10,790 research outputs found

    The role of forebody topology on aerodynamics and aeroacoustics characteristics of squareback vehicles using Computational Aeroacoustics (CAA)

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    This study investigates the influence of forebody configuration on aerodynamic noise generation and radiation in standard squareback vehicles, employing a hybrid computational aeroacoustics approach. Initially, a widely used standard squareback body is employed to establish grid-independent solutions and validate the applied methodology against previously published experimental data. Six distinct configurations are examined, consisting of three bodies with A-pillars and three without A-pillars. Throughout these configurations, the reference area, length, and height remain consistent, while systematic alterations to the forebody are implemented. The findings reveal that changes in the forebody design exert a substantial influence on both the overall aerodynamics and aeroacoustics performance of the vehicle. Notably, bodies without A-pillars exhibit a significant reduction in downforce compared to their A-pillar counterparts. For all configurations, the flow characteristics around the side-view mirror and the side window exhibit an asymmetrical horseshoe vortex with high-intensity pressure fluctuations, primarily within the confines of this vortex and the mirror wake. Side windows on bodies with A-pillars experience more pronounced pressure fluctuations, rendering these configurations distinctly impactful in terms of radiated noise. However, despite forebody-induced variations in pressure fluctuations impacting the side window and side-view mirror, the fundamental structure of the radiated noise remains relatively consistent. The noise pattern transitions from a cardioid-like shape to a monopole-like pattern as the probing distance from the vehicle increases

    A Layered Organic Cathode for High-Energy, Fast-Charging, and Long-Lasting Li-Ion Batteries

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    Eliminating the use of critical metals in cathode materials can accelerate global adoption of rechargeable lithium-ion batteries. Organic cathode materials, derived entirely from earth-abundant elements, are in principle ideal alternatives but have not yet challenged inorganic cathodes due to poor conductivity, low practical storage capacity, or poor cyclability. Here, we describe a layered organic electrode material whose high electrical conductivity, high storage capacity, and complete insolubility enable reversible intercalation of Li+ ions, allowing it to compete at the electrode level, in all relevant metrics, with inorganic-based lithium-ion battery cathodes. Our optimized cathode stores 306 mAh g–1cathode, delivers an energy density of 765 Wh kg–1cathode, higher than most cobalt-based cathodes, and can charge–discharge in as little as 6 min. These results demonstrate the operational competitiveness of sustainable organic electrode materials in practical batteries

    Machine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions

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    Predicting FRP-to-concrete bond strength (FRP-CBS) under diverse exposure conditions is an intricate task influenced by multiple variables. Yet, existing pertinent models have several limitations. Accordingly, this study proposes a novel data driven machine learning (ML) methodology to predict the FRP-CBS considering various exposure conditions. A comprehensive database on single and double lap-shear strength tests on concrete specimens was meticulously compiled. Twenty-seven analytical models were used to appraise the developed ML models. Feature importance analysis was conducted to ascertain the influence of input parameters on bond strength. The proposed data-driven ML models attained exceptional accuracy and superior performance compared to existing analytical models. To enhance the accuracy of bond strength estimation and simplify the process for practicing engineers and FRP applicators, a user-friendly graphical interface was developed. It could eliminate the need for complex design procedures, making it easier to accurately estimate the FRP-CBS, thus improving overall efficiency in engineering practice

    Mesoporous RE<sub>0.5</sub>Ce<sub>0.5</sub>O<sub>2–<i>x</i></sub> Fluorite Electrocatalysts for the Oxygen Evolution Reaction

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    Developing highly active and stable electrocatalysts for the oxygen evolution reaction (OER) is key to improving the efficiency and practical application of various sustainable energy technologies including water electrolysis, CO2 reduction, and metal air batteries. Here, we use evaporation-induced self-assembly (EISA) to synthesize highly porous fluorite nanocatalysts with a high surface area. In this study, we demonstrate that a 50% rare-earth cation substitution for Ce in the CeO2 fluorite lattice improves the OER activity and stability by introducing oxygen vacancies into the host lattice, which results in a decrease in the adsorption energy of the OH* intermediate in the OER. Among the binary fluorite compositions investigated, Nd2Ce2O7 is shown to display the lowest OER overpotential of 243 mV, achieved at a current density of 10 mA cm–2, and excellent cycling stability in an alkaline medium. Importantly, we demonstrate that rare-earth oxide OER electrocatalysts with high activity and stability can be achieved using the EISA synthesis route without the incorporation of transition and noble metals

    Spectral clustering algorithm based web mining and quadratic support vector machine for learning style prediction in E-learning platform

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    A learning system, which is composed of a computer and the internet as the major elements, is termed an e-learning platform. It also promotes the education standard with the utilization of modern technology and equipment. Meanwhile, to enhance the standard of education significantly, it is important to predict the learning style of the users with the assistants of feedback and supervision. Nevertheless, it will avert the inherent correlation among e-learning behaviors. Hence, to predict the learning style automatically we propose a novel Spectral Clustering algorithm based Quadratic Support Vector Machine (E-SVM) approach. Our proposed approach employs two phases: (i) Utilizing the Web usage mining approach the learning secrets are extracted from the log files of learners. (ii) The classification of learning styles of learners is effectuated with the proposed approach. Experiments are demonstrated with Python package and analyzed the performance. For simulation, we have utilized real-time dataset and compared the results with the state-of-art approaches. Our approach surpasses all the other approaches

    Sustainable tourism progress: a 10-year bibliometric analysis

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    AbstractSustainability in tourism has become one of the concerns of the travel industry. More research is required to establish a scientific basis for Sustainable Tourism (ST). In order to develop the field structure on sustainable tourism from 2013 to 2023, the study examines the existing situation using bibliometric analysis. The ‘Scopus’ database was explored on the ‘sustainable tourism’ theme in order to achieve the goal. The original search yielded a total of 701 papers, which were subsequently filtered to 619 documents. For the articles relating to ST, the most cited papers, significant authors, co-citation of references, and sources were all looked into. This study found that research into sustainable tourism has grown recently. Three research with 1512 citations were found by the authors, and they also found two publications with over 500 ‘Scopus’ citations. The study highlights the gap and identifies the conceptual sub-domains, such as biodiversity, economic, environmental issues, and local community engagement, which might be crucial in subsequent studies

    Enhanced anomalous Nernst effects in ferromagnetic materials driven by Weyl nodes

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    Based on high-throughput (HTP) first-principles calculations, we evaluated the anomalous Hall and anomalous Nernst conductivities of 266 transition-metal-based ferromagnetic compounds. Detailed analysis based on the symmetries and Berry curvatures reveals that the origin of singular-like behavior of anomalous Hall/Nernst conductivities can be mostly attributed to the appearance of Weyl nodes or nodal lines located in the proximity of the Fermi energy, which can be further tailored by external stimuli such as biaxial strains and magnetic fields. Moreover, such calculations are enabled by the automated construction of Wannier functions with a success rate of 92%, which paves the way to perform accurate HTP evaluation of the physical properties such as the transport properties using the Wannier interpolation

    Capacity and needs assessment of veterinary services in Vietnam in biosecurity, biosafety and One Health.

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    The Asia-Pacific region is recognised as an epicentre of emerging infectious diseases (EIDs), of which 75% are zoonotic in nature. Vietnam is recognised as a potential hotspot for zoonotic EIDs. There is a growing recognition that progress towards global health security requires greater focus on collaboration between the human health and animal health sectors to control diseases at their animal source and prevent against human health impacts. Assessment of veterinary epidemiology capacity in Vietnam is paramount to strengthening the health security of Asia-Pacific. This study aims to evaluate the national capacity and needs of veterinary services in Vietnam in biosecurity, biosafety and One Health. A cross-sectional, convergent mixed-methods study was conducted between November 2020 and April 2021. An online questionnaire was administered to government-employed field veterinarians. Descriptive analyses and logistic regression models were performed using survey responses to understand capacity in the field. Semi-structured interviews were also conducted with stakeholders in veterinary services including government, academia, research institutes, non-profit and international organisations. Coding and thematic analysis using a deductive approach was used for data collected from interviews to contextualise findings from the survey and understand institutional capacity. In total, 178 field veterinarians completed the online survey and 25 stakeholders were interviewed. The majority of participants had reported receiving training in biosecurity and biosafety, including use of personal protective equipment. Most respondents reported practicing good biosecurity measures (92%) and good biosafety measures (88%). Physical and socioeconomic barriers to practicing biosecurity were reported to be prevalent for smallholder farmers, which may suggest a gap in the capacity of veterinary services to provide cost-effective and practical biosecurity strategies. Seventy five percent of participants had never or rarely participated in One Health approaches in the field in the last 12 months and 69% reported further training as a high priority. There was a knowledge gap reported amongst district and commune-level veterinary staff about the need for, and awareness of multisectoral collaboration. Respondents that completed postgraduate qualifications in epidemiology or Field Epidemiology Training Programs (adjusted OR: 3.06; 95% CI: 1.01, 9.23, p = 0.046) and had longer job tenure between 10-12 years (OR: 10.38; 95% CI: 3.06, 35.15, p = <0.001) were more likely to have higher levels of experience in One Health. This study identified gaps in knowledge, attitudes and adoption of practices related to biosecurity, biosafety and One Health specifically in lower-level or less experienced veterinary staff without further training opportunities in epidemiology. These findings enable prioritisation of training, policy, and planning activities to further enhance the national capacity of veterinary services in Vietnam
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