38517 research outputs found
Sort by
Synthesis of graphene oxide and graphene quantum dots from miscanthus via ultrasound-assisted mechano-chemical cracking method
Get PDFWhilst graphene materials have become increasingly popular in recent years, the followed synthesis strategies face sustainability, environmental and quality challenges. This study proposes an effective, sustainable and scalable ultrasound-assisted mechano-chemical cracking method to produce graphene oxide (GO). A typical energy crop, miscanthus, was used as a carbon precursor and pyrolysed at 1200 ◦C before subjecting to edgecarboxylation via ball-milling in a CO2-induced environment. The resultant functionalised biochar was ultrasonically exfoliated in N-Methyl-2-pyrrolidone (NMP) and water to form GOs. The intermediate and endproducts were characterised via X-ray diffraction (XRD), Raman, high-resolution transmission electron microscopy (HR-TEM) and atomic force microscopy (AFM) analyses. Results show that the proposed synthesis route can produce good quality and uniform GOs (8–10% monolayer), with up to 96% of GOs having three layers or
lesser when NMP is used. Ultrasonication proved to be effective in propagating the self-repulsion of negativelycharged functional groups. Moreover, small amounts of graphene quantum dots were observed, illustrating the potential of producing various graphene materials via a single-step method. Whilst this study has only investigated utilising miscanthus, the current findings are promising and could expand the potential of producing good quality graphene materials from renewable sources via green synthesis routes
Artificial neural network system for cell classification using single cell RNA expression
Get PDFWe implemented an automated system for single-cell classification using artificial neural networks (ANN). Our system takes single-cell gene expression sparse matrices and trains ANN to classify cell types and subtypes. The assemblies of ANNs predict cell classes by voting. We tested the system in a case study where we trained ANNs with a dataset containing approximately 120,000 single cells and tested the resulting model using an independent data set of 13,000 single cells. The overall accuracy of the 5-class classification was 95%. We trained and tested a total of 100 ANNs in 10 cycles. The prediction system demonstrated excellent reproducibility. The analysis of misclassifications indicated that 2% were likely classification errors, while the remaining 3% were likely due to mislabeled types and subtypes in the test set
Novel prior position determination approaches in particle filter for Ultra Wideband (UWB)-Based indoor positioning
Get PDFFiltering‐based indoor positioning using ultra wideband (UWB) requires known velocity to predict prior position in the prediction stage. Velocity can be obtained from an inertial measurement unit (IMU) sensor or the posterior state vector at the previous time stamp. Both methods have limitations when using them in practice. This paper proposes two novel velocity determination approaches, which use measurements to approximate velocity in a self‐contained way. They are integrated into particle filtering algorithms for prior position determination. The test result shows that the particle filter with the proposed approaches performs similarly to the Rao‐Blackwellized particle filter and slightly better than the particle filter with IMU. Compared with the standard particle filter, the particle filters with our proposed approaches achieve similar positioning accuracies with less computation time. Moreover, it is found that the integration of Angle‐of‐Arrival measurements in particle‐filter‐based positioning improves the 3‐D positioning accuracy by about 37.3% on average
Mental health consequences of COVID-19 media coverage: the need for effective crisis communication practices
Get PDFDuring global pandemics, such as coronavirus disease 2019 (COVID-19), crisis communication is indispensable in dispelling fears, uncertainty, and unifying individuals worldwide in a collective fight against health threats. Inadequate crisis communication can bring dire personal and economic consequences. Mounting research shows that seemingly endless newsfeeds related to COVID-19 infection and death rates could considerably increase the risk of mental health problems. Unfortunately, media reports that include infodemics regarding the influence of COVID-19 on mental health may be a source of the adverse psychological effects on individuals. Owing partially to insufficient crisis communication practices, media and news organizations across the globe have played minimal roles in battling COVID-19 infodemics. Common refrains include raging QAnon conspiracies, a false and misleading “Chinese virus” narrative, and the use of disinfectants to “cure” COVID-19. With the potential to deteriorate mental health, infodemics fueled by a kaleidoscopic range of misinformation can be dangerous. Unfortunately, there is a shortage of research on how to improve crisis communication across media and news organization channels. This paper identifies ways that legacy media reports on COVID-19 and how social media-based infodemics can result in mental health concerns. This paper discusses possible crisis communication solutions that media and news organizations can adopt to mitigate the negative influences of COVID-19 related news on mental health. Emphasizing the need for global media entities to forge a fact-based, person-centered, and collaborative response to COVID-19 reporting, this paper encourages media resources to focus on the core issue of how to slow or stop COVID-19 transmission effectively
Investigating the environmental effect of globalization: insights from selected industrialized countries
Get PDFDespite the burgeoning literature on the globalization-environmental degradation nexus, this area of empirical interest is still riddled with ambiguity. Thus, based on an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model, we re-investigate the effect of globalization on environmental degradation for 27 selected industrialized countries over the period 1991-2016. More specifically, we shed light into how overall globalization and its various components – economic, social and political globalization – affect environmental degradation. We advance existing literature by considering a measurement approach which disaggregates overall, economic, social and political globalization into their de facto and de jure aspects. Using the augmented mean group estimator, we find that overall and economic globalization reduce environmental degradation while social and political globalization do not exert any significant effect on globalization. With respect to the de facto and de jure aspects, we observe that, while only de facto economic globalization mitigates environmental degradation, de jure overall, economic and social globalization also dampen environmental degradation. We provide some policy implications in the end
Challenging the commodification of human rights: the case of the right to housing
Get PDFThe profitability of commodified housing is driving extreme levels of corporate investment. To boost profits investors are exploiting “undervalued” low-income housing, evicting vulnerable individuals, hoarding land and charging exploitative fees. This is causing severe harm to individuals’ right to housing across the globe, including, inter alia, rapidly increasing prices and debt, increasing evictions, homelessness, and increased recourse to substandard accommodation. The harm is endemic, but the human rights response has been tepid.
This paper argues that both state obligations and the content of the right to housing under the International Covenant on Economic, Social and Cultural Rights (ICESCR) can usefully address the problem. However, in communications with State Parties the Committee on Economic, Social and Cultural Rights (CESCR) addresses issues of commodification and affordability in vague terms that fail to generate meaningful obligations. The paper grounds the CESCR’s approach in theories of enforceability which argue that enforcement is more practicable when “clear violations” can be established. The CESCR offers clear statements of breach only when identifying explicitly wrongful practices, such as discriminatory laws. This approach, however, almost entirely occludes harm caused by the marketization of human rights. It skeletonizes the “protect” limb of state obligations, permits the long-term retrogression of affordability and enables the serious subsequent effects. The paper proposes that “clear violations” can be constructed from the results of, and laws constituting, harmful marketization. A three-stage process of identification of breach, standard-setting, and policy suggestions is recommended that can turn the long-term retrogression of access to housing into specific, measurable statements of violations and recommendations. This same approach is advocated for business responsibilities under the UN Guiding Principles on Business and Human Rights, with the content of these responsibilities also evaluated
Who looks after the kids? the effects of childcare choice on early childhood development in China*
Get PDFThis paper examines whether childcare choice affects the early childhood development of children aged 7–59 months. Using the data from Chinese Family Panel Studies, we look at household choices between parental and grandparental cares and the timing of four key early life achievements – walking, talking, counting and toilet training. We conceptualize early childhood development within a household production model, which enables us to identify the impacts of childcare. Our results suggest that compared with parental care, grandparental care delays the achievement of all four outcome measures. Grandparental care is particularly disadvantageous for children who are ‘left-behind’ by migrant parents
A hybrid medical text classification framework: integrating attentive rule construction and neural network
Get PDFThe main objective of this work is to improve the quality and transparency of the medical text classification solutions. Conventional text classification methods provide users with only a restricted mechanism (based on frequency) for selecting features. In this paper, a three-stage hybrid method combining the threshold-gated attentive bi-directional Long Short-Term Memory (ABLSTM) and the regular expression based classifier is proposed for medical text classification tasks. The bi-directional Long Short-Term Memory (LSTM) architecture with an attention layer allows the network to weigh words according to their perceived importance and focus on crucial parts of a sentence. Feature words (or keywords) extracted by ABLSTM model are utilized to guide the regular expression rule construction. Our proposed approach leverages the advantages of both the interpretability of rule-based algorithms and the computational power of deep learning approaches for a production-ready scenario. Experimental results on real-world medical online query data clearly validate the superiority of our system in selecting domain-specific and topic-related features. Results show that the proposed approach achieves an accuracy of 0.89 and an F1-score of 0.92 respectively. Furthermore, our experimentation also illustrates the versatility of regular expressions as a user-level tool for focusing on desired patterns and providing interpretable solutions for human modification
Intelligent handover triggering mechanism in 5G ultra-dense networks via clustering-based reinforcement learning
Get PDFUltra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs
Active thermal control for modular power converters in multi-phase permanent magnet synchronous motor drive system
Get PDFModular winding structure has been employed in the Permanent Magnet Synchronous Motors (PMSMs) to increase the reliability and reduce the torque ripple. Nevertheless, the reliability of the motor system depends on the lifetime of the power semiconductor devices. Since the thermal cycles, which can generate the mechanical stress between the different material layers in power devices, are the key factors to influence the lifetime of power devices, in this paper, an Active Thermal Control (ATC) for modular power converters in PMSM drive is proposed to extend the system lifetime. The power routing method is employed to balance the power in a quadruple modular winding PMSM system. The Rainflow Counting Algorithm is used to calculate the thermal cycles with a load mission profile, and estimate the lifetime of the power converters. The proposed method is validated by both simulation and experiments
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇