Gas hydrates represent one of the main flow assurance challenges in the oil and gas industry as they can lead to plugging of pipelines and process equipment. In this paper we present a literature study performed to evaluate the current state of the use of machine learning methods within the field of gas hydrates with specific focus on the oil chemistry. A common analysis technique for crude oils is Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) which could be a good approach to achieving a better understanding of the chemical composition of hydrates, and the use of machine learning in the field of FT-ICR MS was therefore also examined. Several machine learning methods were identified as promising, their use in the literature was reviewed and a text analysis study was performed to identify the main topics within the publications. The literature search revealed that the publications on the combination of FT-ICR MS, machine learning and gas hydrates is limited to one. Most of the work on gas hydrates is related to thermodynamics, while FT-ICR MS is mostly used for chemical analysis of oils. However, with the combination of FT-ICR MS and machine learning to evaluate samples related to gas hydrates, it could be possible to improve the understanding of the composition of hydrates and thereby identify hydrate active compounds responsible for the differences between oils forming plugging hydrates and oils forming transportable hydrates.
Industry 4.0 has become a key concept and buzzword in the global manufacturing industry. This paper argues that the concept has created an Industry 4.0 narrative that is having a decisive impact on the industry. The paper combines the literatures on the sociology of expectations and regional path development to develop an analytical framework which is employed in an analysis of the Raufoss region in Norway. We find that the expectations created by the global Industry 4.0 narrative have trickled down into national industry and innovation policies. This has resulted in the anchoring of innovation schemes, development of new educational provisions and generated technological capability building among manufacturers in the Raufoss region. In turn, we argue that these actions have enabled regional path extension.
A number of intersecting crises are currently ongoing at multiple scales, including increasing inequality, environmental degradation, and climate destabilization, as well as new surges of populism and mounting public health threats. These emergencies question our economic model of past decades and provoke a rethinking of the general approach to economic policy from a multi-scalar perspective. In this article, we compare two approaches aiming to rethink economic development policy: foundational economy and Doughnut economics, and consider if and how they complement each other. We conclude that the two approaches are potentially complementary, most prominently in their call for high-income countries to refocus from growth per se to purpose-driven economic strategies that prioritize public services and redistribute incomes. However, they differ in respect to their geographical focus, environmental concerns, and application. To properly address tradeoffs between social needs and environmental effects, foundational scholarship would benefit from deeper engagement with the socioenvironmental perspective presented in Doughnut economics, which stresses the need to consider human-nature interlinkages. In sum, combining different aspects of the two approaches promises to provide a more robust response to contemporary challenges, especially for local policy making.
Research in sustainability transitions increasingly acknowledges that the structural characteristics of socio-technical systems differ. However, little attention has been paid to the specific transition dynamics that can result from this structural variation. In response, this paper develops a framework for studying transition dynamics that takes the structural characteristics of socio-technical systems and their influence on agency into account. We introduce the concept contestation axis to highlight alternative potential interfaces between functional solutions in a socio-technical system. We argue that considerable agency and frictions between actors can play out at other axes than between established regimes and emerging niches. Our conceptual framework is applied to a case study in the waste sector. We explore how the growing influence of the circular economy triggers misalignment between multiple socio-technical configurations in the Danish waste sector. In the case, we zoom in on three actual frictions that have manifested along different contestation axes.
A key challenge in autonomous vehicles is the high-level decision making required to be reactive to significant changes in the environment. Timeline-based planning is a paradigm for temporal (time-aware) task planning that can provide such high-level decisions. We present an algorithm for incrementally translating a timeline-based planning problem into satisfability modulo theories (SMT). Off-the-shelf SMT solvers have become very efficient, and using such a solver keeps our planner implementation relatively small, simple, and extensible. Compared to existing SMT translations, our approach typically produces smaller SMT problems, and our system shows promising performance on several benchmark problems. Our planning software was developed in the context of research projects on inspection operations for UUVs on subsea installations and for mobile robots on marine installations.
Wood and other bio-based building materials are often perceived as a good choice from a climate mitigation perspective. This article compares the life cycle assessment of the same multi-residential building from the perspective of 16 countries participating in the international project Annex 72 of the International Energy Agency to determine the effects of different datasets and methods of accounting for biogenic carbon in wood construction. Three assessment methods are herein considered: two recognized in the standards (the so-called 0/0 method and –1/+1 method) and a variation of the latter (–1/+1* method) used in Australia, Canada, France, and New Zealand. The 0/0 method considers neither fixation in the production stage nor releases of biogenic carbon at the end of a wood product’s life. In contrast, the –1/+1 method accounts for the fixation of biogenic carbon in the production stage and its release in the end-of-life stage, irrespective of the disposal scenario (recycling, incineration orlandfill). The -1/+1 method assumes that landfills offer only a temporary sequestration of carbon. In the –1/+1* variation, landfills and recycling are considered a partly permanent sequestration of biogenic carbon and thus fewer emissions are accounted for in the end-of-life stage. We examine the variability of the calculated life cycle-based greenhouse gas emissions calculated for a case study building by each participating country, within the same assessment method and across the methods. The results vary substantially. The main reasons for deviations are whether or not landfills and recycling are considered a partly permanent sequestration of biogenic carbon and a mismatch in the biogenic carbon balance. Our findings support the need for further research and to develop practical guidelines to harmonize life cycle assessment methods of buildings with bio-based materials.
Plasmonic structures can help enhance optical activity in the ultraviolet (UV) region and therefore enhancing photocatalytic reactions and the detection of organic and biological species. Most plasmonic structures are composed of Ag or Au. However, producing structures small enough for optical activity in the UV region has proved difficult. In this study, we demonstrate that aluminium nanowires are an excellent alternative. We investigated the plasmonic properties of the Al nanowires as well as the optoelectronic properties of the surrounding aSi matrix by combining scanning transmission electron microscopy (STEM) imaging, electron energy loss spectroscopy (EELS) and electrodynamic modelling. We have found that the Al nanowires have distinct plasmonic modes in the UV and far UV region, from 0.75 eV to 13 eV. In addition, the size and spacing of the Al nanowires, as well as the embedding material were shown to have a large impact on the type of surface plasmons energies that can be generated in the material. Using electromagnetic modelling, we have identified the modes and illustrated how they could be tuned further.
Large-scale agile development has gained widespread interest in the software industry, but it is a topic with few empirical studies of practice. Development projects at scale introduce a range of new challenges in managing a large number of people and teams, often with high uncertainty about product requirements and technical solutions. The coordination of teams has been identified as one of the main challenges. This study presents a rich longitudinal explanatory case study of a very large software development programme with 10 development teams. We focus on inter-team coordination in two phases: one that applies a first-generation agile development method and another that uses a second-generation one. We identified 27 coordination mechanisms in the first phase, and 14 coordination mechanisms in the second. Based on an analysis of coordination strategies and mechanisms, we develop five propositions on how the transition from a first- to a second-generation method impacts coordination. These propositions have implications for theory and practice.
eHealth applications and tools have the potential to improve coordination, knowledge, and information sharing between health professionals as well as continuity of care. One of the main obstacles hindering its full integration and use, particularly in the healthcare sector in developing and low and middle-income countries is the lack of qualified staff and healthcare personnel. To explore obstacles that hinder capacity and innovation promotion initiatives, a survey was conducted among BETTEReHEALTH partners. A questionnaire was used to collect quantitative data from 37 organizations. Although there are different buckets of capacity-building and innovation promotion activities going on, the findings showed very few targeting policymakers and eHealth specialists. The findings found that obstacles to capacity building and innovation promotion include lack of finance, poor infrastructure, poor leadership, and governance, and these obstacles are context or region specific. Findings from our study concur with those from previous research on the need to identify practical solutions and simple interventions to address eHealth obstacles to capacity building in developing countries. As measures to mitigate these obstacles, our study proposed the need for adequate policies, strong political commitment, the development of academic modules to be integrated into existing educational programs, and the creation of more in-country and on-site capacity-building activities. While this study contributes to the discourse on eHealth capacity-building and innovation promotion initiatives among healthcare and public health professionals, the study has a limitation as data was collected only from BETTEReHEALTH partners.
Marine litter in the Arctic Basin is influenced by transport from Atlantic and Pacific waters. This highlights the need for harmonization of guidelines across regions. Monitoring can be used to assess temporal and spatial trends but can also be used to assess if environmental objectives are reached, for example to evaluate the effectiveness of mitigation measures. Seafloor monitoring by trawling needs substantial resources and specific sampling strategies to be sufficiently robust to demonstrate changes over time. Observation and visual evaluation in shallow and deep waters using towed camera systems, ROVs and submersibles are well suited for the Arctic environment. The use of imagery still needs to be adjusted through automation and image analyses, including deep learning approaches and data management, but will also serve to monitor areas with a rocky seafloor. We recommend developing a monitoring plan for seafloor litter by selecting representative sites for visual inspection that cover different depths and substrata in marine landscapes, and recording the litter collected or observed across all forms of seafloor sampling or imaging. We need better coverage and knowledge of status of seafloor litter for the whole Arctic and recommend initiatives to be taken for regions where such knowledge is lacking.
Background Despite growing numbers of studies reporting the efficacy of complex interventions and their implementation, many studies fail to report information on implementation fidelity or describe how fidelity measures used within the study were developed. This study aimed to develop a fidelity checklist for measuring the implementation fidelity of an early, stroke-specialist vocational rehabilitation intervention (ESSVR) in the RETAKE trial. Methods To develop the fidelity measure, previous checklists were reviewed to inform the assessment structure, and core intervention components were extracted from intervention descriptions into a checklist, which was ratified by eight experts in fidelity measurement and complex interventions. Guidance notes were generated to assist with checklist completion. To test the measure, two researchers independently applied the checklist to fifteen stroke survivor intervention case notes using retrospective observational case review. The scoring was assessed for interrater reliability. Results A fidelity checklist containing 21 core components and 6 desirable components across 4 stages of intervention delivery was developed with corresponding guidance notes. Interrater reliability of each checklist item ranged from moderate to perfect (Cohen’s kappa 0.69–1). Conclusions The resulting checklist to assess implementation fidelity is fit for assessing the delivery of vocational rehabilitation for stroke survivors using retrospective observational case review. The checklist proved its utility as a measure of fidelity and may be used to inform the design of future implementation strategies. Trial registration ISRCTN, ISRCTN12464275. Registered on 13 March 2018.
We address the problem of taming data quality in artificial intelligence (AI)-enabled Industrial Internet of Things systems by devising machine learning pipelines as part of a decentralized edge-to-cloud architecture. We present the design and deployment of our approach from an AI engineering perspective using two industrial case studies.
When evaluating fishing gear catches, the focus is often on a few species as opposed to the entire catch. In some fisheries this can lead to ignoring major part of catch composition. Thus, there is a need for a more holistic approach when evaluating the ecological impact of using a specific fishing gear and when comparing two or more gears. In this context, it is relevant to have a method that describes the total catch and quantifies proportions of the catch being wanted and unwanted. In this study, we outline such a method and demonstrate its applicability to catch data from a small-scale coastal gillnet fishery targeting European plaice (Pleuronectes platessa, Linnaeus, 1758) by comparing catch composition when using nylon and biodegradable gillnets. The results showed no significant differences in catch composition between gillnets made of the two materials. Therefore, the catch composition obtained using the more environmentally friendly biodegradable materials does not represent a barrier in this specific gillnet fishery. However, species selectivity of gillnets is still of concern as the primary target species constituted only half of the total catch composition in numbers while the rest was unwanted catch. The presented approach for quantifying and inferring the differences in catch composition can be further applied for assessing the performance of different fishing gears and their modifications.
The computing continuum enables new opportunities for managing big data pipelines concerning efficient management of heterogeneous and untrustworthy resources. We discuss the big data pipelines lifecycle on the computing continuum and its associated challenges, and we outline a future research agenda in this area.
Predictive maintenance systems face a rich set of constraints along dimensions such as latency, throughput, physical size, monetary cost, as well as energy and power consumption. To meet performance requirements, predictive maintenance systems require specialized compute units (i.e., accelerators) in addition to conventional processor cores. Unfortunately, size and cost constraints commonly result in developers being forced into selecting System-on-Chip (SoC) platforms that do not have sufficient resources to fully accelerate all performance-critical functions — in essence raising the challenging question of how to optimally distribute the available resources across accelerators. This work introduces the Resource-Constrained Accelerator Selection (RCS) methodology, which identifies near-optimal multi-accelerator configurations for predictive maintenance applications. RCS takes a library of resource-scalable accelerator architectures as input and then selects the combination of accelerator configurations that minimizes end-to-end latency. We find that enabling RCS for typical predictive maintenance applications requires a resource-scalable Fast Fourier Transform (FFT) accelerator and propose ScaleFFT to fill this gap. We apply RCS and ScaleFFT to a collection of edge computing applications with different sensor bandwidths and find that they reduce end-to-end latency by 2.4× on average for a 256K-point FFT compared to a state-of-the-art configuration that only accelerates the machine learning algorithm. Moreover, we demonstrate that RCS enables real-world gains in oil well and train track monitoring systems.
We report on the carrier lifetime control over 150 µm thick 4H‐SiC epitaxial layers via thermal generation and annihilation of carbon vacancy (VC) related Z1/2 lifetime killer sites. The defect developments upon typical SiC processing steps, such as high‐ and moderate‐temperature anneals in the presence of a carbon cap, are monitored by combining electrical characterization techniques capable of VC depth‐profiling, capacitance‐voltage (CV) and deep‐level transient spectroscopy (DLTS), with a novel all‐optical approach of cross‐sectional carrier lifetime profiling across 4H‐SiC epilayer/substrate based on imaging time‐resolved photoluminescence (TRPL) spectroscopy in orthogonal pump‐probe geometry, which readily exposes in‐depth efficacy of defect reduction and surface recombination effects. The lifetime control is realized by initial high‐temperature treatment (1800ºC) to increase VC concentration to ∽1013 cm‐3 level followed by a moderate‐temperature (1500ºC) post‐annealing of variable duration under C‐rich thermodynamic equilibrium conditions. The post‐annealing carried out for 5 hours in effect eliminates VC throughout the entire ultra‐thick epilayer. The reduction of VC‐related Z1/2 sites is proven by a significant lifetime increase from 0.8 µs to 2.5 µs. We discuss the upper limit of lifetimes in terms of carrier surface leakage and presence of other non‐radiative recombination centers besides Z1/2, possibly related to residual impurities such as boron. This article is protected by copyright. All rights reserved.
In railways, the long-term strategic planning is the process of evaluating improvements to the railway network (e.g., upgrading a single track line to a double track line) and changes to the composition/frequency of train services (e.g., adding 1 train per hour along a certain route). The effects of different combinations of infrastructure upgrades and updated train services (also called scenarios), are usually evaluated by creating new feasible timetables followed by extensive simulation. Strategic Train Timetabling (STT) is indeed the task of producing new tentative timetables for these what-if scenarios. Unlike the more classic train timetabling, STT can often overlook (or at least give less importance to) some complementary aspects, such as crew and rolling stock scheduling. On the other hand, the different scenarios are likely to lead to very different timetables, hindering the common and effective practice of using existing timetables to warm start the solution process. We introduce the concept of quasi-periodic timetables, that are timetables where certain subsets of trains need to start at almost (rather than precisely) the same minute of every period. The additional flexibility offered by quasi-periodic timetables turned out to be crucial in real-life scenarios characterized by elevated train traffic. We describe a MILP based approach for strategic quasi-periodic train timetabling and we test it on 4 different realistic what-if scenarios for an important line in Norway. The timetables produced by our algorithm were ultimately used by the Norwegian Railway Directorate to select 3 out of the 4 scenarios for phasing the progressive expansion of the JȪren line.
This data set contains the rail infrastructure data for Oslo Central Station, the station for which we developed an algorithm for optimal dispatching in . The infrastructure data was collected from restricted access schematics of the signaling and rail infrastructure in Oslo Central Station and on the connected lines. The data had to be collected by hand since the schematics were only available as images. The data set details most of the track infrastructure in the station but is not entirely complete when it comes to depots and other technical areas. The set contains a sample one-hour schedule based on actual traffic at the station. Based on these data, researchers can construct their own entirely realistic instances of the dispatching problem for large passenger stations, which they may, in turn, make freely available. This will allow the community to compare the performance of various approaches to the problem rigorously.
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