Markov switching in-mean effect (SV-MS-M) is employed. We argue that neglecting possible regime changes in the relation between expected return and volatility within an ordinary SV-M specification may lead to spurious insignificance of the risk premium parameter (as being ’averaged out’ over the regimes).Therefore, we allow the volatility-in-mean effect to switch over different regimes according to a discrete homogeneous two- or three-state Markov chain. The model is handled within Bayesian framework, which allows to fully account for the uncertainty of model parameters, latent conditional variances and state variables. MCMC methods, including the Gibbs sampler, Metropolis-Hastings algorithm and the forward-filtering-backward-sampling scheme are suitably adopted to obtain posterior densities of interest as well as marginal data density. The latter allows for a formal model comparison in terms of the in-sample fit and, thereby, inference on the ’adequate’ number of the risk premium regime
Various approaches have been introduced over the years to evaluate information in the expected utility framework. This paper analyzes the relationship between the degree of risk aversion and the selling price of information in a lottery setting with two actions. We show that the initial decision on the lottery as well as the attitude of the decision maker towards risk as a function of the initial wealth level are critical to characterizing this relationship. When the initial decision is to reject, a non-decreasingly risk averse decision maker asks for a higher selling price as he gets less risk averse. Conversely, when the initial decision is to accept, non-increasingly risk averse decision makers ask a higher selling price as they get more risk averse if information is collected on bounded lotteries. We also show that the assumption of the lower bound for lotteries can be relaxed for the quadratic utility family.
In the existent world of continuous production systems, strong attention has been waged to anonymous risk that probably generates significant apprehension. The forecast for net present value is extremely important for any production plant. The objective of this paper is to implement Monte Carlo simulation technique for perceiving the impact of risk and uncertainty in prediction and forecasting company’s profitability. The production unit under study is interested to make the initial investment by installing an additional spray dryer plant. The expressive values acquied from the Monte Carlo technique established a range of certain results. The expected net present value of the cash flow is $14,605, hence the frequency chart outcomes confirmed that there is the highest level of certainty that the company will achieve its target. To forecast the net present value for the next period, the results confirmed that there are 50.73% chances of achieving the outcomes. Considering the minimum and maximum values at 80% certainty level, it was observed that 80% chances exist that expected outcomes will be between $5,830 and $22,587. The model’s sensitivity results validated that cash inflows had a greater sensitivity level of 21.1% and the cash inflows for the next year as 19.7%. Cumulative frequency distribution confirmed that the probability to achieve a maximum value of $23,520 is 90 % and for the value of $6,244 it is about 10 %. These validations suggested that controlling the expenditures, the company’s outflows can also be controlled definitely.
The article describes a shock safety modeling method for low-voltage electric devices, based on using a Bayesian network. This method allows for taking into account all possible combinations of the reliability and unreliability states for the shock protection elements under concern. The developed method allows for investigating electric shock incidents, analysing and assessing shock risks, as well as for determining criteria of dimensioning shock protection means, also with respect to reliability of the particular shock protection elements. Dependencies for determining and analysing the probability of appearance of reliability states of protection as well as an electric shock risk are presented in the article.
The article presents a shock safety model of an indirect contact with a low-voltage electric device. This model was used for computations and analyses concerning the following: the probabilities of appearance of the particular shock protection unreliability states, electric shock states (ventricular fibrillation), contributions of the unreliability of different shock protection elements to the probability of occurrence of these states, as well as the risk of electric shock (and the shock safety), and contributions of the intensity of occurrence of damages to different shock protection elements to this risk. An example of a possibility to reduce the risk of an electric shock through changing the intensity of occurrence of damages to the selected protection elements was provided.