Brexit and the Implications of Food Safety Cultural Compliance in the Food Manufacturing Sector

In theory food safety is a critical measurement, not just for economic and legal reasons but also for the moral integrity of the organisation. However, in reality, the number of accidents or incidents particularly in the food manufacturing sector is a serious cause for concern. The problem is further compounded with the onset of Brexit. Given the UK government’s floundering negotiation talks and the pending conservative leadership challenge, it has resulted in a climate of uncertainty, a devaluation of currency and economic instability. Food manufacturers along with other commercial businesses are reluctant to further invest until the economic future is more transparent. In consequence, food manufacturers are seeking efficiency savings, whilst aiming not to compromise food safety compliance. Whilst there are areas of best practice, sadly there are an increasing number of examples in which failure to comply to food safety is resulting in lost of business, serious injury and in certain cases fatalities. This paper addresses Food Safety Cultural Compliance within UK Food Manufacturers and identifies core issues that hinder the establishment of a proactive food safety culture. The research study adopts a mixed methods approach in which five UK food manufacturers were consulted via 15 semi-structured interviews with management and three focused groups. The data collected clearly indicates a commitment to food safety compliance. However, the majority of organisations struggled to maintain consistent levels of food safety compliance despite implementing costly training and development initiatives. Their strategic and operational drive to both enhance and maintain a positive food safety culture was also undermined with the uncertainty of economic pressures and the quagmire of Brexit. The paper concludes with a series of commercially viable recommendations within the context of the Brexit divorce and provides a clear contribution to the community of practice

Artificial Immune System based on Hybrid and External Memory for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be further improved because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. Thus, a hybrid PSO-AIS and a new external memory CSA based scheme called EMCSA are proposed. In hybrid PSO-AIS, the good features of PSO and AIS are combined in order to reduce any limitation. Alternatively, EMCSA captures all the best antibodies into the memory in order to enhance global searching capability. In this preliminary study, the results show that the performance of hybrid PSO-AIS compares favourably with other algorithms while EMCSA produced moderate results in most of the simulations

Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization

Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further improved as the hypermutation in CSA itself cannot always guarantee a better solution. Conversely, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have an inclination to converge prematurely. In this work, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. Simulation results show that the proposed algorithm is able to enhance the performance of the conventional CSA in terms of accuracy and stability for single objective functions

Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for solving optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively,Genetic Algorithms (GAs) and Particle Swarm Optimization(PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solution for each exposure (iteration) namely Single Best Remainder (SBR) CSA. In this study, the results show that the performance of the proposed algorithm (SBR-CSA) compares favourably with other algorithms while Half Best Insertion (HBI) CSA produced moderate results in most of the simulations

Mapping of serotype-specific, immunodominant epitopes in the NS-4 region of hepatitis C virus (HCV):use of type-specific peptides to serologically differentiate infections with HCV types 1, 2, and 3

The effect of sequence variability between different types of hepatitis C virus (HCV) on the antigenicity of the NS-4 protein was investigated by epitope mapping and by enzyme-linked immunosorbent assay with branched oligopeptides. Epitope mapping of the region between amino acid residues 1679 and 1768 in the HCV polyprotein revealed two major antigenic regions (1961 to 1708 and 1710 to 1728) that were recognized by antibody elicited upon natural infection of HCV. The antigenic regions were highly variable between variants of HCV, with only 50 to 60% amino acid sequence similarity between types 1, 2, and 3. Although limited serological cross-reactivity between HCV types was detected between peptides, particularly in the first antigenic region of NS-4, type-specific reactivity formed the principal component of the natural humoral immune response to NS-4. Type-specific antibody to particular HCV types was detected in 89% of the samples from anti-HCV-positive blood donors and correlated almost exactly with genotypic analysis of HCV sequences amplified from the samples by polymerase chain reaction. Whereas almost all blood donors appeared to be infected with a single virus type (97%), a higher proportion of samples (40%) from hemophiliacs infected from transfusion of non-heat-inactivated clotting factor contained antibody to two or even all three HCV types, providing evidence that long-term exposure may lead to multiple infection with different variants of HCV

(In Press) Rheological characterisation of municipal sludge: A review

Sustainable sludge management is becoming a major issue for wastewater treatment plants due to increasing urban populations and tightening environmental regulations for conventional sludge disposal methods. To address this problem, a good understanding of sludge behaviour is vital to improve and optimize the current state of wastewater treatment operations. This paper provides a review of the recent experimental works in order for researchers to be able to develop a reliable characterization technique for measuring the important properties of sludge such as viscosity, yield stress, thixotropy, and viscoelasticity and to better understand the impact of solids concentrations, temperature, and water content on these properties. In this context, choosing the appropriate rheological model and rheometer is also important

Clear model fluids for peculiar rheological properties of thickened digested sludge

Optimising flow processes in wastewater treatment plants requires that designers and operators take into account the flow properties of the sludge. Moreover, due to increasingly more stringent conditions on final disposal avenues such as landfill, composting, incineration etc., practitioners need to produce safer sludge in smaller quantities. Anaerobic digestion is a key treatment process for solids treatment and pathogen reduction. Due to the inherent opacity of sludge, it is impossible to visualise the mixing and flow patterns inside an anaerobic digester. Therefore, choosing an appropriate transparent model fluid which can mimic the rheological behaviour of sludge is imperative for visualisation of the hydrodynamic functioning of an anaerobic digester. Digested sludge is a complex material with time dependent non-Newtonian thixotropic characteristics. In steady state, it can be modelled by a basic power-law. However, for short-time processes the HerscheleBulkley model can be used to model liquid-like properties. The objective of this study was to identify transparent model fluids which will mimic the behaviour of real sludge. A comparison of three model fluids, Carboxymethyl Cellulose (CMC), Carbopol gel and Laponite clay revealed that these fluids could each model certain aspects of sludge behaviour. It is concluded that the rheological behaviour of sludge can be modelled using CMC in steady state flow at high shear rates, Carbopol gel for short-time flow processes and Laponite clay suspension where time dependence is dominant

Oxidative stress in cardiac hypertrophy: From molecular mechanisms to novel therapeutic targets.

When faced with increased workload the heart undergoes remodelling, where it increases its muscle mass in an attempt to preserve normal function. This is referred to as cardiac hypertrophy and if sustained, can lead to impaired contractile function. Experimental evidence supports oxidative stress as a critical inducer of both genetic and acquired forms of cardiac hypertrophy, a finding which is reinforced by elevated levels of circulating oxidative stress markers in patients with cardiac hypertrophy. These observations formed the basis for using antioxidants as a therapeutic means to attenuate cardiac hypertrophy and improve clinical outcomes. However, the use of antioxidant therapies in the clinical setting has been associated with inconsistent results, despite antioxidants having been shown to exert protection in several animal models of cardiac hypertrophy. This has forced us to revaluate the mechanisms, both upstream and downstream of oxidative stress, where recent studies demonstrate that apart from conventional mediators of oxidative stress, metabolic disturbances, mitochondrial dysfunction and inflammation as well as dysregulated autophagy and protein homeostasis contribute to disease pathophysiology through mechanisms involving oxidative stress. Importantly, novel therapeutic targets have been identified to counteract oxidative stress and attenuate cardiac hypertrophy but more interestingly, the repurposing of drugs commonly used to treat metabolic disorders, hypertension, peripheral vascular disease, sleep disorders and arthritis have also been shown to improve cardiac function through suppression of oxidative stress. Here, we review the latest literature on these novel mechanisms and intervention strategies with the aim of better understanding the complexities of oxidative stress for more precise targeted therapeutic approaches to prevent cardiac hypertrophy

On Sparsification for Computing Treewidth

We investigate whether an n-vertex instance (G,k) of Treewidth, asking whether the graph G has treewidth at most k, can efficiently be made sparse without changing its answer. By giving a special form of OR-cross-composition, we prove that this is unlikely: if there is an e > 0 and a polynomial-time algorithm that reduces n-vertex Treewidth instances to equivalent instances, of an arbitrary problem, with O(n^{2-e}) bits, then NP is in coNP/poly and the polynomial hierarchy collapses to its third level. Our sparsification lower bound has implications for structural parameterizations of Treewidth: parameterizations by measures that do not exceed the vertex count, cannot have kernels with O(k^{2-e}) bits for any e > 0, unless NP is in coNP/poly. Motivated by the question of determining the optimal kernel size for Treewidth parameterized by vertex cover, we improve the O(k^3)-vertex kernel from Bodlaender et al. (STACS 2011) to a kernel with O(k^2) vertices. Our improved kernel is based on a novel form of treewidth-invariant set. We use the q-expansion lemma of Fomin et al. (STACS 2011) to find such sets efficiently in graphs whose vertex count is superquadratic in their vertex cover number.Comment: 21 pages. Full version of the extended abstract presented at IPEC 201