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Comparing colonial legacies in contemporary global queer politics: colonialities and resistances
No full text
HyProv: Hybrid Provenance Management for Scientific Workflows
No full textProvenance plays a crucial role in scientific workflow
execution, for instance by providing data for failure analysis,
real-time monitoring, or statistics on resource utilization for rightsizing allocations. The workflows themselves, however, become
increasingly complex in terms of involved components. Furthermore, they are executed on distributed cluster infrastructures,
which makes the real-time collection, integration, and analysis of
provenance data challenging. Existing provenance systems struggle
to balance scalability, real-time processing, online provenance
analytics, and integration across different components and
compute resources. Moreover, most provenance solutions are
not workflow-aware; by focusing on arbitrary workloads, they
miss opportunities for workflow systems where optimization and
analysis can exploit the availability of a workflow specification
that dictates, to some degree, task execution orders and provides
abstractions for physical tasks at a logical level.
In this paper, we present HyProv, a hybrid provenance
management system that combines centralized and federated
paradigms to offer scalable, online, and workflow-aware queries
over workflow provenance traces. HyProv uses a centralized
component for efficient management of the small and stable
workflow-specification-specific provenance, and complements this
with federated querying over different scalable monitoring and
provenance databases for the large-scale execution logs. This
enables low-latency access to current execution data. Furthermore,
the design supports complex provenance queries, which we
exemplify for the workflow system Airflow in combination with
the resource manager Kubernetes. Our experiments indicate that
HyProv scales to large workflows, answers provenance queries
with sub-second latencies, and adds only modest CPU and memory
overhead to the cluster
Artificially intelligent framework for multi-output performance prediction in diverse solid oxide electrolyzer cells for green hydrogen production plants
No full textHydrogen sourced from renewable resources has been increasingly regarded as a promising alternative chemical energy carrier to fossil fuels, with support being given to efforts for climate change mitigation and the achievement of net-zero emission targets. Solid oxide electrolyzer cells (SOECs) operating in higher temperatures have shown greater system-level efficiency in comparison to other types of electrolyzers, making them a promising option for mass production of hydrogen. However, diverse SOEC architectures—such as varying anode and cathode materials—and wide operating ranges make it difficult to develop a single, multi-output, multi-parameter, broadly applicable performance model. In this paper, artificial intelligence (AI) techniques have been applied to SOEC data to develop an advanced model based on a cascade-forward neural network (CFNN) that holistically captures these design and operating variables. The resulting CFNN model reliably predicts ohmic resistance, current density, and hydrogen production rate with coefficients of determination (R2) of 0.9981, 0.9752, and 0.9878, respectively, in comparison with experimentally sourced testing data and across a wide range of SOEC configurations and conditions. Essentially, this paper delivers a highly accurate and computationally efficient digital surrogate model based on an AI technique, enabling smarter design, operation, and scaling of SOECs, and contributing to broader decarbonization goals through cost-effective and efficient sustainable hydrogen production
Multilayered capillary barrier systems: analytical, numerical, and experimental study
Get PDFCapillary barrier systems (CBSs) are geotechnical structures that are used to limit rainwater infiltration into the underlying soil. The conventional layout of CBSs consists of a finer-grained layer overlying a coarser-grained layer. The use of multilayered CBSs (MCBs) consisting of multiple pairs of finer and coarser layers can be used to improve the water storage capacity (WSC), and hence the effectiveness, of these systems. Four column infiltration tests with a slow infiltration rate of 1.3×10−7 m/s were carried out and these demonstrated (1) that a significant gain in WSC can be achieved by using MCBs compared to conventional single CBSs (up to 40% in the specific tested cases); and (2) the existence of an optimum number of layers that maximizes the gain in WSC (three pairs of finer- and coarser-grained layers in the specific tested cases). Although values of WSC achieved if the finer layer material is relatively coarse will always be lower than those achieved if the finer layer material is finer, the difference can be reduced by the use of multilayered CBSs. A simple analytical method for the design of horizontal MCBs is proposed and then validated by comparison with the results of the column infiltration tests and rigorous FE analyses. A parametric analysis was undertaken to examine the effect of the number and thickness of layers, materials, and infiltration rate on the water storage capacity of horizontal MCBs
The Future of Accounting and Finance: Embracing Technology, Digitalisation, Sustainability, Education, and Employability
No full textThe integration of cutting-edge technologies – such as Artificial Intelligence (AI) and Machine Learning (ML) – combined with a growing emphasis on sustainability and the Sustainable Development Goals (SDGs), is profoundly transforming the business landscape in general, and the fields of Accounting and Finance in particular. This edited volume presents a comprehensive collection of expert insights, research findings, and reflective essays that examine the evolving landscape of Accounting and Finance research, education, and professional practice.
Bringing together diverse perspectives from various scholars worldwide, the book offers readers a deeper understanding of how technology, digitalization, innovation, sustainability, and evolving pedagogical approaches are reshaping the Accounting and Finance domain. Rather than predicting a future in which automation replaces human expertise, it highlights the changing role of Accounting and Finance professionals – emphasizing the need for new skill sets and a mindset that embraces technological advancement while aligning with sustainability goals. This volume serves as a timely and essential resource for anyone seeking to navigate – and contribute to – the profound transformations underway in the Accounting and Finance field
Seamless short- to mid-term probabilistic wind power forecasting
No full textThis paper brings a new understanding to the relative importance of different uncertainty sources across forecast horizons up to 7 days ahead. It presents a method for probabilistic wind power forecasting that quantifies uncertainty from weather forecasts and weather-to-power conversion separately. The study reveals that weather-to-power uncertainty is more significant for short-term forecasts, while weather forecast uncertainty dominates mid-term forecasts, with the transition point varying between wind farms. Offshore farms typically see this shift at shorter lead times than onshore. By addressing both uncertainty sources, the proposed forecast method achieves state-of-the-art results for lead times of 6 to 162 h, eliminating the need for separate models for short- and mid-term forecasting. Importantly, it also significantly improves short-term forecasts during high weather uncertainty periods, where methods based on deterministic weather forecasts dramatically underestimate total uncertainty. The findings are supported by an extensive, reproducible case study comprising 73 wind farms in Great Britain over 5 years
Genotype-phenotype correlations in a Scottish CADASIL cohort and comparison with sporadic small vessel disease
No full textIntroduction:
CADASIL is a monogenic inherited cerebral small vessel disease (SVD) caused by a mutation affecting the NOTCH3 gene. Mutation location appears to influence disease severity. We investigated the hypothesis that mutation location modifies phenotype by comparing a CADASIL population stratified by mutation site risk with a cohort of older people with sporadic SVD.
Patients and methods:
We included adults with CADASIL and control group from the XILO-FIST trial. We recorded age at first stroke, white matter hyperintensity (WMH) volume, lacunes, cerebral microbleeds and other clinical biomarkers. We divided the CADASIL cohort into (1) two groups NOTCH3 mutations affecting epidermal growth factor-like repeat (EGFr) domains 1–6 (proximal) and EGFr domains 7–34 (distal); and (2) three groups; low, medium and high-risk based on a proposed three-tiered risk stratification.
Results:
The CADASIL cohort included 129 people, 57 (44.2%) male, mean age 47.5 ± 11.7 years. The sporadic SVD cohort included 460 people, 317 (68.9%) male, mean age 65.7 ± 8.7 years. The CADASIL proximal group were imaged at younger age, but fewer had hypertension (14.3% v 38.1%) compared to distal mutations. Lacune count and WMH volume differed between low, medium and high-risk CADASIL mutations, and sporadic SVD. Percentage progression of WMH volume was higher in proximal CADASIL (0.26%), than distal CADASIL (0.14%) which was higher than sporadic SVD (0.05%), p < 0.001.
Discussion and conclusion:
Proximal CADASIL mutations average more extensive WMH, higher lacune count and experienced first stroke at younger age than those with distal mutations. Both groups showed imaging differences compared to sporadic SVD
Real-Time CFD in the simulation of aerial firefighting
Get PDFAerial firefighting is crucial in the combat of wildfires, as the speed, range, and payload (water, fire-retardant, crew) advantages provided by aircraft enable much easier access to remote areas and much faster response to active fires than ground vehicles. However, the challenging mission scopes and flight conditions, including low-altitude operations, smoke-induced low visibility, fire-induced turbulence, and complex terrain, encountered during these operations place the pilot and crew under high workload and at considerable risk of accidents. With the increased frequency and intensity of wildfires seen in recent years due to climate change and other factors, research into safer and more efficient aerial firefighting techniques, strategies, and training is essential. This paper presents a high-fidelity simulation environment, leveraging real-time computational fluid dynamics (CFD) in the loop and capable of evaluating different aerial platforms and firefighting techniques
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