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This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation.

A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency.

An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems.

The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described).

A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved.

For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.

This study examines the discursive accounts of civil society in a rural English village to understand what these reveal about contemporary political discourses. It employs a critical discourse analysis of the conversational interactions of Ambridge residents. The sample comprised all recorded conversations referencing charities, volunteering and civic action drawn from the two-week period corresponding with the change in UK Prime Minister (July 2019). Using three analytical tools derived from extant theory, it considers the salient political ideology underpinning these social interactions. These tools are illustrated with earlier examples of individual civil activities such as the oat-based civil disobedience of a respected older resident. This analysis scrutinises the philanthropic nature of Peggy Woolley's Ambridge Conservation Trust. The fraught process of village fete planning is cited as exemplifying conventional decision-making mechanisms. Problems of staffing a community shop are considered in the light of an increasing political reliance on community volunteers replacing paid staff. Thus, the relative impact of Thatcher, Blair, Cameron and May are considered in exchanges between Ambridge residents from Lynda and Robert Snell to Jazzer McCreery and Jill Archer. The aim is to explore what Ambridge's civil society tells us about Boris Johnson's Britain.

This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.

Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.

The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support.

Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems.

Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support.

This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making.

This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.

This piece is a review of the animal selfhood literature in sociology, organized into four main parts. First, I review the sociological literature of human–animal interactions, in which sociologists claim that animals possess selves. Second, I review how sociologists have referred to the self, from which I construct five criteria of selfhood, including self as attribution, self-awareness, intersubjectivity, self-concept/reflexivity, and narration. Third, I address how animals have selves using these criteria, drawing on sociological and ethological evidence. Fourth, I critique the animal interaction sociologists’ specific claims of animal selfhood, including their epistemological failure to distinguish between human accounts of animal subjectivities and animal subjectivities, and their empirical failure to show how animals act toward themselves. Ultimately, I conclude that animal selves, particularly in an elemental Meadian sense, are potentially real, but in most cases are unobservable or unverifiable phenomena.

The purpose of this paper is to describe the development of a comprehensive categorisation of food scares.

Following an initial desktop study, the categorisation was developed collaboratively with industry experts through a workshop and series of semi-structured interviews.

The new categorisation developed is in Venn diagram format allowing overlapping categories. It is organised around the two major types of contamination (biological, and chemical/physical contaminants) and the two major causes of contamination (wilful deception, and transparency and awareness issues).

The long and complex supply chains characteristic of current food production systems have resulted in a rising number of food scares. There is thus an increased emphasis on developing strategies to reduce both the number of incidents of food scares, and their associated economic, social and environmental impacts. The new categorisation developed in this study enables experts to address categories of food scares. Inclusion of the cause of contamination is particularly important as the method through which contamination occurs is key in devising food scare prevention strategies.

The new categorisation, unlike previous categorisations, enables food scares to fall into multiple categories, as appropriate. Also, again in contrast to previous categorisations, it takes into account not only the physical problem of a food scare but also the mechanism through which it arises.