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Computational systems that process multiple affective states may benefit from explicitly considering the interaction between the states to enhance their recognition performance. This work proposes the combination of a multi-label classifier, Circular Classifier Chain (CCC), with a multimodal classifier, Fusion using a Semi-Naive Bayesian classifier (FSNBC), to include explicitly the dependencies between multiple affective states during the automatic recognition process. This combination of classifiers is applied to a virtual rehabilitation context of post-stroke patients. We collected data from post-stroke patients, which include finger pressure, hand movements, and facial expressions during ten longitudinal sessions. Videos of the sessions were labelled by clinicians to recognize four states: tiredness, anxiety, pain, and engagement. Each state was modelled by the FSNBC receiving the information of finger pressure, hand movements, and facial expressions. The four FSNBCs were linked in the CCC to exploit the dependency relationships between the states. The convergence of CCC was reached by 5 iterations at most for all the patients. Results (ROC AUC)) of CCC with the FSNBC are over $0.940 \pm 0.045$ ( $mean \pm std.\,deviation$ ) for the four states. Relationships of mutual exclusion between engagement and all the other states and co-occurrences between pain and anxiety were detected and discussed.

Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging problem. This is due to the lack of anomaly labels, high data volatility and the demands of ultra-low inference times in modern applications. Despite the recent developments of deep learning approaches for anomaly detection, only a few of them can address all of these challenges. In this paper, we propose TranAD, a deep transformer network based anomaly detection and diagnosis model which uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD uses focus score-based self-conditioning to enable robust multi-modal feature extraction and adversarial training to gain stability. Additionally, model-agnostic meta learning (MAML) allows us to train the model using limited data. Extensive empirical studies on six publicly available datasets demonstrate that TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training. Specifically, TranAD increases F1 scores by up to 17%, reducing training times by up to 99% compared to the baselines., Comment: Accepted in VLDB 2022

Long term sustainability in a competitive and changing environment requires an organization to continuously learn and adapt. The ability to access and use new knowledge is contingent on the organisational absorptive capacity (AC). In this paper, we focus on how an organization’s absorptive capacity and its enterprise system practices develop and co-evolve over time. Analysing a fifteen years’ ERP practice in an organisational context, this study synthesizes a new AC analysis framework that takes into account the dynamic nature of AC. This high-level analysis coupled with a longitudinal view resolves inconsistent results between current AC studies and suggest further directions for organisational AC research.

In Disaster Management (DM), reusing knowledge of best practices from past experiences is envisaged as the best approach for dealing with future disasters. But analysing and modelling processes involved in those experiences is a well-known challenge. But the efficient storage of those processes to allow reuse by others in future DM endeavours is even more challenging and less discussed. Without an efficient process in place, DM knowledge reuse becomes even more remote as the effort incurred gets construed as a hindrance to more pressing activities during the execution of disaster activities. Efficiency has to also be pursued without compromising the effectiveness of the knowledge analysis and reuse. It is important to ensure that knowledge remains meaningful and relevant after it is transformed. This paper presents and validates a DM knowledge analysis framework (DMKAF 2.0) that caters for efficient transformation of DM knowledge intended for reuse. The paper demonstrates that undertaking knowledge transformation and storage in the context of its use is crucial in DM for both, effectiveness and efficiency of the transformation process. Design Science Research methodology guides the research undertaken, by informing enhancements and how the framework is evaluated. A real case study of flood DM from the State Emergency Service of Victoria State Australia is successfully used to validate these enhancements.

Spatial memory is a cognitive skill that allows the recall of information about the space, its layout, and items’ locations. We present a novel application built around 3D spatial audio technology to evaluate audio-spatial memory abilities. The sound sources have been spatially distributed employing the 3D Tune-In Toolkit, a virtual acoustic simulator. The participants are presented with sequences of sounds of increasing length emitted from virtual auditory sources around their heads. To identify stimuli positions and register the test responses, we designed a custom-made interface with buttons arranged according to sound locations. We took inspiration from the Corsi-Block test for the experimental procedure, a validated clinical approach for assessing visuo-spatial memory abilities. In two different experimental sessions, the participants were tested with the classical Corsi-Block and, blindfolded, with the proposed task, named Audio-Corsi for brevity. Our results show comparable performance across the two tests in terms of the estimated memory parameter precision. Furthermore, in the Audio-Corsi we observe a lower span compared to the Corsi-Block test. We discuss these results in the context of the theoretical relationship between the auditory and visual sensory modalities and potential applications of this system in multiple scientific and clinical contexts.

The rising population of millennials, coupled with Digital Assistants (DA) and online purchasing trends among consumers have gained increasing attention by global marketers. The study evaluates the influence of DA attributes on the purchasing intention (PUI) of millennials. A combined approach of PLS-SEM, Artificial Neural Network (ANN) and Fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to predict the PUI of 345 millennials. Also, multi-group analysis is employed to uncover the influence of gender on the relationship between PUI and DA attributes. The findings suggest that DA attributes may amplify purchasing intention among millennials, especially through perceived interactivity and anthropomorphism. Further, the moderating role of gender was found significant on the inter-relationship of perceived interactivity and PUI. This research is a pioneer study in the area of artificial intelligence, conversational commerce, DA and AI-powered chatbots. This study will help marketers and practitioners to predict millennial purchasing intentions. An evaluation of this paper may help them to foster immersive and effective engagement through DA.

Background Therapeutic hypothermia is standard of care for babies with moderate to severe hypoxic–ischaemic encephalopathy. There is limited evidence to inform provision of nutrition during hypothermia. Objectives To assess the association during therapeutic hypothermia between (1) enteral feeding and outcomes, such as necrotising enterocolitis and (2) parenteral nutrition and outcomes, such as late-onset bloodstream infection. Design A retrospective cohort study using data held in the National Neonatal Research Database and applying propensity score methodology to form matched groups for analysis. Setting NHS neonatal units in England, Wales and Scotland. Participants Babies born at ≥ 36 gestational weeks between 1 January 2010 and 31 December 2017 who received therapeutic hypothermia for 72 hours or who died during treatment. Interventions Enteral feeding analysis – babies who were enterally fed during therapeutic hypothermia (intervention) compared with babies who received no enteral feeds during therapeutic hypothermia (control). Parenteral nutrition analysis – babies who received parenteral nutrition during therapeutic hypothermia (intervention) compared with babies who received no parenteral nutrition during therapeutic hypothermia (control). Outcome measures Primary outcomes were severe and pragmatically defined necrotising enterocolitis (enteral feeding analysis) and late-onset bloodstream infection (parenteral nutrition analysis). Secondary outcomes were survival at neonatal discharge, length of neonatal stay, breastfeeding at discharge, onset of breastfeeding, time to first maternal breast milk, hypoglycaemia, number of days with a central line in situ, duration of parenteral nutrition, time to full enteral feeds and growth. Results A total of 6030 babies received therapeutic hypothermia. Thirty-one per cent of babies received enteral feeds and 25% received parenteral nutrition. Seven babies (0.1%) were diagnosed with severe necrotising enterocolitis, and further comparative analyses were not conducted on this outcome. A total of 3236 babies were included in the matched enteral feeding analysis. Pragmatically defined necrotising enterocolitis was rare in both groups (0.5% vs. 1.1%) and was lower in babies who were fed during hypothermia (rate difference –0.5%, 95% confidence interval –1.0% to –0.1%; p = 0.03). Higher survival to discharge (96.0% vs. 90.8%, rate difference 5.2%, 95% confidence interval 3.9% to 6.6%; p p p = 0.03). Survival was lower in babies who did not receive parenteral nutrition (90.0% vs. 93.1%, rate difference 3.1%, 95% confidence interval 1.5% to 4.7%; p Limitations Propensity score methodology can address imbalances in observed confounders only. Residual confounding by unmeasured or poorly recorded variables cannot be ruled out. We did not analyse by type or volume of enteral or parenteral nutrition. Conclusions Necrotising enterocolitis is rare in babies receiving therapeutic hypothermia, and the introduction of enteral feeding is associated with a lower risk of pragmatically defined necrotising enterocolitis and other beneficial outcomes, including rates of higher survival and breastfeeding at discharge. Receipt of parenteral nutrition during therapeutic hypothermia is associated with a higher rate of late-onset infection but lower mortality. These results support introduction of enteral feeding during therapeutic hypothermia. Future work Randomised trials to assess parenteral nutrition during therapeutic hypothermia. Trial registration Current Controlled Trials ISRCTN474042962. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 36. See the NIHR Journals Library website for further project information.

This study proposes a supply chain resilience assessment framework at the network (i.e. structural) level based on quantifying supply chain networks’ structural factors and their relationships to different resilience strategies, by using a hybrid DEMATEL-ANP approach. DEMATEL is used to quantify interdependencies between the structural resilience factors, and between the resilience strategies. ANP is then used to quantify the outer-dependencies among these elements and to construct the limit super-matrix from which the global weights of all the decision network’s elements are estimated. To create the structural resilience factors, different network factors are selected and adopted from the social network analysis and supply chain resilience literatures. A case study is then performed to assess the performance of the proposed approach and to derive important observations to support future decision making. According to the results, the proposed approach can suitably measure the resilience performance of a supply chain network and help decision makers plan for more effective resilience improvement actions. Accepted version

Today’s complex problems call for multidisciplinary analytics teams comprising of both analytics and non-technical domain (i.e. subject matter) experts. Recognizing the difference between data visualisaion (DV) (i.e. static visual outputs) and visual analytics (VA) (i.e. a process of interactive visual data exploration, guided by user’s domain and contextual knowledge), this paper focuses on VA for non-technical domain experts. By seeking to understand knowledge sharing from VA experts to non-technical users of VA in a multidisciplinary team, we aim to explore how these domain experts learn to use VA as a thinking tool, guided by their knowing-in-practice. The research described in this paper was conducted in the context of a long-term industry-wide research project called the ‘Visual Historical Atlas of the Australian Co-operatives’, led by a multidisciplinary VA team who faced the challenge tackled by this research. Using Action Design Research (ADR) and the combined theoretical lens of boundary objects and secondary design, the paper theorises a three-phase method for knowledge transfer, translation and transformation from VA experts to domain experts using different types of VA-related boundary objects. Together with the proposed set of design principles, the three-phase model advances the well-established stream of research on organizational use of analytics, extending it to the emerging area of visual analytics for non-technical decision makers.

While multidisciplinary collaboration is increasingly considered as a prerequisite for innovation in design, it is unclear what has been studied and what to investigate next. To address this, we conducted a systematic literature review on multidisciplinary design collaboration, focussing on what has been found, and how these studies have been implemented. Following a PRISMA approach, 17 papers were selected for a critical review. A co-occurrence analysis found that the selected literature covered five themes centred on communication, all highlighting the importance of shared understanding in multidisciplinary design collaboration. Further analysis revealed biases and differences between the methodological approach followed in the studies. For future research, we suggest investigating two under-explored areas of design collaboration: distributed work and digital/service-oriented design activities.

Background Colonoscopy surveillance is recommended for some patients post polypectomy. The 2002 UK surveillance guidelines classify post-polypectomy patients into low, intermediate and high risk, and recommend different strategies for each classification. Limited evidence supports these guidelines. Objectives To examine, for each risk group, long-term colorectal cancer incidence by baseline characteristics and the number of surveillance visits; the effects of interval length on detection rates of advanced adenomas and colorectal cancer at first surveillance; and the cost-effectiveness of surveillance compared with no surveillance. Design A retrospective cohort study and economic evaluation. Setting Seventeen NHS hospitals. Participants Patients with a colonoscopy and at least one adenoma at baseline. Main outcome measures Long-term colorectal cancer incidence after baseline and detection rates of advanced adenomas and colorectal cancer at first surveillance. Data sources Hospital databases, NHS Digital, the Office for National Statistics, National Services Scotland and Public Health England. Methods Cox regression was used to compare colorectal cancer incidence in the presence and absence of surveillance and to identify colorectal cancer risk factors. Risk factors were used to stratify risk groups into higher- and lower-risk subgroups. We examined detection rates of advanced adenomas and colorectal cancer at first surveillance by interval length. Cost-effectiveness of surveillance compared with no surveillance was evaluated in terms of incremental costs per colorectal cancer prevented and per quality-adjusted life-year gained. Results Our study included 28,972 patients, of whom 14,401 (50%), 11,852 (41%) and 2719 (9%) were classed as low, intermediate and high risk, respectively. The median follow-up time was 9.3 years. Colorectal cancer incidence was 140, 221 and 366 per 100,000 person-years among low-, intermediate- and high-risk patients, respectively. Attendance at one surveillance visit was associated with reduced colorectal cancer incidence among low-, intermediate- and high-risk patients [hazard ratios were 0.56 (95% confidence interval 0.39 to 0.80), 0.59 (95% confidence interval 0.43 to 0.81) and 0.49 (95% confidence interval 0.29 to 0.82), respectively]. Compared with the general population, colorectal cancer incidence without surveillance was similar among low-risk patients and higher among high-risk patients [standardised incidence ratios were 0.86 (95% confidence interval 0.73 to 1.02) and 1.91 (95% confidence interval 1.39 to 2.56), respectively]. For intermediate-risk patients, standardised incidence ratios differed for the lower- (0.70, 95% confidence interval 0.48 to 0.99) and higher-risk (1.46, 95% confidence interval 1.19 to 1.78) subgroups. In each risk group, incremental costs per colorectal cancer prevented and per quality-adjusted life-year gained with surveillance were lower for the higher-risk subgroup than for the lower-risk subgroup. Incremental costs per quality-adjusted life-year gained were lowest for the higher-risk subgroup of high-risk patients at £7821. Limitations The observational design means that we cannot assume that surveillance caused the reductions in cancer incidence. The fact that some cancer staging data were missing places uncertainty on our cost-effectiveness estimates. Conclusions Surveillance was associated with reduced colorectal cancer incidence in all risk groups. However, in low-risk patients and the lower-risk subgroup of intermediate-risk patients, colorectal cancer incidence was no higher than in the general population without surveillance, indicating that surveillance might not be necessary. Surveillance was most cost-effective for the higher-risk subgroup of high-risk patients. Future work Studies should examine the clinical effectiveness and cost-effectiveness of post-polypectomy surveillance without prior classification of patients into risk groups. Trial registration This trial is registered as ISRCTN15213649. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 26. See the NIHR Journals Library website for further project information.

Sequentially detecting multiple changepoints in a data stream is a challenging task. Difficulties relate to both computational and statistical aspects, and in the latter, specifying control parameters is a particular problem. Choosing control parameters typically relies on unrealistic assumptions, such as the distributions generating the data, and their parameters, being known. This is implausible in the streaming paradigm, where several changepoints will exist. Further, current literature is mostly concerned with streams of continuous-valued observations, and focuses on detecting a single changepoint. There is a dearth of literature dedicated to detecting multiple changepoints in transition matrices, which arise from a sequence of discrete states. This paper makes the following contributions: a complete framework is developed for adaptively and sequentially estimating a Markov transition matrix in the streaming data setting. A change detection method is then developed, using a novel moment matching technique, which can effectively monitor for multiple changepoints in a transition matrix. This adaptive detection and estimation procedure for transition matrices, referred to as ADEPT-M, is compared to several change detectors on synthetic data streams, and is implemented on two real-world data streams – one consisting of over nine million HTTP web requests, and the other being a well-studied electricity market data set.

This research paper assesses complexity in cloud computing adoption, using the context of the local government sector in Australia. The research utilized both cloud computing adoption literature and an Information Systems Complexity Framework to propose a complexity assessment model for cloud computing adoption. A mixed method approach was used in this research. Firstly, we conducted 21 indepth interviews with IT managers in the local governments in Australia to obtain their insights into the complexity of cloud computing adoption. Secondly, a quantitative method is used in which 480 IT staff from 47 local governments responded to an online survey to validate the proposed assessment model. The findings indicate that structural complexity of an organization (i.e., knowledge management), structural complexity of technology (i.e., technology interoperability, and data processing capability), dynamic complexity of an organization (i.e., business operations), and dynamic complexity of technology (i.e., systems integration, IT infrastructure update, and customization resources) are critical complexity aspects to be considered during cloud computing adoption. These findings provide important implications for both researchers and managers that are trying to understand the complexities involved in cloud computing adoption.

A significant proportion of classes in modern software introduce or use object protocols, prescriptions on the temporal orderings of method calls on objects. This article studies search-based test generation techniques that aim to exploit a particular abstraction of object protocols (enabledness preserving abstractions (EPAs)) to find failures. We define coverage criteria over an extension of EPAs that includes abnormal method termination and define a search-based test case generation technique aimed at achieving high coverage. Results suggest that the proposed case generation technique with a fitness function that aims at combined structural and extended EPA coverage can provide better failure-detection capabilities not only for protocol failures but also for general failures when compared to random testing and search-based test generation for standard structural coverage.

Building software that can support the huge growth in data and computation required by modern research needs individuals with increasingly specialist skill sets that take time to develop and maintain. The Research Software Engineering movement, which started in the UK and has been built up over recent years, aims to recognise and support these individuals. Why does research software matter to professional software development practitioners outside the research community? Research software can have great impact on the wider world and recent progress means the area can now be considered as a more realistic option for a professional software development career. In this article we present a structure, along with supporting evidence of real-world activities, that defines four elements that we believe are key to providing comprehensive and sustainable support for Research Software Engineering. We also highlight ways that the wider developer community can learn from, and engage with, these activities.

© 1999-2012 IEEE. Satellite networks have long been regarded as a key enabler for ubiquitous Internet access and global data distribution. However, since they are highly dynamic and much more vulnerable to various failures, how to detour traffic around fault satellites and interrupted links becomes an important but challenging issue. Thanks to the emerging software-defined networking, great controllability can be introduced to the satellite networks for agile management and automation. Hence, in this article, we focus on elastic resilience for software-defined satellite networking, and propose a preliminary solution to cope with the related fundamental challenges in guarantees of controller reachability, collections of network status, and failure detection and recovery. We also discuss several key open issues to be urgently addressed, hoping to shed some light on this promising land.

Big data and machine learning technologies facilitate various business intelligence activities for businesses. However, personal data collection can generate adverse effects on consumers. Big data collection can compromise people’s sense of autonomy, harming digital privacy, transparency and trust. This research investigates personal data collection, control, awareness, and privacy regulation on people’s autonomy in Saudi. This study used a hybrid analytical model that incorporates symmetrical and asymmetrical analysisviafuzzy set qualitative comparative analysis (fsQCA) to analyze consumer sense of autonomy regarding big data collection. The symmetrical shows that ‘Control’ had the most significant influence on people’s autonomy, followed by ‘Big data collection’ and ‘Awareness’. The fsQCA shows 84% of the variation, explaining the people’s autonomy.

Recognition of glomeruli lesions is the key for diagnosis and treatment planning in kidney pathology; however, the coexisting glomerular structures such as mesangial regions exacerbate the difficulties of this task. In this paper, we introduce a scheme to recognize fine-grained glomeruli lesions from whole slide images. First, a focal instance structural similarity loss is proposed to drive the model to locate all types of glomeruli precisely. Then an Uncertainty Aided Apportionment Network is designed to carry out the fine-grained visual classification without bounding-box annotations. This double branch-shaped structure extracts common features of the child class from the parent class and produces the uncertainty factor for reconstituting the training dataset. Results of slide-wise evaluation illustrate the effectiveness of the entire scheme, with an 8-22% improvement of the mean Average Precision compared with remarkable detection methods. The comprehensive results clearly demonstrate the effectiveness of the proposed method., 33 pages, 6 figures, accepted by the Pattern Recognition journal

Companies in platform-based business markets have widely embraced freemium business models where profit primarily depends on a minority of paying customers. However, the key challenge of these models is transitioning participants from free users to paying consumers. To encourage paid consumption, companies often rely on product differentiation such as providing consumers who pay for products or services with enhanced features. However, limited research has addressed how such product differentiation may convert consumers from “free” to “fee.” Our research examines multiplayer online battle arena (MOBA) games as a compelling example of freemium platform-based business models. We contribute to the freemium literature by introducing three new MOBA-specific differentiations—character competency, character variety, and character-appearance differentiation. We also extend consumption values theory (CVT) into a dual-path model to unveil the underlying mechanisms through which product differentiation influences in-game purchase. We empirically validate our dual-path model using data from a two-wave longitudinal experiment and three cross-sectional experiments. Our findings support opposing mediating paths for product differentiation in character competency and variety and indicate that these two types of differentiation can indeed undermine perceived game fairness. Conversely, character-appearance differentiation exerts only a positive influence on players’ purchasing of in-game items. Consequently, the findings of this study have important potential implications for platform-based companies leveraging freemium business models that seek to increase their share of paying customers. Accepted version

In this paper we introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act component houses a set of options, high-level actions enacted by pre-trained deep reinforcement learning (DRL) policies. The Understand component provides a novel answer set programming (ASP) paradigm for symbolically implementing a meta-policy over options and effectively learning it using inductive logic programming (ILP). We evaluate our framework on the Animal-AI (AAI) competition testbed, a set of physical cognitive reasoning problems. Given a set of pre-trained DRL policies, DUA requires only a few examples to learn a meta-policy that allows it to improve the state-of-the-art on multiple of the most challenging categories from the testbed. DUA constitutes the first holistic hybrid integration of computer vision, ILP and DRL applied to an AAI-like environment and sets the foundations for further use of ILP in complex DRL challenges.

Explosion in usage of social media has made its analysis a relevant topic of interest, and particularly so in the political science area. Within Data Science, no other techniques are more widely accepted and appealing than visualisation. However, with datasets growing in size, visualisation tools also require a paradigm shift to remain useful in big data contexts. This work presents our proposal for a Large-Scale Twitter Observatory that enables researchers to efficiently retrieve, analyse and visualise data from this social network to gain actionable insights and knowledge related with political events. In addition to describing the supporting technologies, we put forward a working pipeline and validate the setup with different examples.

In this work, the results of usability and user experience (UX) evaluation of a serious video game for the cognitive stimulation and motor rehabilitation of the upper limb of the elderly are presented. The serious game includes features that allow (1) performing cooperative therapy exercises between two patients, (2) remote session configuration therapy, and (3) monitoring/analyzing the sessions’ results by the therapist. An evaluation of the game with 16 older adults provides evidence about how the tool is perceived by participants, who embraced it as stimulating, useful, usable and even fun, and which impacts in therapy compliance and acceptability by the elderly. In addition, through an in depth analysis of the participants’ performance and observed emotions, as well as their self-report, we determined which engagement attributes are present in the game. Finally, we also found evidence that suggests that the participants’ skill level and the game difficulty level rather than just a good performance on the game are key factors that influence their enjoyment and frustration.

The routine measurement of gastric residual volume to guide the initiation and delivery of enteral feeding is widespread in paediatric intensive care and neonatal units, but has little underlying evidence to support it.To answer the question: is a trial of no gastric residual volume measurement feasible in UK paediatric intensive care units and neonatal units?A mixed-methods study involving five linked work packages in two parallel arms: neonatal units and paediatric intensive care units. Work package 1: a survey of units to establish current UK practice. Work package 2: qualitative interviews with health-care professionals and caregivers of children admitted to either setting. Work package 3: a modified two-round e-Delphi survey to investigate health-care professionals' opinions on trial design issues and to obtain consensus on outcomes. Work package 4: examination of national databases to determine the potential eligible populations. Work package 5: two consensus meetings of health-care professionals and parents to review the data and agree consensus on outcomes that had not reached consensus in the e-Delphi study.Parents of children with experience of ventilation and tube feeding in both neonatal units and paediatric intensive care units, and health-care professionals working in neonatal units and paediatric intensive care units.Baseline surveys showed that the practice of gastric residual volume measurement was very common (96% in paediatric intensive care units and 65% in neonatal units). Ninety per cent of parents from both neonatal units and paediatric intensive care units supported a future trial, while highlighting concerns around possible delays in detecting complications. Health-care professionals also indicated that a trial was feasible, with 84% of staff willing to participate in a trial. Concerns expressed by junior nurses about the intervention arm of not measuring gastric residual volumes were addressed by developing a simple flow chart and education package. The trial design survey and e-Delphi study gained consensus on 12 paediatric intensive care unit and nine neonatal unit outcome measures, and identified acceptable inclusion and exclusion criteria. Given the differences in physiology, disease processes, environments, staffing and outcomes of interest, two different trials are required in the two settings. Database analyses subsequently showed that trials were feasible in both settings in terms of patient numbers. Of 16,222 children who met the inclusion criteria in paediatric intensive care units, 12,629 stayed for 3 days. In neonatal units, 15,375 neonates 32 weeks of age met the inclusion criteria. Finally, the two consensus meetings demonstrated 'buy-in' from the wider UK neonatal communities and paediatric intensive care units, and enabled us to discuss and vote on the outcomes that did not achieve consensus in the e-Delphi study.Two separate UK trials (one in neonatal units and one in paediatric intensive care units) are feasible to conduct, but they cannot be combined as a result of differences in outcome measures and treatment protocols, reflecting the distinctness of the two specialties.Current Controlled Trials ISRCTN42110505.This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full inNurses looking after babies and children on intensive care units in the UK usually pass a tube and aspirate whatever food or fluid is in the baby’s stomach before they give a feed. The idea is to ensure that the stomach is not overdistended with food and prevent the baby vomiting or, worse, aspirating food into the lungs. However, there is little justification for this practice. It is rarely done in many other countries. It may not be pleasant for the child and perhaps is unnecessary. Some experts have suggested that the policy should be evaluated in a randomised controlled trial. This would mean allocating a large number of children at random to either have the stomach aspirated before feeds, or not. Such a trial would be a major undertaking and we are unsure if parents or staff would be willing to allow children to participate. The aim of this study was to see if it is possible to conduct such a large trial in the UK. Two surveys (of 119 units) showed us that regularly measuring the stomach contents when starting and increasing feeds is common practice for both newborn and older children in UK intensive care units. However, in some countries, such as France, this practice is rarely done. We asked 31 parents and 51 health-care professionals about a future study. Overall, parents were supportive of a trial if it was explained to them well by a knowledgeable and caring professional, and if they were approached at the right time. Some concerns were expressed about not picking up complications early if gastric residual volume was not measured. Health-care professionals were also mainly positive about a future trial, but mentioned similar concerns about not picking up complications early and the difficulty of changing a long-standing routine practice. Parents suggested study outcomes that were important to them. These, along with other outcomes, were voted on in a further survey of 106 professionals and at face-to-face meetings involving 41 participants. Overall, our findings suggest that a trial is feasible to perform and acceptable to parents. However, because of differences in both treatments and important outcomes between children’s intensive care units and newborn baby intensive care units, two trials would be needed, one in each type of intensive care unit. These two trials will test whether or not the benefits of not measuring gastric residual volume (e.g. improved calorie intake) outweigh the potential harms (e.g. delayed diagnosis of complications).

In the era of big data, large-scale information visualization has become an important challenge. Scalable resolution display environments (SRDEs) have emerged as a technological solution for building high-resolution display systems by tiling lower resolution screens. These systems bring serious advantages, including lower construction cost and better maintainability compared to other alternatives. However, they require specialized software but also purpose-built content to suit the inherently complex underlying systems. This creates several challenges when designing visualizations for big data, such that can be reused across several SRDEs of varying dimensions. This is not yet a common practice but is becoming increasingly popular among those who engage in collaborative visual analytics in data observatories. In this paper, we define three key requirements for systems suitable for such environments, point out limitations of existing frameworks, and introduce Tuoris , a novel open-source middleware for visualizing dynamic graphics in SRDEs. Tuoris manages the complexity of distributing and synchronizing the information among different components of the system, eliminating the need for purpose-built content. This makes it possible for users to seamlessly port existing graphical content developed using standard web technologies, and simplifies the process of developing advanced, dynamic and interactive web applications for large-scale information visualization. Tuoris is designed to work with Scalable Vector Graphics (SVG), reducing bandwidth consumption and achieving high frame rates in visualizations with dynamic animations. It scales independent of the display wall resolution and contrasts with other frameworks that transmit visual information as blocks of images.

Background: Despite widely implemented, enterprise systems remain an unsettled role in organizational innovation. This study purposes to address the effects of enterprise systems (ES) on firm innovation by adopting resource-based theory and capability building theory to focus on ES-enabled competence, rather than ES investment or implementation. ES-enabled competence is proposed to mediate the effect of ES integration on innovation performance. We further propose that continuous improvement moderates (1) the relationship between ES integration and ES-enabled competence, and (2) the relationship between ES-enabled competence and innovation performance. By examining these effects, we aim to discover how ES enables innovation at operational and strategic levels separately. Method: A survey method is conducted to explore the relationship between enterprise systems (ES) and innovation. Data are collected from manufacturing companies in 10 countries of three regions, i.e., Europe, Asia-Pacific, and the USA, and analyzed by using structural equation modeling technique. Results: We confirm the roles of enterprise systems as a resource and a capability and the effects of these roles on innovation—including the operational outcome, new product development performance, and the strategic one, innovation uniqueness. We demonstrate that continuous improvement moderates the mediation paths, namely “ES integration – ES-enabled competence – innovation performance”. The moderated mediation effect exists among continuous improvement, ES integration, ES-enabled competence, and innovation uniqueness. Conclusion: This study contributes to the ES and innovation research by uncovering the micro-foundation underlying ES-enabled innovation from a capability-based framework and elaborating the moderating role of continuous improvement in enhancing innovation. Accepted version