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Supervision of public health and social care organizations – frame analysis of actions of organizations
No full textInvestigation of gold and silver based hybrid nanofluid with effects of thermal radiation over a stretching sheet
No full textThe low heat conductivity of base fluids poses significant challenges, leading to growing interest in nanofluids, which have shown promise in improving heat transfer compared to conventional fluids. A nanofluid refers to a fluid that contains nanoparticles uniformly distributed within a base fluid, prepared using specialized techniques to ensure stability and long-term performance, making them distinct from conventional mixtures. Freshwater, refrigeration, glycol, or heating oil are examples of base fluids. The base fluid in this investigation is ethylene glycol mixed with hybrid nanoparticles of graphene oxide, gold, and silver. This study aims to examine the Magnetohydrodynamic (MHD) behavior of hybrid nanofluids over a stretching sheet when exposed to heat radiation and a magnetic field. The basic equations undergo appropriate similarity transformations to convert them from nonlinear higher-order partial differential equations to nonlinear ordinary differential equations. The bvp4c method, an inherent function in MATLAB, is employed to solve nonlinear ordinary differential equations (ODEs). The influence of multiple relevant factors, such as thermal radiation, Prandtl number, slip parameter, Biot number, magnetic field, melting parameter, Nusselt number and skin friction against velocity and thermal profiles is being exemplified through the graphs and tables. Velocity decreases with magnetic and slip parameters, while the melting parameter increases it but reduces the thermal profile. Thermal radiation, Biot number, and the presence of a heat source or sink enhance the thermal profile, whereas higher Nusselt numbers and skin friction reduce it while improving convective heat transfer. The study demonstrates a 29.6 % enhancement in the Nusselt number for hybrid nanofluids compared to conventional fluids as the Prandtl number rises from 2.2 to 3.2. Moreover, the heat transfer performance of hybrid nanofluids surpasses that of traditional nanofluids. The results align well with existing literature, confirming their reliability and consistency. This study holds potential benefits for various fields, including applications in extreme temperature environments, medical devices, metal coatings, biosensors, and aerospace technology. The use of hybrid nanofluids has proven to be instrumental in enhancing temperature regulation and improving fuel efficiency across diverse sectors such as automotive and energy industries
Towards Equitable Climate Governance : strengthening the role of climate victims in addressing loss and damage under the international climate regime
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A foreign-language patient and equity of care in a hospital environment: Systematic literature review
No full textAssociations of serum amino acids with blood glucose metabolism following lean fish, fatty fish, and camelina sativa oil consumption in adults with impaired glucose metabolism : A Randomized Controlled Trial - AlfaFish study, Kuopio, Finland
No full textClassroom teachers' experiences of work well-being and strategies for setting boundaries
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Human posture recognition using random search neural architecture for accident injury severity prediction and victim identification
No full textNumerous lives are lost due to the ignorance of the victim's conditions and the injury severity in road accidents. In developing countries like India, the challenges of emergency responders are identifying and prioritizing victims and their injuries amid chaotic environments. In this paper, a systematic approach for accident injury severity and victim identification using human posture recognition and instance segmentation is proposed. To overcome the challenge of fixed architectures limiting the adaptability to diverse accident scenarios, Random Search Neural Architecture Search (RNAS) is adapted to automatically find an optimal Convolutional Neural Network (CNN). To enhance the efficiency and accuracy of identifying victims in accident scenes, Mask RCNN, an instance segmentation technique trained over accident images, has been used. By leveraging computer vision techniques, an automated accident injury severity and victim identification system facilitating more timely decision-making for the emergency response systems is designed. The model has been experimented with and evaluated, and the introduction of random search neural architecture has determined a computationally less expensive CNN model. The model can produce an accuracy of 95% in recognizing the human posture. Further, Mask RCNN is trained, experimented with, and validated on accident images to produce 0.99 mAP in identifying victims
