Engineering handbook

2004 handbook for the faculty of Engineerin

The public health risk posed by Listeria monocytogenes in frozen fruit and vegetables including herbs, blanched during processing

A multi-country outbreak ofListeria monocytogenesST6 linked to blanched frozen vegetables (bfV)took place in the EU (2015–2018). Evidence of food-borne outbreaks shows thatL. monocytogenesisthe most relevant pathogen associated with bfV. The probability of illness per serving of uncooked bfV,for the elderly (65–74 years old) population, is up to 3,600 times greater than cooked bfV and verylikely lower than any of the evaluated ready-to-eat food categories. The main factors affectingcontamination and growth ofL. monocytogenesin bfV during processing are the hygiene of the rawmaterials and process water; the hygienic conditions of the food processing environment (FPE); andthe time/Temperature (t/T) combinations used for storage and processing (e.g. blanching, cooling).Relevant factors after processing are the intrinsic characteristics of the bfV, the t/T combinations usedfor thawing and storage and subsequent cooking conditions, unless eaten uncooked. Analysis of thepossible control options suggests that application of a complete HACCP plan is either not possible orwould not further enhance food safety. Instead, specific prerequisite programmes (PRP) andoperational PRP activities should be applied such as cleaning and disinfection of the FPE, water control,t/T control and product information and consumer awareness. The occurrence of low levels ofL. monocytogenesat the end of the production process (e.g.<10 CFU/g) would be compatible with thelimit of 100 CFU/g at the moment of consumption if any labelling recommendations are strictly followed(i.e. 24 h at 5°C). Under reasonably foreseeable conditions of use (i.e. 48 h at 12°C),L. monocytogeneslevels need to be considerably lower (not detected in 25 g). Routine monitoring programmes forL. monocytogenesshould be designed following a risk-based approach and regularly revised based ontrend analysis, being FPE monitoring a key activity in the frozen vegetable industry

Human Resource Management and Productivity

In this chapter we examine the relationship between Human Resource Management (HRM) and productivity. HRM includes incentive pay (individual and group) as well as many nonpay aspects of the employment relationship such as matching (hiring and firing) and work organization (e.g. teams, autonomy). We place HRM more generally within the literature on management practices and productivity. We start with some facts on levels and trends of both HRM and productivity and the main economic theories of HRM. We look at some of the determinants of HRM - risk, competition, ownership and regulation. The largest section analyses the impact of HRM on productivity emphasizing issues of methodology, data and results (from micro-econometric studies). We conclude briefly with suggestions of avenues for future frontier work.human resource management, productivity, personnel economics

A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

Engineering handbook

2006 handbook for the faculty of Engineerin