Academic literature on the topic 'The Underworld Detection Agency series'

A series of dried blood spot (DBS) detection methods for doping agents have been developed in the last two decades. The DBS technique minimizes invasiveness and reduces storage and shipping costs. Recently, the World Anti-Doping Agency announced the use of DBS for the 2022 Beijing Winter Olympic Games and Paralympic Games owing to the advantages of the DBS application in routine doping control. Therefore the further development of detection methods for doping agents in DBS is important and urgent. This review summarizes five aspects of DBS application in doping analysis: sample collection, storage conditions, pretreatment, instrumentation and validation according to the Prohibited List issued by the World Anti-Doping Agency, and proposes some suggestions for future studies of DBS in doping analysis.

Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

Purpose: By empirically examining Zambia’s CPI between 2010 and 2020, the study attempts to determine the structural change in the time series. The CPI is one of the most important variables for analyzing inflation in macroeconomics, therefore any change in the dynamic must be determined. In this paper change points and dates are highlighted and statistical analysis methods have been employed to explore and discover the underlying patterns and trends of Zambia’s CPI for the past 10 years. Methodology/approach: Secondary Data from Zambia Statistics Agency (ZamStats.gov.zm) was used for the Study. From 132 elements of observations of time series for 10 years, the detection methods of structural change were employed. The Cumulative Sum Tests (CUSUM test) of Ordinary Least Squares (OLS), Andrew Sup F test, Bai and Perron test, and Chow test were used to detect the model stability and verify the hypothesis using P-value. Results: The results show that there were five (5) Structural changes or breaks in mean and variance and these were February 2012, February 2014, October 2015, October 2017, and May 2019. The structural breaks are highly suggestive as they appear to broadly coincide with readily identifiable macroeconomic events, increased stock of external debt following the issuance of Eurobonds in 2012, 2014, and 2015, rise increased food prices arising from the adverse impact of erratic rainfall on agricultural output and the pass-through from the depreciation of the Kwacha. Policy Implication: Based on the study, strong and sound macroeconomic policies are needed to be implemented: Such as debt management and diversification of foreign exchange sources, and increased earnings.

Abstract. In this work we present BrazilDAM, a novel public dataset based on Sentinel-2 and Landsat-8 satellite images covering all tailings dams cataloged by the Brazilian National Mining Agency (ANM). The dataset was built using georeferenced images from 769 dams, recorded between 2016 and 2019. The time series were processed in order to produce cloud free images. The dams contain mining waste from different ore categories and have highly varying shapes, areas and volumes, making BrazilDAM particularly interesting and challenging to be used in machine learning benchmarks. The original catalog contains, besides the dam coordinates, information about: the main ore, constructive method, risk category, and associated potential damage. To evaluate BrazilDAM’s predictive potential we performed classification essays using state-of-the-art deep Convolutional Neural Network (CNNs). In the experiments, we achieved an average classification accuracy of 94.11% in tailing dam binary classification task. In addition, others four setups of experiments were made using the complementary information from the original catalog, exhaustively exploiting the capacity of the proposed dataset.

Abstract. In the last decades, the Brazilian Cerrado biome has undergone major transformations due to the expansion of the agricultural frontier. The region called MATOPIBA acronym for states Maranhão, Tocantins, Piauí, and Bahia can be considered very attractive for agricultural expansion. The Cerrado predominates in the MATOPIBA region (91% of the area), also having small areas of the Amazon and Caatinga biomes to the northeast and east, respectively. In this work, we will present a study to identify center pivot irrigation systems in the MATOPIBA region using remote sensing images from Landsat-8 satellite. The methodology is based on the use of robust edge detection techniques such as Canny, Circular Hough Transform (CHT) and time series extraction through the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13Q1 which has two vegetation indices NDVI and EVI. These time series will be used to filter the detected circles, seeking to eliminate the circles that do not correspond to center pivots. Our approach detected 80% of the center pivots mapped by the Brazilian National Water Agency (ANA) used as a knowledge base. The states with better detection were Piauí and Bahia that showed the accuracy of 90% and 85% respectively, Maranhão obtained 57% and Tocantins 41%.

This study developed the European Space Agency (ESA) Setinel-1 Ground Range Detected (GRD) time series analysis model for monitoring floating debris in lake areas through Google Earth Engine Application Programming Interface. The study aims to monitor floating debris caused by heavy rainfall efficiently. Regarding water resources and water quality management, floating debris from multipurpose dams requires continuous monitoring from the initial generation stage. In the study, a Synthetic Aperture Radar (SAR) time series analysis model that is easy to identify water bodies was developed due to low accessibility in large areas. Although SAR satellite images could be used to observe inland water environments, debris detection on water surface surfaces has yet to be studied. For the first time, this study detected floating debris patches in a wide range of lakes from GRD imagery acquired by ESA’s Sentinel-1 satellite. It demonstrated the potential to distinguish them from naturally occurring materials such as invasive floating plants. In this study, the case of Daecheong Dam, in which predicted floating debris was detected after heavy rain using Sentinel-1 GRD data, is presented. It could quickly detect various floating debris flowing into dams used as a source of drinking water and serve as a reference for establishing a collection plan.

This article explores the convergence, inversion, and collapse of heterotopic spaces in E. S. Thomson’s neo-Victorian Jem Flockhart series about a cross-dressing female apothecary in mid-nineteenth-century London. The eponymous first-person narrator becomes embroiled in the detection of horrific murder cases, with the action traversing a wide range of Michel Foucault’s exemplary Other spaces, including hospitals, graveyards, brothels, prisons, asylums, and colonies, with the series substituting the garden for Foucault’s ship as the paradigmatic heterotopia. These myriad juxtaposed sites, which facilitate divergence from societal norms while seemingly sequestering forms of alterity and resistance, repeatedly merge into one another in Thomson’s novels, destabilising distinct kinds of heterotopias and heterotopic functions. Jem’s doubled queerness as a cross-dressing lesbian beloved by their Watsonean side-kick, the junior architect William Quartermain, complicates the protagonist’s role in helping readers negotiate the re-imagined Victorian metropolis and its unequal power structures. Simultaneously defending/reaffirming and contesting/subverting the status quo, Jem’s body itself becomes a microcosmic heterotopia, problematising the elision of agency in Foucault’s conceptualisation of the term. The proliferation of heterotopias in Thomson’s series suggests that neo-Victorian fiction reconfigures the nineteenth century into a vast network of confining, contested, and liberating Other spaces.

The amount of freely available satellite data is growing rapidly as a result of Earth observation programmes, such as Copernicus, an initiative of the European Space Agency. Analysing these huge amounts of geospatial data and extracting useful information is an ongoing pursuit. This paper presents an alternative method for flood detection based on the description of spatio-temporal dynamics in satellite image time series (SITS). Since synthetic aperture radar (SAR) satellite data has the capability of capturing images day and night, irrespective of weather conditions, it is the preferred tool for flood mapping from space. An object-based approach can limit the necessary computer power and computation time, while a graph-based approach allows for a comprehensible interpretation of dynamics. This method proves to be a useful tool to gain insight in a flood event. Graph representation helps to identify and locate entities within the study site and describe their evolution throughout the time series.

In the framework of the Terrafirma Extension project, a study has been established for ground motion detection and mapping in the Anthemountas basin (Central Macedonia). Terrafirma promotes the exploitation of Persistent Scatterer Interferometry (PSI) techniques, a remote sensing technology based on multi-temporal satellite Synthetic Aperture Radar (SAR) imagery. The potential of SAR interferometry has been exploited through the innovative Wide Area Mapping approach, recently implemented by the German Space Agency (DLR) and aimed at measuring land deformation over large areas. Interferometric results from 1995-2001 by ERS1/2 satellites has been analyzed at a basin scale to investigate spatial patterns of land motion in the wider Anthemountas plain, where subsidence phenomena related to intense groundwater extraction is clearly manifested. The WAP results turned out to be a valuable tool for the characterization at local scale of the land subsidence in the runaways area of the Macedonia airport and in the village of Perea, affected in 2005-2006 by a series of tensile ground ruptures due to excessive groundwater withdrawal. Besides the study of the phenomenon, this work confirmed the suitability of PSI techniques to detect and measure surface displacements with millimetre accuracy and also to reconstruct the deformations history of the investigated areas through displacement time series analysis.

Abstract Recently, ovarian cancer research has evolved considerably because of the emerging recognition that rather than a single disease, ovarian carcinomas comprise several different histotypes that vary by etiologic origin, risk factors, molecular profiles, therapeutic approaches and clinical outcome. Despite significant progress in our understanding of the etiologic heterogeneity of ovarian cancer, as well as important clinical advances, it remains the eighth most frequently diagnosed cancer in women worldwide and the most fatal gynecologic cancer. The International Agency for Research on Cancer and the United States National Cancer Institute jointly convened an expert panel on ovarian carcinoma to develop consensus research priorities based on evolving scientific discoveries. Expertise ranged from etiology, prevention, early detection, pathology, model systems, molecular characterization and treatment/clinical management. This report summarizes the current state of knowledge and highlights expert consensus on future directions to continue advancing etiologic, epidemiologic and prognostic research on ovarian carcinoma.

Abstract The term ‘supergrass ‘ was first coined by journalists in the early 1970s to describe those ‘grasses ‘ (informers) from the London underworld who testified against their alleged former associates in a series of high-profile mass trials ‘:-a modern instance of the age old use of informants for the purpose of social control. It should be noted at the outset of this study, however, that ‘police inform ants ‘ come in many forms, that the information they supply can be used by crime-control systems in a variety of ways, and that together these variables open up a range of opportunities and dilemmas, particularly for law enforcement professing committment to due process and the rule of law. There are two main difficulties. The first concerns the extent to which informants genuinely assist in the prevention, detection, and punishment of crime, and how this contribution can be measured. It should be stressed that this is not the same question as the extent to which the police consider certain types helpful in their investigations.

Abstract Henry, Lord Neville, does not seem to have been a bookish young man. That was unlucky, because during the same winter that he was being defrauded and bamboozled by Gregory Wisdom, a book was published which could have solved his gambling problems more effectively than any magician. It was the first of a series of books and pamphlets written over the next fifty or more years which offer a glimpse into London’s criminal underworld, and to how it went about parting fools from their money. The book, apparently the work of one Gilbert Walker, was called A manifest detection of the most vyle and detestable vse of Diceplay. It is as instructive to us as it would have been to Lord Henry, for it provides a unique glimpse into another of the worlds where Gregory Wisdom lived and worked: the networks of fraud and crime which pervaded Tudor London.

This chapter examines E. Nesbit’s The Story of the Amulet (1906), C. S. Lewis’s The Silver Chair (1953), Roger Lancelyn Green’s Mystery at Mycenae (1957), Caroline Lawrence’s Roman Mysteries series (2001–9), K. M. Peyton’s Roman Pony trilogy (2007–9), Katherine Marsh’s The Night Tourist (2007) and The Twilight Prisoner (2009), and Tony Abbott’s Underworlds series (2011–12). All these texts involve journeys that can be plotted upon maps geographical and/or chronological, with the consequence that the major cognitive metaphor is HISTORY IS A MAP. Here family is not the site of trauma but rather a zone for the exercise of agency on the part of the young protagonist who must effectively visit an underworld to retrieve or make a family relationship and to come to terms with death. These books suggest that while the past is associated with death, it is also a haven from death.

This article describes how sequential data modeling is a relevant task in Cybersecurity. Sequences are attributed temporal characteristics either explicitly or implicitly. Recurrent neural networks (RNNs) are a subset of artificial neural networks (ANNs) which have appeared as a powerful, principle approach to learn dynamic temporal behaviors in an arbitrary length of large-scale sequence data. Furthermore, stacked recurrent neural networks (S-RNNs) have the potential to learn complex temporal behaviors quickly, including sparse representations. To leverage this, the authors model network traffic as a time series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with a supervised learning method, using millions of known good and bad network connections. To find out the best architecture, the authors complete a comprehensive review of various RNN architectures with its network parameters and network structures. Ideally, as a test bed, they use the existing benchmark Defense Advanced Research Projects Agency / Knowledge Discovery and Data Mining (DARPA) / (KDD) Cup ‘99' intrusion detection (ID) contest data set to show the efficacy of these various RNN architectures. All the experiments of deep learning architectures are run up to 1000 epochs with a learning rate in the range [0.01-0.5] on a GPU-enabled TensorFlow and experiments of traditional machine learning algorithms are done using Scikit-learn. Experiments of families of RNN architecture achieved a low false positive rate in comparison to the traditional machine learning classifiers. The primary reason is that RNN architectures are able to store information for long-term dependencies over time-lags and to adjust with successive connection sequence information. In addition, the effectiveness of RNN architectures are shown for the UNSW-NB15 data set.

The chapter examines Robin Stevens’s Murder Most Unladylike Mystery series, arguing that subtextual concerns of the English interwar detective fiction and boarding school stories, namely those of national, racial, and sexual Others intruding into traditional English places, are foregrounded, offering a lens on the exclusionary and containing structures and spaces of English society. The novels inscribe the reassuring geographies and interior spaces of the boarding school novel with the anxieties of detective fiction, allowing the protagonists an escape from identities of class, ethnicity and childhood through the assertion of an identity as a detective. The essay reveals how detection offers the protagonists agency in contrast to the performative identity they take on as schoolgirls. In other words, the girls perform the identity of ‘schoolgirl’; their real identity is ‘detective’.

Researchers interested in remote locations have developed monitoring schemes, sometimes called “Watchful Eye” monitoring, that use a time series of remotely sensed images to assess changes over time to a protected area or habitat. For instance, the European Space Agency (ESA) and UNESCO have set up repeat analyses of satellite imagery for World Heritage sites. The first area for which they developed this technique was the habitat of the critically endangered mountain gorilla (Gorilla berengei berengei) in the Virunga Mountains in Central Africa, including the Bwindi and Mgahinga National Parks in Uganda, the Virunga and Kahuzi-Biega National Parks in the Democratic Republic of Congo, and the trans-boundary Volcanoes Conservation Area. The project developed detailed maps of these inaccessible zones so that protected area managers can monitor the gorilla habitat. Previously, available maps were old and inaccurate (at times handmade), did not completely cover the range of the gorillas, and did not cross national boundaries. Because there was no systematic information from the ground regarding changes over time, researchers also used remotely sensed data to complete change detection analyses over the past two decades. Using both optical (Landsat series) and radar (ENVISAT ASAR) satellite data, researchers were able to quantify rates of deforestation between 1990 and 2003 and relate these rates to human migration rates into the area resulting from regional political instability. Researchers constructed the first digital base maps of the areas, digital elevation models (DEMs), and updated vegetation and land use maps. They faced significant problems in both field and laboratory activities, including lack of existing ground data, dense vegetation cover, and fairly continuous cloud cover. They therefore used a combination of ESA ENVISAT ASAR as well as Landsat and ESA Medium Resolution Imaging Spectrometer (MERIS) optical data. The radar images allowed them to quantify elevation and distances between trees and homes. Landsat and MERIS data helped identify forest cover types, with Landsat providing finer-scale images at less frequent intervals and MERIS serving lower-resolution images more frequently.

This book describes a hypothetical process in which populations of protocells can spontaneously assemble and begin to grow and proliferate by energy- dependent polymerization. This might seem to be just an academic question pursued by a few dozen researchers as a matter of curiosity, but in the past three decades advances in engineering have reached a point where both NASA and the European Space Agency (ESA) routinely send spacecraft to other planetary objects in our solar system. A major question being pursued is whether life has emerged elsewhere than on Earth. The limited funds available to support such missions require decisions to be made about target priorities that are guided by judgment calls. These in turn depend on plausible scenarios related to the origin of life on habitable planetary surfaces. We know that other planetary bodies in our solar system have had or do have conditions that would permit microbial life to exist and perhaps even to begin. By a remarkable coincidence, the two most promising objects for extraterrestrial life happen to represent the two alternative scenarios described in this book: An origin of life in conditions of hydrothermal vents or an origin in hydrothermal fields. This final chapter will explore how these alternative views can guide our judgment about where to send future space missions designed as life-detection missions. Questions to be addressed: What is meant by habitability? Which planetary bodies are plausible sites for the origin of life? How do the hypotheses described in this book relate to those sites? There is healthy public interest in how life begins and whether it exists elsewhere in our solar system or on the myriad exoplanets now known to orbit other stars. This has fueled a series of films, television programs, and science fiction novels. Most of these feature extrapolations to intelligent life but a few, such as The Andromeda Strain, explore what might happen if a pathogenic organism from space began to spread to the human population. There is a serious and sustained scientific effort—SETI, or Search for Extraterrestrial Intelligence—devoted to finding an answer to this question.

An urgent lesson learned from Fukushima Daiichi accident is what can happen by natural disaster can also occur by human design. The accident raised a fear that terrorists could cause a similar accident by acts of sabotage against nuclear power plant (NPP) and it is noticeable that threats of terrorism for nuclear security are increased after the accident. When considering sabotage, the prime threat to nuclear power plants, due attention should be paid to sabotage by insiders. Generally, insiders are the individuals with authorized access to nuclear facilities in transport who could attempt unauthorized sabotage. They could take advantage of their access authority and knowledge, to bypass dedicated physical protection elements or other provisions [1]. Thus, we should value the catastrophic consequences of the attack or act of insider sabotage which may lead to loss of safety functions of NPP. International Atomic Energy Agency (IAEA) indicated that the physical protection system (PPS) of a nuclear facility should be integrated and effective against both sabotage and unauthorized removal. The primary PPS functions are deterrence, detection, delay and response. It is noticeable that if detection failed, delay and response would become invalid. Thus, detection of insiders’ sabotage should be enhanced. Considering current countermeasures of PPS to insiders’ sabotage, the most significant challenge is how to distinguish ordinary maintenance behaviors and malicious behaviors since some malicious behaviors may hidden in ordinary maintenance behaviors. It appears that hand behavior has high contribution to human activity and a significant portion of maintenance behaviors and malicious behaviors. In this study, we proposed a hand behavior detection algorithm for insiders’ malicious behaviors for nuclear security [2]. We focused on the fact that the hand shape is uniquely determined by the fingertip coordinates. First, the depth image of the hand was captured with Kinect v2, and after removing the five fingers were remained by removing the palm and wrist parts, and the five fingers were identified using the K-means clustering [3], and the farthest point of each finger from wrist pixel was taken as the fingertip coordinates. The fingertip coordinates of the five fingers were combined for 60 frames to be time-series data, and this was used as the training data of the neural network. Time-series data obtained from five kinds of behaviors of five hands was used for training data. For the machine learning method, the Stacked-Auto Encoder (SAE) [4–5] which is one of popular methods was used. It extracts the feature of input data at intermediate layer of the first stage. In the second layer, the extracted feature is input and its feature is extracted to be used as the input of the softmax layer for pattern classification. Meanwhile, a real-time fingertip tracking system was developed and time-series data of each fingertip was successfully obtained with 29.8fps using MATLAB whose CPU was Intel Xeon Processor E5-2630v4 (25M Cache, 2.20 GHz). Moreover, a time-series data analysis based behavior recognition method was developed and all assumed malicious behaviors were detected with high accuracy (82.555% in overall) and speed (0.0023 seconds per frame) in the same computing environment. Also, robustness of the behavior recognition method was verified.

On the sites of Belgoprocess several thousands of drums containing conditioned legacy waste are stored. A significant number of these waste packages are 220 litre drums containing radioactive waste embedded into inactive bitumen. Most of the radioactive waste in these drums was generated during the development and production of MOX-fuels and the operation of the Eurochemic reprocessing plant. The current state of a number of these packages is no longer acceptable for long term storage. In order to make the waste packages acceptable for interim storage a repackaging process was developed. The process involves the repackaging of the waste items into 400 or 700 litre waste drums and a non-destructive gamma-ray assay (NDA) measurement performed on the new package. The aim of the NDA measurement is to detect significant quantities of fissile material in order to demonstrate compliance with the operational limits of the storage building. Since the waste items are destined for geological disposal, there is no specific need for a detection limit in the order of milligrams of plutonium as required for surface disposal. To meet this NDA requirement Babcock International Group supplied, calibrated and commissioned an open geometry system from its HRGS product range. The DrumScan® HRGS Solo assay system was delivered to the Belgoprocess site in 2009 after completing a series of factory acceptance tests performed in the UK. In May 2009 after successful completion of the site acceptance tests performed in Belgium, the system has been undergoing extensive testing and validation by Belgoprocess in order to demonstrate acceptance and compliance to the Belgian Radioactive Waste Agency, NIRAS/ONDRAF. After a careful evaluation of the qualification file, NIRAS/ONDRAF approved the system for operational measurements at the end of 2010. This paper provides a detailed description of the NDA requirement, calibration methodology, system validation tests and overall measurement performance of the system.

Campylobacter spp. are the most common bacterial cause of foodborne illness in the UK, with chicken considered to be the most important vehicle for this organism. The UK Food Standards Agency (FSA) agreed with industry to reduce Campylobacter spp. contamination in raw chicken and issued a target to reduce the prevalence of the most contaminated chickens (those with more than 1000 cfu per g chicken neck skin) to below 10 % at the end of the slaughter process, initially by 2016. To help monitor progress, a series of UK-wide surveys were undertaken to determine the levels of Campylobacter spp. on whole UK-produced, fresh chicken at retail sale in the UK. The data obtained for the first four years was reported in FSA projects FS241044 (2014/15) and FS102121 (2015 to 2018). The FSA has indicated that the retail proxy target for the percentage of highly contaminated raw whole retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target. This report presents results from testing chickens from non-major retailer stores (only) in a fifth survey year from 2018 to 2019. In line with previous practise, samples were collected from stores distributed throughout the UK (in proportion to the population size of each country). Testing was performed by two laboratories - a Public Health England (PHE) laboratory or the Agri-Food & Biosciences Institute (AFBI), Belfast. Enumeration of Campylobacter spp. was performed using the ISO 10272-2 standard enumeration method applied with a detection limit of 10 colony forming units (cfu) per gram (g) of neck skin. Antimicrobial resistance (AMR) to selected antimicrobials in accordance with those advised in the EU harmonised monitoring protocol was predicted from genome sequence data in Campylobacter jejuni and Campylobacter coli isolates The percentage (10.8%) of fresh, whole chicken at retail sale in stores of smaller chains (for example, Iceland, McColl’s, Budgens, Nisa, Costcutter, One Stop), independents and butchers (collectively referred to as non-major retailer stores in this report) in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. has decreased since the previous survey year but is still higher than that found in samples from major retailers. 8 whole fresh raw chickens from non-major retailer stores were collected from August 2018 to July 2019 (n = 1009). Campylobacter spp. were detected in 55.8% of the chicken skin samples obtained from non-major retailer shops, and 10.8% of the samples had counts above 1000 cfu per g chicken skin. Comparison among production plant approval codes showed significant differences of the percentages of chicken samples with more than 1000 cfu per g, ranging from 0% to 28.1%. The percentage of samples with more than 1000 cfu of Campylobacter spp. per g was significantly higher in the period May, June and July than in the period November to April. The percentage of highly contaminated samples was significantly higher for samples taken from larger compared to smaller chickens. There was no statistical difference in the percentage of highly contaminated samples between those obtained from chicken reared with access to range (for example, free-range and organic birds) and those reared under standard regime (for example, no access to range) but the small sample size for organic and to a lesser extent free-range chickens, may have limited the ability to detect important differences should they exist. Campylobacter species was determined for isolates from 93.4% of the positive samples. C. jejuni was isolated from the majority (72.6%) of samples while C. coli was identified in 22.1% of samples. A combination of both species was found in 5.3% of samples. C. coli was more frequently isolated from samples obtained from chicken reared with access to range in comparison to those reared as standard birds. C. jejuni was less prevalent during the summer months of June, July and August compared to the remaining months of the year. Resistance to ciprofloxacin (fluoroquinolone), erythromycin (macrolide), tetracycline, (tetracyclines), gentamicin and streptomycin (aminoglycosides) was predicted from WGS data by the detection of known antimicrobial resistance determinants. Resistance to ciprofloxacin was detected in 185 (51.7%) isolates of C. jejuni and 49 (42.1%) isolates of C. coli; while 220 (61.1%) isolates of C. jejuni and 73 (62.9%) isolates of C. coli isolates were resistant to tetracycline. Three C. coli (2.6%) but none of the C. jejuni isolates harboured 23S mutations predicting reduced susceptibility to erythromycin. Multidrug resistance (MDR), defined as harbouring genetic determinants for resistance to at least three unrelated antimicrobial classes, was found in 10 (8.6%) C. coli isolates but not in any C. jejuni isolates. Co-resistance to ciprofloxacin and erythromycin was predicted in 1.7% of C. coli isolates. 9 Overall, the percentages of isolates with genetic AMR determinants found in this study were similar to those reported in the previous survey year (August 2016 to July 2017) where testing was based on phenotypic break-point testing. Multi-drug resistance was similar to that found in the previous survey years. It is recommended that trends in AMR in Campylobacter spp. isolates from retail chickens continue to be monitored to realise any increasing resistance of concern, particulary to erythromycin (macrolide). Considering that the percentage of fresh, whole chicken from non-major retailer stores in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. continues to be above that in samples from major retailers more action including consideration of interventions such as improved biosecurity and slaughterhouse measures is needed to achieve better control of Campylobacter spp. for this section of the industry. The FSA has indicated that the retail proxy target for the percentage of highly contaminated retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target.