University of Derby

Abstract A colour‐difference equation based on CIELAB is developed. It includes not only lightness, chroma, and hue weighting functions, but also an interactive term between chroma and hue differences for improving the performance for blue colours and a scaling factor for CIELAB a * scale for improving the performance for gray colours. Four reliable colour discrimination datasets based upon object colours were accumulated and combined. The equation was tested together with the other advanced CIELAB based equations using the combined dataset and each individual dataset. It outperformed CMC and CIE94 by a large margin, and predicted better than BFD and LCD. The equation has been officially adopted as the new CIE colour‐difference equation. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 340–350, 2001

General circulation models (GCMs) suggest that rising concentrations of greenhouse gases may have significant consequences for the global climate. What is less clear is the extent to which local (subgrid) scale meteorological processes will be affected. So-called 'downscaling' techniques have subsequently emerged as a means of bridging the gap between what climate modellers are currently able to provide and what impact assessors require. This article reviews the present generation of downscaling tools under four main headings: regression methods; weather pattern (circulation)-based approaches; stochastic weather generators; and limited-area climate models. The penultimate section summarizes the results of an international experiment to intercompare several precipitation models used for downscaling. It shows that circulation-based downscaling methods perform well in simulating present observed and model-generated daily precipitation characteristics, but are able to capture only part of the daily precipitation variability changes associated with model-derived changes in climate. The final section examines a number of ongoing challenges to the future development of climate downscaling.

Abstract In the current context of the global pandemic of coronavirus disease-2019 (COVID-19), health professionals are working with social scientists to inform government policy on how to slow the spread of the virus. An increasing amount of social scientific research has looked at the role of public message framing, for instance, but few studies have thus far examined the role of individual differences in emotional and personality-based variables in predicting virus-mitigating behaviors. In this study, we recruited a large international community sample ( N = 324) to complete measures of self-perceived risk of contracting COVID-19, fear of the virus, moral foundations, political orientation, and behavior change in response to the pandemic. Consistently, the only predictor of positive behavior change (e.g., social distancing, improved hand hygiene) was fear of COVID-19, with no effect of politically relevant variables. We discuss these data in relation to the potentially functional nature of fear in global health crises.

Abstract The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

This article reviews the historical development of statistical weather models, from simple analyses of runs of consecutive rainy and dry days at single sites, through to multisite models of daily precipitation. Weather generators have been used extensively in water engineering design and in agricultural, ecosystem and hydrological impact studies as a means of in-filling missing data or for producing indefinitely long synthetic weather series from finite station records. We begin by describing the statistical properties of the rainfall occurrence and amount processes which are necessary precursors to the simulation of other (dependent) meteorological variables. The relationship between these daily weather models and lower-frequency variations in climate statistics is considered next, noting that conventional weather generator techniques often fail to capture wholly interannual variability. Possible solutions to this deficiency - such as the use of mixtures of slowly and rapidly varying conditioning variables - are discussed. Common applications of weather generators are then described. These include the modelling of climate-sensitive systems, the simulation of missing weather data and statistical downscaling of regional climate change scenarios. Finally, we conclude by considering ongoing advances in the simulation of spatially correlated weather series at multiple sites, the downscaling of interannual climate variability and the scope for using nonparametric techniques to synthesize weather series.

Purpose Employability concerns the extent to which people possess the skills and other attributes to find and stay in work of the kind they want. It is thought by many to be a key goal for individuals to aim for in managing their careers, and for organisations to foster in workforces. The purpose of this paper is to report on the development of a self‐report measure of individuals' perceived employability. It also seeks to examine its construct validity and correlates. Design/methodology/approach Based on the analysis of relevant literature, this study developed 16 items which were intended collectively to reflect employability within and outside the person's current organisation, based on his or her personal and occupational attributes. This study administered these items by questionnaire to 200 human resources professionals in the UK, along with established measures of career success and professional commitment, as well as questions reflecting demographic variables. Findings This article retained 11 of the 16 items for assessing self‐perceived employability. Concludes that self‐perceived employability can usefully be thought of as either a unitary construct, or one with two related components – internal (to the organisation) and external employability. The measure very successfully distinguished employability from professional commitment, and fairly successfully from career success. Only slight variations in employability could be attributed to demographic characteristics. Research limitations/implications This research has begun to address the gap in the literature for a brief yet psychometrically adequate measure of self‐perceived individual employability. Practical implications This author believes that the scale can be applied to other occupational groups, in organisational consultancy, and in individual career development. It can be used either as one scale or two, depending on the purpose of the investigation. Originality/value Concludes that this research represents a psychometrically adequate contribution in an under‐researched field, and will lead to future research with other occupational samples, and in other settings.

Purpose The purpose of this paper is to explore how rising technologies from Industry 4.0 can be integrated with circular economy (CE) practices to establish a business model that reuses and recycles wasted material such as scrap metal or e-waste. Design/methodology/approach The qualitative research method was deployed in three stages. Stage 1 was a literature review of concepts, successful factors and barriers related to the transition towards a CE along with sustainable supply chain management, smart production systems and additive manufacturing (AM). Stage 2 comprised a conceptual framework to integrate and evaluate the synergistic potential among these concepts. Finally, stage 3 validated the proposed model by collecting rich qualitative data based on semi-structured interviews with managers, researchers and professors of operations management to gather insightful and relevant information. Findings The outcome of the study is the recommendation of a circular model to reuse scrap electronic devices, integrating web technologies, reverse logistics and AM to support CE practices. Results suggest a positive influence from improving business sustainability by reinserting waste into the supply chain to manufacture products on demand. Research limitations/implications The impact of reusing wasted materials to manufacture new products is relevant to minimising resource consumption and negative environmental impacts. Furthermore, it avoids hazardous materials ending up in landfills or in the oceans, seriously threatening life in ecosystems. In addition, reuse of wasted material enables the development of local business networks that generate jobs and improve economic performance. Practical implications First, the impact of reusing materials to manufacture new products minimises resource consumption and negative environmental impacts. The circular model also encourages keeping hazardous materials that seriously threaten life in ecosystems out of landfills and oceans. For this study, it was found that most urban waste is plastic and cast iron, leaving room for improvement in increasing recycling of scrap metal and similar materials. Second, the circular business model promotes a culture of reusing and recycling and motivates the development of collection and processing techniques for urban waste through the use of three-dimensional (3D) printing technologies and Industry 4.0. In this way, the involved stakeholders are focused on the technical parts of recycling and can be better dedicated to research, development and innovation because many of the processes will be automated. Social implications The purpose of this study was to explore how Industry 4.0 technologies are integrated with CE practices. This allows for the proposal of a circular business model for recycling waste and delivering new products, significantly reducing resource consumption and optimising natural resources. In a first stage, the circular business model can be used to recycle electronic scrap, with the proposed integration of web technologies, reverse logistics and AM as a technological platform to support the model. These have several environmental, sociotechnical and economic implications for society. Originality/value The sociotechnical aspects are directly impacted by the circular smart production system (CSPS) management model, since it creates a new culture of reuse and recycling techniques for urban waste using 3D printing technologies, as well as Industry 4.0 concepts to increase production on demand and automate manufacturing processes. The tendency of the CSPS model is to contribute to deployment CE in the manufacture of new products or parts with AM approaches, generating a new path of supply and demand for society.

OBJECTIVES: Self-critical people, compared with those who self-reassure, are at increased risk of psychopathology. However, there has been little work on the different forms and functions of these self-experiences. This study developed two self-report scales to measure forms and functions of self-criticism and self-reassurance and explore their relationship to depression. METHODS: A self-report scale measuring forms of self-criticism and self-reassuring, and a scale measuring possible functions of self-criticism, together with a measure of depression and another self-criticism scale (LOSC), were given to 246 female students. RESULTS: Self-criticizing vs. self-reassuring separated into two components. Forms of self-criticizing separated into two components related to: being self-critical, dwelling on mistakes and sense of inadequacy; and a second component of wanting to hurt the self and feeling self-disgust/hate. The reasons/functions for self-criticism separated into two components. One was related to desires to try to self-improve (called self-improving/correction), and the other to take revenge on, harm or hurt the self for failures (called self-harming/persecuting). Mediation analysis suggested that wanting to harm the self may be particularly pathogenic and is positively mediated by the effects of hating the self and negatively mediated by being able to self-reassure and focus on one's positives. CONCLUSIONS: Self-criticism is not a single process but has different forms, functions, and underpinning emotions. This indicates a need for more detailed research into the variations of self-criticism and the mechanisms for developing self-reassurance.

This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conventional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.

ABSTRACT: Simulated daily precipitation, temperature, and runoff time series were compared in three mountainous basins in the United States: (1) the Animas River basin in Colorado, (2) the East Fork of the Carson River basin in Nevada and California, and (3) the Cle Elum River basin in Washington State. Two methods of climate scenario generation were compared: delta change and statistical downscaling. The delta change method uses differences between simulated current and future climate conditions from the Hadley Centre for Climate Prediction and Research (HadCM2) General Circulation Model (GCM) added to observed time series of climate variables. A statistical downscaling (SDS) model was developed for each basin using station data and output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEPINCAR) reanalysis regridded to the scale of HadCM2. The SDS model was then used to simulate local climate variables using HadCM2 output for current and future conditions. Surface climate variables from each scenario were used in a precipitation‐runoff model. Results from this study show that, in the basins tested, a precipitation‐runoff model can simulate realistic runoff series for current conditions using statistically down‐scaled NCEP output. But, use of downscaled HadCM2 output for current or future climate assessments are questionable because the GCM does not produce accurate estimates of the surface variables needed for runoff in these regions. Given the uncertainties in the GCMs ability to simulate current conditions based on either the delta change or downscaling approaches, future climate assessments based on either of these approaches must be treated with caution.

This paper provides a discussion of the development and application of Artificial Neural Networks (ANNs) to flow forecasting in two flood-prone UK catchments using real hydrometric data.Given relatively brief calibration data sets it was possible to construct robust models of 15-min flows with six hour lead times for the Rivers Amber and Mole.Comparisons were made between the performance of the ANN and those of conventional flood forecasting systems.The results obtained for validation forecasts were of comparable quality to those obtained from operational systems for the River Amber.The ability of the ANN to cope with missing data and to "learn" from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models.However, further research is required to determine the optimum ANN training period for a given catchment, season and hydrological contexts.Une approche de la modlisation pluie-deblt par ies rseaux neuronaux artificiels Rsum Ce document traite du dveloppement et de l'application des rseaux neuronaux artificiels (RNA) la prvision des dbits de deux bassins versants du Royaume Uni sujets aux inondations grce l'utilisation de donnes hydromtriques relles.Partant d'un ensemble restreint de donnes d'apprentissage, il a t possible de raliser des modles pour la prvision des dbits au pas de temps de 15 min chance de 6 heures pour les rivires Amber et Mole.On a compar les performances des RNA et des systmes conventionnels d'annonce de crue.Les rsultats obtenus lors de la validation des prvisions des RNA taient de qualit comparable ceux obtenus par les systmes actuellement utiliss oprationnellement sur la Rivire Amber.La capacit des RNA grer les donnes manquantes et "apprendre" en temps rel partir de l'vnement en cours, fait de ces outils une alternative sduisante aux actuels modles de prvision agrgs ou semi-distribus.De plus amples recherches sont cependant ncessaires pour dterminer la priode d'apprentissage optimale des RNA pour un bassin donn et selon le contexte climatique et hydrologique.

Nature connectedness relates to an individual’s subjective sense of their relationship with the natural world. A recent meta-analysis has found that people who are more connected to nature also tend to have higher levels of self-reported hedonic well-being; however, no reviews have focussed on nature connection and eudaimonic well-being. This meta-analysis was undertaken to explore the relationship of nature connection with eudaimonic well-being and to test the hypothesis that this relationship is stronger than that of nature connection and hedonic well-being. From 20 samples (n = 4758), a small significant effect size was found for the relationship of nature connection and eudaimonic well-being (r = 0.24); there was no significant difference between this and the effect size (from 30 samples n = 11,638) for hedonic well-being (r = 0.20). Of the eudaimonic well-being subscales, personal growth had a moderate effect size which was significantly larger than the effect sizes for autonomy, purpose in life/meaning, self-acceptance, positive relations with others and environmental mastery, but not vitality. Thus, individuals who are more connected to nature tend to have greater eudaimonic well-being, and in particular have higher levels of self-reported personal growth.

The Internet of Things is a novel cutting edge technology that proffers to connect a plethora of digital devices endowed with several sensing, actuation, and computing capabilities with the Internet, thus offering manifold new services in the context of a smart city. The appealing IoT services and big data analytics are enabling smart city initiatives all over the world. These services are transforming cities by improving infrastructure and transportation systems, reducing traffic congestion, providing waste management, and improving the quality of human life. In this article, we devise a taxonomy to best bring forth a generic overview of the IoT paradigm for smart cities, integrated ICT, network types, possible opportunities and major requirements. Moreover, an overview of the up-to-date efforts from standard bodies is presented. Later, we give an overview of existing open source IoT platforms for realizing smart city applications followed by several exemplary case studies. In addition, we summarize the latest synergies and initiatives worldwide taken to promote IoT in the context of smart cities. Finally, we highlight several challenges in order to give future research directions.

ABSTRACT Nuptial feeding encompasses any form of nutrient transfer from the male to the female during or directly after courtship and/or copulation. In insects, nuptial gifts may take the form of food captured or collected by the male, parts, or even the whole of the male's body, or glandular products of the male such as salivary secretions, external glandular secretions, the spermatophore and substances in the ejaculate. Over the past decade, there has been considerable debate over the current function of nuptial feeding in insects. This debate has centred on the issue of whether nuptial gifts function as paternal investment (i.e. function to increase the fitness and/or number of the gift‐giving male's own offspring) or as mating effort (i.e. function to attract females, facilitate coupling, and/or to maximize ejaculate transfer), although the two hypotheses are not mutually exclusive. In the present article, evidence for the potential of nuptial gifts to function as either paternal investment, mating effort, or both is reviewed for each form of nuptial feeding in each insect taxon for which sufficient data are available. Empirical evidence suggests that many diverse forms of nuptial feeding in different insect taxa function, at least in part, as mating effort. For example, nuptial prey and salivary masses in the Mecoptera, regurgitated food in Drosophila (Diptera), hind‐wing feeding in Cyphoderris (Orthoptera) and the secretion of the male's cephalic gland in Neopyrochroa (Coleoptera) and Zorotypus (Zoraptera) appear to function to entice females to copulate and/or to facilitate coupling. Nuptial prey and salivary masses in the Mecoptera also appear to function to maximize ejaculate transfer (which is also a form of mating effort), as do nuptial prey in Empis (Diptera), external glandular secretions in Oecanthus and Allonemobius (Orthoptera) and the spermatophylax in gryllids and tettigoniids (Orthoptera). Large spermatophores in, for example, the Lepidoptera and Coleoptera, also appear to be maintained by selection on the male to maximize ejaculate transfer and thereby counter the effects of sperm competition. In contrast to the large amount of evidence in support of the mating effort hypothesis, there is a relative lack of good evidence to support the paternal investment hypothesis. Certain studies have demonstrated an increase in the weight and/or number of eggs laid as a result of the receipt of larger gifts, or a greater number of gifts, in tettigoniids, gryllids, acridids, mantids, bruchid beetles, drosophilids and lepidopterans. However, virtually all of these studies (with the possible exception of studies of the spermatophylax in tettigoniids) have failed to control adequately for hormonal substances in the ejaculate that are known to affect female reproductive output. Furthermore, in at least four tettigoniids (but not in the case of two species), three lepidopterans, a drosophilid and probably also bruchid beetles and bittacids, evidence suggests that the male has a low probability of fertilising the eggs that stand to benefit from his nuptial gift nutrients. Therefore, the hypothesis that paternal investment might account for the function of nuptial gifts in general is not supported.

Abstract This article classifies colour emotions for single colours and develops colour‐science‐based colour emotion models. In a psychophysical experiment, 31 observers, including 14 British and 17 Chinese subjects assessed 20 colours on 10 colour‐emotion scales: warm–cool, heavy–light, modern–classical, clean–dirty, active–passive, hard–soft, tense–relaxed, fresh–stale, masculine–feminine, and like–dislike. Experimental results show no significant difference between male and female data, whereas different results were found between British and Chinese observers for the tense–relaxed and like–dislike scales. The factor analysis identified three colour‐emotion factors: colour activity, colour weight, and colour heat. The three factors agreed well with those found by Kobayashi and Sato et al. Four colour‐emotion models were developed, including warm–cool, heavy–light, active–passive, and hard–soft. These models were compared with those developed by Sato et al. and Xin and Cheng. The results show that for each colour emotion the models of the three studies agreed with each other, suggesting that the four colour emotions are culture‐independent across countries. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 232–240, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20010

AIMS: Previous systematic reviews have found that drug-related morbidity accounts for 4.3% of preventable hospital admissions. None, however, has identified the drugs most commonly responsible for preventable hospital admissions. The aims of this study were to estimate the percentage of preventable drug-related hospital admissions, the most common drug causes of preventable hospital admissions and the most common underlying causes of preventable drug-related admissions. METHODS: Bibliographic databases and reference lists from eligible articles and study authors were the sources for data. Seventeen prospective observational studies reporting the proportion of preventable drug-related hospital admissions, causative drugs and/or the underlying causes of hospital admissions were selected. Included studies used multiple reviewers and/or explicit criteria to assess causality and preventability of hospital admissions. Two investigators abstracted data from all included studies using a purpose-made data extraction form. RESULTS: From 13 papers the median percentage of preventable drug-related admissions to hospital was 3.7% (range 1.4-15.4). From nine papers the majority (51%) of preventable drug-related admissions involved either antiplatelets (16%), diuretics (16%), nonsteroidal anti-inflammatory drugs (11%) or anticoagulants (8%). From five studies the median proportion of preventable drug-related admissions associated with prescribing problems was 30.6% (range 11.1-41.8), with adherence problems 33.3% (range 20.9-41.7) and with monitoring problems 22.2% (range 0-31.3). CONCLUSIONS: Four groups of drugs account for more than 50% of the drug groups associated with preventable drug-related hospital admissions. Concentrating interventions on these drug groups could reduce appreciably the number of preventable drug-related admissions to hospital from primary care.

Concerns about problematic gaming behaviors deserve our full attention. However, we claim that it is far from clear that these problems can or should be attributed to a new disorder. The empirical basis for a Gaming Disorder proposal, such as in the new ICD-11, suffers from fundamental issues. Our main concerns are the low quality of the research base, the fact that the current operationalization leans too heavily on substance use and gambling criteria, and the lack of consensus on symptomatology and assessment of problematic gaming. The act of formalizing this disorder, even as a proposal, has negative medical, scientific, public-health, societal, and human rights fallout that should be considered. Of particular concern are moral panics around the harm of video gaming. They might result in premature application of diagnosis in the medical community and the treatment of abundant false-positive cases, especially for children and adolescents. Second, research will be locked into a confirmatory approach, rather than an exploration of the boundaries of normal versus pathological. Third, the healthy majority of gamers will be affected negatively. We expect that the premature inclusion of Gaming Disorder as a diagnosis in ICD-11 will cause significant stigma to the millions of children who play video games as a part of a normal, healthy life. At this point, suggesting formal diagnoses and categories is premature: the ICD-11 proposal for Gaming Disorder should be removed to avoid a waste of public health resources as well as to avoid causing harm to healthy video gamers around the world.

Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power systems and to help customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using deep reinforcement learning, a hybrid type of methods that combines reinforcement learning with deep learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and deep policy gradient, both of which have been extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly dimensional database includes information about photovoltaic power generation, electric vehicles and buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide realtime feedback to consumers to encourage more efficient use of electricity.

Food allergy affects 6% of children but there is no cure, and strict avoidance of index allergens along with immediate access to rescue medication is the current best management. With specialist care, morbidity from food allergy in children is generally low, and mortality is very rare. However, there is strong evidence that food allergy and food hypersensitivity has an impact on psychological distress and on the quality of life (QoL) of children and adolescents, as well as their families. Until recently, the measurement of QoL in allergic children has proved difficult because of the lack of investigative tools available. New instruments for assessing QoL in food allergic children have recently been developed and validated, which should provide further insights into the problems these children encounter and will enable us to measure the effects of interventions in patients. This review examines the published impact of food allergy on affected children, adolescents and their families. It considers influences such as gender, age, disease severity, co-existing allergies and external influences, and examines how these may impact on allergy-related QoL and psychological distress including anxiety and depression. Implications of the impact are considered alongside avenues for future research.

Depressive personality and depressive illness are examined from an evolutionary adaptationist standpoint. It is postulated that the depressive state evolved in relation to social competition, as an unconscious, involuntary losing strategy, enabling the individual to accept defeat in ritual agonistic encounters and to accommodate to what would otherwise be unacceptably low social rank.