64623 research outputs found
Sort by
Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing
Get PDFThe Industry 4.0 (I4.0) revolution has led to rapid digital transformation, automation of manufacturing processes and efficient decision-making in business operations. Despite the potential benefits of I4.0 technologies in operations management reported in the extant literature, there has been a paucity of empirical research examining the intention to adopt I4.0 technologies for managing risks. Risk management identifies, assesses, and introduces responses for risks to avert crises. This study combines institutional theory, the resource-based view and the technology acceptance model to develop a novel behavioural model examining the adoption of big data, artificial intelligence, cloud computing, and blockchain for risk management from the operations manager's perspective, which has never been examined in the literature. The model was tested for each I4.0 technology using data collected from 117 operations managers in the UK manufacturing industry which were analysed using structural equation modelling. We contribute to the theory on I4.0 in digital manufacturing by showing the impact of digital transformation maturity, market pressure, regulations, and resilience on the perceived usefulness and adoption of these technologies for managing risks in business operations. Based on the findings, we discuss implications for operations managers effectively and efficiently to adopt I4.0 technologies aiming to boost operational productivity
Deep Generative Models in the Industrial Internet of Things: a Survey
Get PDFAdvances in communication technologies and artificial intelligence (AI) are accelerating the paradigm of Industrial Internet of Things (IIoT). With IIoT enabling continuous integration of sensors and controllers with the network, intelligent analysis of the generated Big Data is a critical requirement. Although IIoT is considered a subset of IoT, it has its own peculiarities in terms of higher levels of safety, security and low-latency communication in an environment of critical real-time operations. Under these circumstances, discriminative deep learning (DL) algorithms are unsuitable due to their need for large amounts of labelled and balanced training data and the uncertainty of inputs, etc. To overcome these issues, researchers have started using Deep Generative Models (DGMs), which combine the flexibility of DL with the inference power of probabilistic modeling. In this work, we review the state of the art of DGMs and their applicability to IIoT, classifying the reviewed works into the IIoT application areas of anomaly detection, trust boundary protection, network traffic prediction and platform monitoring. Following an analysis of existing IIoT DGM implementations, we identify challenges (i.e. weak discriminative capability, insufficient interpretability, lack of generalization ability, generated data vulnerability, privacy concern and data complexity) that need to be investigated in order to accelerate the adoption of DGMs in IIoT and also propose some potential research directions
Environmental Audio Analysis by Non-negative Matrix Factorization
Get PDFEnvironmental sounds occur in a complex mixture. Recognizing, isolating and interpreting different environmental sounds are easy and natural tasks for human listeners but remain challenging problems for machines. With proliferation of smart audio devices it is becoming ever more important to develop robust algorithms for analysis of such environmental sounds. In this thesis, we investigate methods based on Non-negative Matrix Factorization (NMF) for several tasks of environmental audio analysis. NMF as a dictionary learning method has been proven to learn compact representations of sounds, that can be used for source separation or classification, to name just a few. The experimental evidence presented in this thesis focuses on showing how we can adapt NMF via regularization for recognition and detection of environmental sounds.Firstly, we approach the task of Audio Event Detection (AED), which aims to automatically recognize, label and estimate position in time of sound events in continuous audio signals. Using a carefully labelled dataset of real life recordings, we show how enforcing sparse representations and modelling temporal context in Coupled Sparse NMF improves accuracy of polyphonic AED.Secondly, we tackle the problem of binary classification of weakly labelled audio. By weak labels we mean that each audio file has just a tag denoting the presence or absence of a sound of interest, without its specific location in time. We propose to address this challenge by adding a constraint to basic NMF, introducing a novel Masked NMF for learning on weakly labelled audio data. We show that Masked NMF performs well on the Bird Audio Detection task.Finally, we propose an orthogonality regularizer for Masked NMF to perform AED on weakly labelled audio data. By adding a regularization term to Masked NMF, we show how the novel method can be used for monophonic AED. We conduct experiments on a dataset of rare audio events. Our results show that Orthogonality-Regularized Masked NMF is a promising method for monophonic detection of impact sounds
Orchestrating a digital platform ecosystem to address societal challenges: a robust action perspective
Get PDFOrchestration of digital platform ecosystems has been well examined in the context of markets and the private sector where an orchestrator is a resourceful firm exploiting commercial opportunities in an industry. However, little is known about how it occurs when a government organization orchestrates to address societal challenges. We build upon the construct of ‘robust action’—identified with chess masters who play to advance a broad strategy while simultaneously maintaining flexibility—to explain how orchestration by a government organization might overcome initiation, stabilization, expansion, and meta-governance challenges through digital platform enabled participative architecture, multivocal inscriptions, and distributed experimentation. Evidence for our theoretical framework is drawn from empirical studies of the Aadhaar, a platform ecosystem orchestrated to address multiple societal challenges stemming from identity and its management
Revisiting Institutional Voids: Advancing the International Business Literature by Leveraging Social Sciences
No full textInstitutions are vital for solving collective action problems and enabling functioning markets. Based on this notion, the institutional voids literature has offered a dynamic research agenda for international business scholarship. In this perspective article, we leverage work from political science, development economics, legal studies, and anthropology to: (a) expose hidden assumptions about institutional voids in the management literature; (b) propose new directions for research based on our revised assumptions; and (c) provide direction-specific theoretical constructs from other social sciences to stimulate theory-building and empirical inquiry into institutional voids. We develop a framework that identifies four revised assumptions about institutional voids research that we derive from current studies and elaborate on eight theoretical constructs from other social sciences that exemplify the revised assumptions and generate future research questions for international business scholars
Sparking Change: Evaluating the effectiveness of a multi-component intervention at encouraging more sustainable food behaviors
Get PDFFacilitating the adoption of more sustainable food behaviors is key in order to reduce pressure on nature and improve public health. Food businesses that interact directly with consumers are well placed to enable a positive change in food behaviors. The present study evaluates the effectiveness of a 9-week multi-component behavioral intervention implemented by a large UK food retailer. Three food behaviors were explored: meat consumption, food waste and scratch cooking. Evaluation methods comprise of surveys issued pre-intervention, at intervention-end and at delayed follow-up (3 months after intervention end), and focus groups where participants were divided according to life-stage (pre-family, family, retired). Results show the intervention mitigated individual barriers to change and had a positive impact on awareness, intention and behavior which lasted beyond intervention-end. Participants reported reducing their meat consumption and food waste and cooking more frequently from scratch. Findings indicate that the online community, ‘ask the expert’ videos and product samples were the most impactful intervention components, while recipes and cook-alongs were less effective. This study provides an effective and feasible intervention which could be implemented and scaled by food companies. While behavioral interventions offer a positive opportunity for companies to drive consumer behavior change, structural and cultural changes to the food environment will be needed to facilitate long-term change at scale
A Meeting of Minds: Can Cognitive Psychology Meet the Demands of Queer Theory?
Get PDFHow can cognitive psychology make a contribution to critical psychologies of science and technology? Here, we read cognitive experimental research on categorization diffractively (Barad, 2007), cutting between the commitment to positivist-empiricist ontology in any given experimental set up, and the empirical conclusions that human categorization practices are inherently hybrid. In so doing, we mean to exemplify the difference made by reading cognitive psychology as either non-critical (on the grounds of its positivist-empiricist ontology) or reparatively (on the basis of its conclusions) as a resource for critical psychology. We aim this intervention to normalize the idea that humans think queerly, and particularly to engage long-standing discussion of the relationship between criticality and positivist-empiricist methods in LGBTQ+ psychology. We aim to exemplify the difference that this diffractive reading can make, by drawing out its relevance for contemporary psychosocial research in intersex studies
Contact-Controlled Thin-Film Transistors and Compact Circuits for Low-Power Sensors and Internet of Things
Get PDFIn order to deliver billions of cost-effective sensors for Internet of Things applications, large area electronics (LAE) would need to overcome challenges in high-throughput manufacturing methods, while providing power-efficient operation. Processes, such as roll-to-roll and/or inkjet printing, hold the most promise for future disposable and wearable technologies, however progress is limited despite growing demand. The reason is the fundamental electronic device at the heart of their circuits, the thin-film transistor (TFT). TFTs share the same device physics as silicon field-effect transistors found in chip technology, as well as their limitations, notably uniformity of operation. Ordinarily, device non-idealities are compensated through cascode circuits or gain stages, which increases circuit complexity. But these strategies reduce yield, as circuit failure increases with component count. Even though there have been many breakthroughs in material systems, the limitations have persisted. Thus, the ability to produce high yield in high-throughput methods requires TFTs with more robust and uniform operation that can tolerate imprecise processes.An alternative, the source-gated transistor (SGT) is a type of TFT that uses energy barriers at the source contact to control charge injection. As a contact-controlled architecture, the SGT provides uniform and robust operation with extremely high gain and power-efficient operation.In this thesis, SGT operation is further explored in light of off-state behaviour, which produces lower leakage current. The first source-gated transistor (SGT) circuits are also included and provide exemplary performance without support circuitry. Two-transistor (2T) circuits with polysilicon SGTs demonstrate: 49 dB gain in common-source amplifiers, a record for any polysilicon TFT-based amplifier; and current mirrors that have a tuneable temperature dependence by design of the source region, where positive, neutral or negative dependence of output current can be obtained.The SGT’s ability to provide superior high-gain and power-efficient performance in a compact footprint is only superseded by that of the newly invented multimodal transistor (MMT), which shares its benefits. The MMT is an evolution of the contact-controlled concept, where charge injection is separately controlled from channel conduction. As the channel in the MMT is not responsible for charge injection, it provides: faster digital switching (up to two orders-of-magnitude faster than SGTs and one order faster than regular TFTs); an alternative means of charge transport control to mitigate hot-carrier effects in high mobility materials, when doping strategies are unavailable; a unique sample-and-hold or enable line function, ensuring signal propagation when activated. Together with the inherent ability for the MMT to produce a directly proportional dependence of output current on input voltage, these functional benefits allow for extremely compact digital-to-analog conversion and multiplication. Examples of the MMT’s ability to reduce circuit complexity would allow for circuit designers to explore new avenues into developing future LAE applications
Management of chronic peripheral artery disease patients with indication for endovascular revascularisation - A position paper by the European Society of Vascular Medicine
No full textSurface Analysis Techniques in Forensic Science: Successes, Challenges, and Opportunities for Operational Deployment
No full textSurface analysis techniques have rapidly evolved in the last decade. Some of these are already routinely used in forensics, such as for the detection of gunshot residue or for glass analysis. Some surface analysis approaches are attractive for their portability to the crime scene. Others can be very helpful in forensic laboratories owing to their high spatial resolution, analyte coverage, speed, and specificity. Despite this, many proposed applications of the techniques have not yet led to operational deployment. Here, we explore the application of these techniques to the most important traces commonly found in forensic casework. We highlight where there is potential to add value and outline the progress that is needed to achieve operational deployment. We consider within the scope of this review surface mass spectrometry, surface spectroscopy, and surface X-ray spectrometry. We show how these tools show great promise for the analysis of fingerprints, hair, drugs, explosives, and microtraces. Expected final online publication date for theVolume 15 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates
Data sources:
Access Repository Dashboard
Do you manage University of Surrey? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!