Quality and Safety of Import and Export Food

2014 ◽  
pp. 89-116
Author(s):  
Ivanov I. V. ◽  
◽  
Shvabskii O. R. ◽  
Minulin I. B. ◽  
Shcheblykina A. A. ◽  
...  

Author(s):  
A. Vovkohon ◽  
V. Nadtochiy ◽  
G. Kalinina ◽  
O. Hrebelnyk ◽  
N. Fedoruk ◽  
...  

The article highlights comparative research results of milk quality indices obtained from the milking in specialized milking halls with such milking units as «Parallel», «Carousel» or in stalls with the milking unit «Molokoprovid». The fat and protein mass fraction, dry matter and fat-free dry matter, density, titratable and active acidity, heat resistance and freezing point have been determined according to the accepted techniques. The electrical conductivity of milk has been determined by using the analytical device MD-20 MAS-D-TEC. The total amount of milk bacteria has been determined by reductase reduction test and by seeding method in Petri dish. The milk quality has been investigated by the fermentation and rennet fermentation tests. The higher indices of the fat mass fraction, the protein mass fraction and the dry substance concentration of milk, obtained in specialized milking halls, have been established. This is not statistically significant. Milk, obtained from the milking unit «Molokoprovid», has higher index of titratable acidity, lower thermal stability in comparison with milk, obtained from specialized milking halls with milking units «Parallel» and «Carousel». It has been determined that there is the bacteria insemination increase in milk received from milking cows in stalls in comparison with milk, obtained from milking in specialized halls. Milk, obtained from the milking unit «Carousel», indicates the subclinical form of mastitis in cows or «Carousel» operation violationif there is in 1,8 mS/cm conductivity increase above average index 4,6 mS/cm. Key words: technology, quality and safety of milk, milking, milking unit, milking hall, bacterial insemination.


2013 ◽  
Vol 9 (3) ◽  
pp. 215-227 ◽  
Author(s):  
Suzanne Eggins ◽  
Diana Slade

Clinical handover – the transfer between clinicians of responsibility and accountability for patients and their care (AMA 2006) – is a pivotal and high-risk communicative event in hospital practice. Studies focusing on critical incidents, mortality, risk and patient harm in hospitals have highlighted ineffective communication – including incomplete and unstructured clinical handovers – as a major contributing factor (NSW Health 2005; ACSQHC 2010). In Australia, as internationally, Health Departments and hospital management have responded by introducing standardised handover communication protocols. This paper problematises one such protocol – the ISBAR tool – and argues that the narrow understanding of communication on which such protocols are based may seriously constrain their ability to shape effective handovers. Based on analysis of audio-recorded shift-change clinical handovers between medical staff, we argue that handover communication must be conceptualised as inherently interactive and that attempts to describe, model and teach handover practice must recognise both informational and interactive communication strategies. By comparing the communicative performance of participants in authentic handover events we identify communication strategies that are more and less likely to lead to an effective handover and demonstrate the importance of focusing close up on communication to improve the quality and safety of healthcare interactions.


Author(s):  
Karandeep Singh Singh ◽  
Kaitlin Drouin Drouin ◽  
Lisa P. Newmark Newmark ◽  
Ronen Rozenblum Rozenblum

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.


Author(s):  
Devi Pratami

A project always has risks that can lead to project failure. In the project, a risk analysis is required to provide an evaluation for the project to proceed as planned. In the event of inadequate planning and ineffective control, it will result in irregularities identified as a risk to the project. This study aims to analyze the qualitative risk on Fiber Optic Installaion project in Sukabumi, West Java, Indonesia. In addition, risk assessment is undertaken on project implementation. Assessment of risk using the impact and probability to measure the impact of risk occurrence. The impacts are more detailed by classified by time impact, cost impact, quality impact, safety and security impact, proximity. The result is there are 36 risk that may occur and mostly risks are associaated by quality and safety&security impact.


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