De Montfort University

Type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-base fuzzy logic systems. However, they are difficult to understand for a variety of reasons which we enunciate. In this paper, we strive to overcome the difficulties by: (1) establishing a small set of terms that let us easily communicate about type-2 fuzzy sets and also let us define such sets very precisely, (2) presenting a new representation for type-2 fuzzy sets, and (3) using this new representation to derive formulas for union, intersection and complement of type-2 fuzzy sets without having to use the Extension Principle.

To date, because of the computational complexity of using a general type-2 fuzzy set (T2 FS) in a T2 fuzzy logic system (FLS), most people only use an interval T2 FS, the result being an interval T2 FLS (IT2 FLS). Unfortunately, there is a heavy educational burden even to using an IT2 FLS. This burden has to do with first having to learn general T2 FS mathematics, and then specializing it to an IT2 FSs. In retrospect, we believe that requiring a person to use T2 FS mathematics represents a barrier to the use of an IT2 FLS. In this paper, we demonstrate that it is unnecessary to take the route from general T2 FS to IT2 FS, and that all of the results that are needed to implement an IT2 FLS can be obtained using T1 FS mathematics. As such, this paper is a novel tutorial that makes an IT2 FLS much more accessible to all readers of this journal. We can now develop an IT2 FLS in a much more straightforward way

The world's human population is becoming concentrated into cities, giving rise to concerns that it is becoming increasingly isolated from nature. Urban public greenspaces form the arena of many people's daily contact with nature and such contact has measurable physical and psychological benefits. Here we show that these psychological benefits increase with the species richness of urban greenspaces. Moreover, we demonstrate that greenspace users can more or less accurately perceive species richness depending on the taxonomic group in question. These results indicate that successful management of urban greenspaces should emphasize biological complexity to enhance human well-being in addition to biodiversity conservation.

Abstract It is widely recognised that public acceptability often poses a barrier towards renewable energy development. This article reviews existing research on public perceptions of wind energy, where opposition is typically characterized by the NIMBY (not in my back yard) concept. The objectives of the article are to provide a critical assessment of past research and an integrated, multidimensional framework to guide future work. Six distinct strands of research are identified, summarized and critiqued: public support for switching from conventional energy sources to wind energy; aspects of turbines associated with negative perceptions; the impact of physical proximity to turbines; acceptance over time of wind farms; NIMBYism as an explanation for negative perceptions; and, finally, the impact of local involvement on perceptions. Research across these strands is characterized by opinion poll studies of general beliefs and case studies of perceptions of specific developments. In both cases, research is fragmented and has failed to adequately explain, rather than merely describe, perceptual processes. The article argues for more theoretically informed empirical research, grounded in social science concepts and methods. A multidimensional framework is proposed that goes beyond the NIMBY label and integrates previous findings with social and environmental psychological theory. Copyright © 2004 John Wiley & Sons, Ltd.

There is mounting empirical evidence that interacting with nature delivers measurable benefits to people. Reviews of this topic have generally focused on a specific type of benefit, been limited to a single discipline, or covered the benefits delivered from a particular type of interaction. Here we construct novel typologies of the settings, interactions and potential benefits of people-nature experiences, and use these to organise an assessment of the benefits of interacting with nature. We discover that evidence for the benefits of interacting with nature is geographically biased towards high latitudes and Western societies, potentially contributing to a focus on certain types of settings and benefits. Social scientists have been the most active researchers in this field. Contributions from ecologists are few in number, perhaps hindering the identification of key ecological features of the natural environment that deliver human benefits. Although many types of benefits have been studied, benefits to physical health, cognitive performance and psychological well-being have received much more attention than the social or spiritual benefits of interacting with nature, despite the potential for important consequences arising from the latter. The evidence for most benefits is correlational, and although there are several experimental studies, little as yet is known about the mechanisms that are important for delivering these benefits. For example, we do not know which characteristics of natural settings (e.g., biodiversity, level of disturbance, proximity, accessibility) are most important for triggering a beneficial interaction, and how these characteristics vary in importance among cultures, geographic regions and socio-economic groups. These are key directions for future research if we are to design landscapes that promote high quality interactions between people and nature in a rapidly urbanising world.

Two qualitative studies in the U.K. health care sector trace eight purposefully selected innovations. Complex, contested, and nonlinear innovation careers emerged. Developing the nonlinear perspective on innovation spread further, we theorize that multi-professionalization shapes “nonspread.” Social and cognitive boundaries between different professions retard spread, as individual professionals operate within unidisciplinary communities of practice. This new theory helps explain barriers to the spread of innovation in multiprofessional organizations in both health care and other settings.

The aim of an equivalence trial is to show the therapeutic equivalence of two treatments, usually a new drug under development and an existing drug for the same disease used as a standard active comparator. Unfortunately the principles that govern the design, conduct, and analysis of equivalence trials are not as well understood as they should be. Consequently such trials often include too few patients or have intrinsic design biases which tend towards the conclusion of no difference. In addition the application of hypothesis testing in analysing and interpreting data from such trials sometimes compounds the drawing of inappropriate conclusions, and the inclusion and exclusion of patients from analysis may be poorly managed. The design of equivalence trials should mirror that of earlier successful trials of the active comparator as closely as possible. Patient losses and other deviations from the protocol should be minimised; analysis strategies to deal with unavoidable problems should not centre on an "intention to treat" analysis but should seek to show the similarity of results from a range of approaches. Analysis should be based on confidence intervals, and this also carries implications for the estimation of the required numbers of patients at the design stage.

In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, etc.). Furthermore, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes. The Edge-IIoTset dataset can be publicly accessed from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://ieee-dataport.org/8939</uri> .

Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO). Most current EMO algorithms perform well on problems with two or three objectives, but encounter difficulties in their scalability to many-objective optimization. This paper proposes a grid-based evolutionary algorithm (GrEA) to solve many-objective optimization problems. Our aim is to exploit the potential of the grid-based approach to strengthen the selection pressure toward the optimal direction while maintaining an extensive and uniform distribution among solutions. To this end, two concepts-grid dominance and grid difference-are introduced to determine the mutual relationship of individuals in a grid environment. Three grid-based criteria, i.e., grid ranking, grid crowding distance, and grid coordinate point distance, are incorporated into the fitness of individuals to distinguish them in both the mating and environmental selection processes. Moreover, a fitness adjustment strategy is developed by adaptively punishing individuals based on the neighborhood and grid dominance relations in order to avoid partial overcrowding as well as guide the search toward different directions in the archive. Six state-of-the-art EMO algorithms are selected as the peer algorithms to validate GrEA. A series of extensive experiments is conducted on 52 instances of nine test problems taken from three test suites. The experimental results show the effectiveness and competitiveness of the proposed GrEA in balancing convergence and diversity. The solution set obtained by GrEA can achieve a better coverage of the Pareto front than that obtained by other algorithms on most of the tested problems. Additionally, a parametric study reveals interesting insights of the division parameter in a grid and also indicates useful values for problems with different characteristics.

Purpose Technological disruptions such as the Internet of Things and autonomous devices, enhanced analytical capabilities (artificial intelligence) and rich media (virtual and augmented reality) are creating smart environments that are transforming industry structures, processes and practices. The purpose of this paper is to explore critical technological advancements using a value co-creation lens to provide insights into service innovations that impact ecosystems. The paper provides examples from tourism and hospitality industries as an information dependent service management context. Design/methodology/approach The research synthesizes prevailing theories of co-creation, service ecosystems, networks and technology disruption with emerging technological developments. Findings Findings highlight the need for research into service innovations in the tourism and hospitality sector at both macro-market and micro-firm levels, emanating from the rapid and radical nature of technological advancements. Specifically, the paper identifies three areas of likely future disruption in service experiences that may benefit from immediate attention: extra-sensory experiences, hyper-personalized experiences and beyond-automation experiences. Research limitations/implications Tourism and hospitality services prevail under varying levels of infrastructure, organization and cultural constraints. This paper provides an overview of potential disruptions and developments and does not delve into individual destination types and settings. This will require future work that conceptualizes and examines how stakeholders may adapt within specific contexts. Social implications Technological disruptions impact all facets of life. A comprehensive picture of developments here provides policymakers with nuanced perspectives to better prepare for impending change. Originality/value Guest experiences in tourism and hospitality by definition take place in hostile environments that are outside the safety and familiarity of one’s own surroundings. The emergence of smart environments will redefine how customers navigate their experiences. At a conceptual level, this requires a complete rethink of how stakeholders should leverage technologies, engage and reengineer services to remain competitive. The paper illustrates how technology disrupts industry structures and stimulates value co-creation at the micro and macro-societal level.

Multi‐organizational partnerships are now an important means of governing and managing public programmes. They typically involve business, community and not‐for‐profit agencies alongside government bodies. Partnerships are frequently contrasted with competitive markets and bureaucratic hierarchies. A more complex reality is revealed once partnerships as an organizational form are distinguished from networks as a mode of social co‐ordination or governance. Data from studies of UK urban regeneration partnerships are used to develop a four‐stage partnership life cycle: pre‐partnership collaboration; partnership creation; partnership programme delivery; and partnership termination. A different mode of governance ‐ network, market or hierarchy ‐ predominates at each stage. Separating organizational form from mode of governance enables a richer understanding of multi‐organizational activity and provides the basis from which theory and practice can be developed. The key challenge for partnerships lies in managing the interaction of different modes of governance, which at some points will generate competition and at other points collaboration.

Personality makes us who we are and influences every aspect of our lives – from how we interact with others, to how we respond in stressful situations. Personality Psychology uses a unique organizational framework to explore the six key domains of knowledge about personality – Dispositional, Biological, Intrapsychic, Cognitive/Experiential, Social/Cultural and Adjustment. This third edition focuses on the scientific basis of current understanding, highlighting contemporary research while also covering classic viewpoints.

A dynamic model predicting human thermal responses in cold, cool, neutral, warm, and hot environments is presented in a two-part study. This, the first paper, is concerned with aspects of the passive system: 1) modeling the human body, 2) modeling heat-transport mechanisms within the body and at its periphery, and 3) the numerical procedure. A paper in preparation will describe the active system and compare the model predictions with experimental data and the predictions by other models. Here, emphasis is given to a detailed modeling of the heat exchange with the environment: local variations of surface convection, directional radiation exchange, evaporation and moisture collection at the skin, and the nonuniformity of clothing ensembles. Other thermal effects are also modeled: the impact of activity level on work efficacy and the change of the effective radiant body area with posture. A stable and accurate hybrid numerical scheme was used to solve the set of differential equations. Predictions of the passive system model are compared with available analytic solutions for cylinders and spheres and show good agreement and stable numerical behavior even for large time steps.

The objective of this document is to promote the use of dynamic daylight performance measures for sustainable building design. The paper initially explores the shortcomings of conventional, static daylight performance metrics which concentrate on individual sky conditions, such as the common daylight factor. It then provides a review of previously suggested dynamic daylight performance metrics, discussing the capability of these metrics to lead to superior daylighting designs and their accessibility to nonsimulation experts. Several example offices are examined to demonstrate the benefit of basing design decisions on dynamic performance metrics as opposed to the daylight factor.

Over half of the world's human population lives in cities, and for many, urban greenspaces are the only places where they encounter biodiversity. This is of particular concern because there is growing evidence that human well-being is enhanced by exposure to nature. However, the specific qualities of greenspaces that offer the greatest benefits remain poorly understood. One possibility is that humans respond positively to increased levels of biodiversity. Here, we demonstrate the lack of a consistent relationship between actual plant, butterfly, and bird species richness and the psychological well-being of urban greenspace visitors. Instead, well-being shows a positive relationship with the richness that the greenspace users perceived to be present. One plausible explanation for this discrepancy, which we investigate, is that people generally have poor biodiversity-identification skills. The apparent importance of perceived species richness and the mismatch between reality and perception pose a serious challenge for aligning conservation and human well-being agendas.

Data reviewed suggest that previous theories of emotion experience are too narrow in scope and that lack of consensus is due to the fact that emotion experience takes various forms and is heterogenous. The authors treat separately the content of emotion experience, the underlying nonconscious correspondences, and processes producing emotion experience. They classify the nature and content of emotion experience and propose that it depends on 3 aspects of attention: mode (analytic-synthetic; detached-immersed), direction (self-world), and focus (evaluation-action). The account is informed by a 2-level view of consciousness in which phenomenology (1st order) is distinguished from awareness (2nd order). These distinctions enable the authors to differentiate and account for cases of "unconscious" emotion, in which there is an apparent lack of phenomenology or awareness.

It is commonly accepted that Pareto-based evolutionary multiobjective optimization (EMO) algorithms encounter difficulties in dealing with many-objective problems. In these algorithms, the ineffectiveness of the Pareto dominance relation for a high-dimensional space leads diversity maintenance mechanisms to play the leading role during the evolutionary process, while the preference of diversity maintenance mechanisms for individuals in sparse regions results in the final solutions distributed widely over the objective space but distant from the desired Pareto front. Intuitively, there are two ways to address this problem: 1) modifying the Pareto dominance relation and 2) modifying the diversity maintenance mechanism in the algorithm. In this paper, we focus on the latter and propose a shift-based density estimation (SDE) strategy. The aim of our study is to develop a general modification of density estimation in order to make Pareto-based algorithms suitable for many-objective optimization. In contrast to traditional density estimation that only involves the distribution of individuals in the population, SDE covers both the distribution and convergence information of individuals. The application of SDE in three popular Pareto-based algorithms demonstrates its usefulness in handling many-objective problems. Moreover, an extensive comparison with five state-of-the-art EMO algorithms reveals its competitiveness in balancing convergence and diversity of solutions. These findings not only show that SDE is a good alternative to tackle many-objective problems, but also present a general extension of Pareto-based algorithms in many-objective optimization.

The mixed methods approach has emerged as a ``third paradigm'' for social research. It has developed a platform of ideas and practices that are credible and distinctive and that mark the approach out as a viable alternative to quantitative and qualitative paradigms. However, there are also a number of variations and inconsistencies within the mixed methods approach that should not be ignored. This article argues the need for a vision of research paradigm that accommodates such variations and inconsistencies. It is argued that the use of ``communities of practice'' as the basis for such a research paradigm is (a) consistent with the pragmatist underpinnings of the mixed methods approach, (b) accommodates a level of diversity, and (c) has good potential for understanding the methodological choices made by those conducting mixed methods research.

Abstract Sixty British primary school children aged 9‐10 and their teachers took part in an experimental teaching programme, designed to improve the quality of children's reasoning and collaborative activity by developing their awareness of language use and promoting certain ‘ground rules’ for talking together. Children's subsequent use of language when carrying out collaborative activities in the classroom was observed and analysed, and effects on their performance on Raven's Progressive Matrices test of non‐verbal reasoning were also investigated. Comparative data were gathered from children in matched control classes. Qualitative and quantitative analyses of discourse showed a marked shift in target children's use of language in accord with the aims of the teaching programme, and demonstrated that adherence to the ground rules helped groups solve the reasoning test problems. Children's individual scores on the Raven's test also improved. These findings support a sociocultural view of intellectual development and confirm the value of explicitly teaching children how to use language to reason.

The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this success has been met by increasing model complexity and employing black-box AI models that lack transparency. In response to this need, Explainable AI (XAI) has been proposed to make AI more transparent and thus advance the adoption of AI in critical domains. Although there are several reviews of XAI topics in the literature that have identified challenges and potential research directions of XAI, these challenges and research directions are scattered. This study, hence, presents a systematic meta-survey of challenges and future research directions in XAI organized in two themes: (1) general challenges and research directions of XAI and (2) challenges and research directions of XAI based on machine learning life cycle’s phases: design, development, and deployment. We believe that our meta-survey contributes to XAI literature by providing a guide for future exploration in the XAI area.