The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The books main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology

An Introduction to MultiAgent Systems.

Campylobacter is one of the most common bacterial pathogens of food safety concern. Campylobacter jejuni infects chickens by 2–3 weeks of age and colonized chickens carry a high C. jejuni load in their gut without developing clinical disease. Contamination of meat products by gut contents is difficult to prevent because of the high numbers of C. jejuni in the gut, and the large percentage of birds infected. Therefore, effective intervention strategies to limit human infections of C. jejuni should prioritize the control of pathogen transmission along the food supply chain. To this end, there have been ongoing efforts to develop innovative ways to control foodborne pathogens in poultry to meet the growing customers’ demand for poultry meat that is free of foodborne pathogens. In this review, we discuss various approaches that are being undertaken to reduce Campylobacter load in live chickens (pre-harvest) and in carcasses (post-harvest). We also provide some insights into optimization of these approaches, which could potentially help improve the pre- and post-harvest practices for better control of Campylobacter.

/pdf/intervention-strategies-to-control-campylobacter-at-3cde3f7w.pdf

Intervention Strategies to Control Campylobacter at Different Stages of the Food Chain

Globally, Campylobacter is a significant contributor to gastroenteritis. Efficient pathogens are qualified by their virulence power, resistance to antibiotics and epidemic spread. However, the correlation between antimicrobial resistance (AR) and the pathogenicity power of pathogens is complex and poorly understood. In this study, we aimed to investigate genes encoding virulence and AR mechanisms in 177 Campylobacter isolates collected from layer hens and eggs in Tunisia and to assess associations between AR and virulence characteristics. Virulotyping was determined by searching 13 virulence genes and AR-encoding genes were investigated by PCR and MAMA-PCR. The following genes were detected in C. jejuni and C. coli isolates: tet(O) (100%/100%), blaOXA-61 (18.82%/6.25%), and cmeB (100%/100%). All quinolone-resistant isolates harbored the Thr-86-Ile substitution in GyrA. Both the A2074C and A2075G mutations in 23S rRNA were found in all erythromycin-resistant isolates; however, the erm(B) gene was detected in 48.38% and 64.15% of the C. jejuni and C. coli isolates, respectively. The machine learning algorithm Random Forest was used to determine the association of virulence genes with AR phenotypes. This analysis showed that C. jejuni virulotypes with gene clusters encompassing the racR, ceuE, virB11, and pldA genes were strongly associated with the majority of phenotypic resistance. Our findings showed high rates of AR and virulence genes among poultry Campylobacter, which is a cause of concern to human health. In addition, the correlations of specific virulence genes with AR phenotypes were established by statistical analysis.

/pdf/relationships-between-virulence-genes-and-antibiotic-2nmz7bmg.pdf

Relationships between Virulence Genes and Antibiotic Resistance Phenotypes/Genotypes in Campylobacter spp. Isolated from Layer Hens and Eggs in the North of Tunisia: Statistical and Computational Insights

Animal and food sources are seen as a potential transmission pathway of multi-drug resistance (MDR) micro-organisms to humans. Escherichia. coli is frequently used as an indicator of fecal contamination in the food industry and known as a reservoir of antimicrobial resistance genes (ARGs). Microbial contamination as a major outcome for the poultry and egg industry and is a serious public health problem. In the present study we performed the quantification of β-glucoronidase positive E. coli in 60 fecal samples of breeding and laying hens collected in Portugal in 2019. Phylogenetic and pathotypic characterization, antimicrobial susceptibility, and detection of resistant extended-spectrum β-lactamase (ESBL) genes were assessed. The phylogenetic and pathogenic characterization and detection of ESBL genes were assessed by real-time PCR and antimicrobial susceptibility was evaluated using the disk diffusion method. Overall, E. coli quantification was 6.03 log CFU/g in breeding hens and 6.02 log CFU/g in laying hens. The most frequent phylogroups were B1. None of the isolates was classified as diarrheagenic E. coli (DEC). In total, 57% of the isolates showed MDR and 3.8% were positive for ESBL. Our study highlights that consumers may be exposed to MDR E. coli, presenting a major hazard to food safety and a risk to public health.

/pdf/evaluation-of-antimicrobial-resistance-of-different-2ec12u7b.pdf

Evaluation of Antimicrobial Resistance of Different Phylogroups of Escherichia coli Isolates from Feces of Breeding and Laying Hens

Cyber–Physical Systems (CPS) interconnect embedded computing technologies into the physical world, forming a complex, multi-disciplinary, physically-effective system. However, interacting with the physical world brings unpredictability, as real-world events are uncertain and dynamic by its nature. Consequently, CPS must be capable of reasoning about these unpredictable situations and adapt their behaviour accordingly. Therefore, it is essential to enhance the intelligence of CPS to overcome this challenge effectively, leading to the development of smart CPS. As an approach, a reasoning mechanism can support the system to reason about its state and actions to handle unpredictable changes. In this study, we employ intelligent Belief-Desire-Intention (BDI) agents to achieve this objective. Nevertheless, traditional logic approaches based on crisp numbers, which are used in typical BDI agents, may not effectively handle uncertainty. Hence, the reasoning mechanism can be extended with fuzzy logic to deal with run-time uncertainties. Thus, the target system's suitable inputs, outputs and reasoning phases are fuzzified to indicate the impact of the fuzzy logic on the CPS. To this end, an architecture is provided to deploy BDI agents integrated with a fuzzy reasoning model for handling imprecise information and improving process control. The approach is validated and evaluated on a complex case study with a heterogeneous structure controlled by multiple agents, namely the "smart production system". The multiple modular and end-to-end experiments conducted reveal that, on the whole, the fuzzy BDI agent outperforms the classical one by up to three times, with only requiring approximately 10% more computation time. 

On the impact of fuzzy-logic based BDI agent model for cyber-physical systems

Despite the importance of eggs in the human diet, and unlike other products, for which food safety risks are widely investigated, information on the occurrence of Campylobacter and antimicrobial resistance in eggs and layer hen flocks is lacking in Tunisia. This study was conducted to determine the occurrence of Campylobacter and the antimicrobial resistance in layer hens and on eggshells. Thus, 366 cloacal swabs and 86 eggshell smear samples were collected from five layer hen farms in the North-East of Tunisia. The occurrence of Campylobacter infection, and the antimicrobial resistance rates and patterns, were analyzed. The occurrence rates of Campylobacter infection in laying hens and eggshells were 42.3% and 25.6%, respectively, with a predominance of C. jejuni (68.4%, 81.9%), followed by C. coli (31.6%, 18.2%). The antimicrobial susceptibility testing revealed high resistance rates against macrolides, tetracycline, quinolones, β-lactams, and chloramphenicol, with percentages ranging from 35.5% to 100%. All isolates were multidrug resistant (MDR) and five resistance patterns were observed. These results emphasized the risk to consumer health and the need to establish a surveillance strategy to control and prevent the emergence and the spread of resistant strains of Campylobacter in poultry and humans.

/pdf/campylobacter-spp-in-eggs-and-laying-hens-in-the-north-east-1z56yw18.pdf

Campylobacter spp. in Eggs and Laying Hens in the North-East of Tunisia: High Prevalence and Multidrug-Resistance Phenotypes

With the emergence of new requirements in manufacturing, mainly the product customization trend, implementing a solution for product quality control becomes an increasingly complicated task. In this modern industrial context, those solutions need to take into account the constant updates in the product design and combine its resources in the most effective manner in order to meet the new product specifications. By reason of the lack of available technical solutions, this task is usually carried out by human operators which is time consuming, tiring and not always a reliable solution. In the present work, a self-* automatic quality control platform based on cyber-physical systems and multi-agents paradigm is presented. The solution is validated within a real industrial usecase.

A CPS-Agent self-adaptive quality control platform for industry 4.0

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Campylobacter spp. in Eggs and Laying Hens in the North-East of Tunisia: High Prevalence and Multidrug-Resistance Phenotypes

A CPS-Agent self-adaptive quality control platform for industry 4.0