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Experimental investigation of process parameters for hydrogen-rich syngas production from rice husk gasification
Get PDFThe overconsumption of conventional fossil fuel by the energy and petrochemical sectors demands for the development of alternate renewable sources. Biomass is found to be an effective alternate renewable energy source for the carbon-neutral production of energy and chemicals. Rice husk, one of the most common and abundant lignocellulosic biomass in Asia, is used in the present work as potential feedstock for the thermochemical conversion through gasification process in a lab-scale fixed bed (downdraft) reactor. In this work, the influence of various parameters such as the physico-chemical properties of the biomass, the temperature of gasifier, the size of the particles, the steam flow rate, equivalence ratio (ER) and gasifying agents such as mixture of (air + steam) and steam alone were investigated. The (air + steam) mixture at an ER of 0.27, steam flow rate of 0.775 ml.min‾¹, and the reactor temperature at 950 °C, yielded the highest hydrogen of ∼40 % (vol.) and carbon monoxide of ∼12 % (vol.) with a high heating value (HHV) of ∼7 MJ.m‾³. In contrast, when the experiments were conducted using steam alone as the gasifying agent at an ER of 0.24 with steam flow rate 0.6 ml.min‾¹ and the reactor temperature 950 °C, the produced syngas reported to have a HHV of ∼11 MJ.m‾³ with hydrogen and carbon monoxide content of ∼70 % (vol.) and ∼10 % (vol.), respectively. The outcome shows that steam is a better gasifying agent for production of hydrogen rich syngas as compared to the (air + steam) gasifying mixture. The results also show that higher temperature favors hydrogen production however, a significant decrease in the HHV of the syngas was observed at elevated temperatures. Furthermore, the hydrogen conversion efficiency and energy conversion efficiency were calculated and were found to be 81 and ∼70 %, respectively. Consequently, the produced syngas has the potential to be utilized as a renewable fuel in the industrial sector
An experimental and numerical investigation into tensile fatigue failure of composite laminates containing wrinkles and cut plies
Get PDFFibre-reinforced polymer composites are generally seen as more fatigue resistant than metals. However, layup features such as discontinuous plies and/or manufacturing-induced defects such as wrinkles, can initiate fatigue damage and reduce the overall performance of composites. Through an extensive experimental programme and an advanced progressive damage model, this paper investigates the influence of defects and features, both in isolation and in combination, on the tensile static and fatigue performance of quasi-isotropic IM7/8552 laminates. The numerical model describes delamination and intralaminar matrix cracking using cohesive elements that follow a mixed-mode static and fatigue formulation. Experiments and modelling correlate well regarding both the ultimate static strength and S-N curves. The ultimate static strength shows a clear decreasing trend in the order of pristine, wrinkle, cut-ply and combined cut-ply & wrinkle. The cut-ply and wrinkle plus cut-ply show a similar influence on the fatigue life; both are more detrimental than wrinkle. The decrease of the fatigue life due to the defects/features can be up to two decades at a loading level corresponding to half of the pristine ultimate strength. This study implies that, for the IM7/8552 material, a wrinkle in a region of terminating plies does not add significant reduction to fatigue life
LLM-based task offloading and resource allocation in satellite edge computing networks
Get PDFSatellite Mobile Edge Computing (MEC) networks offer a promising solution for delivering global services to terrestrial Internet of Things (IoT) terminals in 5 G and beyond. However, satellite MEC systems face challenges such as underutilization of resources and task congestion, leading to resource waste and increased latency. In this paper, we investigate the joint resource allocation and task offloading problem in multi-satellite MEC networks, aiming to minimize the average latency of IoT terminals. To solve the joint optimization problem involving IoT terminals' task offloading decisions, uplink transmission power and sub-channel allocation, and satellite computation resource allocation, we propose an iterative optimization algorithm that uses the Lagrange multipliers method to optimize the satellite computation resource allocation and a Large Language Model (LLM) based optimizer to optimize the other variables in each iteration. Prompts and templated parameters are designed to enhance the LLM's inference accuracy and generalization capability across scenarios with varying numbers of satellites and IoT terminals. Simulation results show that our proposed LLM-based algorithm outperforms benchmark algorithms in convergence speed and average latency of IoT terminals
Supporting mainstream school staff in England to meet the needs and address educational inequalities in autistic girls
Get PDFAutistic girls often experience educational inequalities and inadequate support in schools that lead to poor educational, health and wellbeing outcomes. In response, education policy requires mainstream schools to adopt inclusive practices for children and young people (CYP) with special educational needs and disabilities, including autistic CYP, where possible. However, teachers report complex barriers to inclusive practice provision, including a lack of training and resources, mental health deterioration among CYP post-pandemic and a lack of how to support autistic girls to engage in mainstream education. This study adopted a neurodiversity approach to explore challenges that 17 mainstream school staff faced and provides recommendations for inclusive practices for autistic girls in mainstream schools. Three themes and one subtheme were developed through thematic analysis of interview and group interview data: (1) Ineffective and inconsistent training; (2) Time as a barrier (subtheme: Pastoral support demands and mental exhaustion); (3) Future training recommendations. Future research should explore how to tailor training to staff needs and the specific needs of ethnic minorities and LGBTQ+ autistic girls. Recommendations will have practical significance in advancing understanding of action that must be taken in schools and by policymakers to achieve inclusive practices for autistic girls in mainstream education settings
Towards automated chemical analysis of materials using secondary electron hyperspectral imaging and unsupervised learning
Get PDFAdvancements in materials science have significantly transformed materials discovery and advanced manufacturing. This, along with the rapid development of sensing and instrumentation, results in a continuous increase in data volumes. To address the limitations of conventional manual analysis, this paper introduces an AI-driven framework for high-throughput chemical analysis of material surfaces at the micro- and nano-scale. The framework integrates unsupervised machine learning with secondary electron hyperspectral imaging (SEHI). It consists of four stages: hyperspectral image processing via tiling, spectral peak extraction, peak categorisation by probabilistic clustering, and chemical analysis. Tiling enables the capture of local spatial-spectral information and generation of a large number of training samples from a single SEHI image stack. After tile-wise spectral peak extraction, the distribution of the peak positions is accurately represented by probabilistic clustering with a Gaussian mixture model (GMM) or a Dirichlet process Gaussian mixture model (DPGMM). Each peak corresponds to a specific chemical bond or element in a material, reflecting the unique spectral characteristics. The performance of the GMM and GPGMM approaches is validated over a case study for identifying chemical elements or bonds of complex metal alloy and carbon films. The results demonstrate accurate chemical analysis, yielding relative errors within ±15% compared to the theoretical model of the valence band density of states. This work is a step forward towards automated material analysis across different tasks such as identifying chemical elements and bonds, visualizing surface (in)homogeneity in metal alloy films for guiding film printing, and supporting digital twins integration for advanced manufacturing
Our place in the agentic AI loop: the value of information professional competencies
No full textPurpose
This study aims to analyse the concept of agentic artificial intelligence (AI) and the relevance of information professional competencies to it.
Design/methodology/approach
The paper is based on literature and a small-scale testing of the deep research services from Gemini and ChatGPT.
Findings
The eight promises that are made in the discourse around agents are elaborated, with the questions an information professional would ask. The main features of deep research agents are analysed. Information professionals are likely to be concerned about the sources of information in use and the reliability of outputs, and the wider societal impacts such as on the environment.
Research limitations/implications
The author questions how far the promises of the discourse around agents can be really delivered.
Practical implications
Information professionals should be involved in configuration of agents and training of users.
Originality/value
The paper is an early review of the trending agent/agentic AI concept
Philanthropy, agriculture and social development:lessons from the long Green Revolution
No full textThis chapter explores the role of large US foundations in shaping agricultural development policy and practice over the last 70 years. Their postwar vision of a “Green Revolution” to modernise developing country agriculture has been sustained throughout successive development eras through evolving combinations of technical, market and policy ‘fixes’ that have aligned domestic policies and farmer practices ever more closely with priorities of transnational agribusiness. Increasing support among development agencies for integrating social protection into agricultural development has opened policy space to advocate for transformative approaches that go beyond the roll-out of digital transfers championed by elite foundations, among others, to create synergies between social protection and entitlements to food. Meanwhile, recent insights into tensions between conventional agricultural productivity goals and properties of resilient, “climate-wise” farming systems highlight the need for further research on informal social protection mechanisms in different agroecological contexts, and how these might be integrated into formal interventions
Investigation of mechanical and fresh properties of ultra-high-performance concrete incorporating second-generation superplasticizers
Get PDFUltra-high-performance concrete (UHPC) has been following economic and environmental trends for the past two decades. Limited research has been conducted on the significance of superplasticizers in UHPC products, despite the high costs they entail for projects. The current study assesses UHPC based on rheological properties and mechanical characteristics considering different factors. In this study, the effects of different levels of superplasticizer derived from sulfonated naphthalene formaldehyde (SNF: 0.7%, 0.8%, and 0.9%), silica fume (SF: 15%, 20%, and 25%), and the water-to-binder ratio (w/b: 0.18, 0.20, and 0.22) were examined. Fresh tests such as slump flow, Vicat needle, and squeezing, as well as hardened tests like compressive strength, flexural strength, and electrical resistivity, were conducted. In the analysis, an artificial neural network (ANN) model and a fuzzy logic (FL) model were employed to forecast compressive strength results at 7 and 28 days. The results indicated that a higher SF dosage reduced slump flow and set time, whereas the opposite was observed for SNF and the w/b ratio. Three distinct behaviors were identified in the squeezing flow test findings: (1) specific elastic behavior and low plasticity, (2) extensive plastic behavior and significant dilatancy, and (3) heightened responsiveness to compressive flow rate and material ratio. SNF demonstrated promise in enhancing compressive, flexural, and electrical strength. The prediction models suggested that the FL (error range 3.18–4.36%) and ANN (0.74–1.03%) models performed well in predicting compressive strength at 7 and 28 days. The encouraging findings from this study set the stage for further sustainable and cost-effective construction methods
Editorial: Microbial ecology supporting growth of free-living amoebae in natural and engineered water systems
Get PDFEntanglement and apparent thermality in simulated black holes
Get PDFWe investigate the apparent thermality of Hawking radiation in the semi-classical limit of quantum black holes using the mean-field limit of a chiral spin-chain simulator, which models fermions propagating on a black hole space-time in the continuum. In this free-theory regime, no genuine thermalisation occurs. Nevertheless, we show that a bipartition across the event horizon yields a reduced density matrix whose mode occupations follow an apparent thermal Fermi-Dirac distribution. In contrast, partitions away from the horizon do not exhibit thermal behaviour, reflecting the absence of true equilibration. Our results demonstrate that Hawking radiation appears thermal only with respect to horizon bipartitions in free theories, while true thermal behaviour emerges only in the presence of interactions deep in the black hole interior
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