The phenomenon of urbanization in Indonesia is inevitable. The new residential and economic centers in suburban areas is also a problem in city development. The gradual planning and development of smart cities in a li...
The phenomenon of urbanization in Indonesia is inevitable. The new residential and economic centers in suburban areas is also a problem in city development. The gradual planning and development of smart cities in a living lab require consideration of the right location for a living lab. This study wants to show that suburban areas as city buffer zones can become living laboratories for smart city development. The diversity of situations in cities and regencies across Indonesia, the potential for resources, and the problems faced are the challenges of developing a living lab - Garuda Smart City Framework. This research uses the method of reviewing the literature of research publications for the last five years (2019–2023) to obtain information on Smart City development in Indonesia. We collected selected articles from databases in Google Scholar, IEEE, and Scopus using the Publish and Perish 8. The search keywords used were garuda AND Smart city AND Framework. The findings show the potential and dynamics of buffer zones to become appropriate living laboratories. Smart city regional planning can more holistically involve neighborhoods and address urban issues.
The interaction between cell surface receptors and extracellular ligands is highly related to many physiological processes in living *** techniques have been developed to measure the ligand-receptor binding kinetics a...
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The interaction between cell surface receptors and extracellular ligands is highly related to many physiological processes in living *** techniques have been developed to measure the ligand-receptor binding kinetics at the single-cell ***,few techniques can measure the physiologically relevant shear binding affinity over a single cell in the clinical ***,we develop a new optical technique,termed single-cell rotational adhesion frequency assay(scRAFA),that mimics in vivo cell adhesion to achieve label-free determination of both homogeneous and heterogeneous binding kinetics of targeted cells at the subcellular ***,the scRAFA is also applicable to analyze the binding affinities on a single cell in native human *** its superior performance and general applicability,scRAFA is expected to find applications in study of the spatial organization of cell surface receptors and diagnosis of infectious diseases.
Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation....
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Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics a...
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ISBN:
(数字)9798350360585
ISBN:
(纸本)9798350360592
Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.
In this work, hydrothermal (HT)-synthesized gallium oxide (Ga2O3) nanorods were applied on indium tin oxide (ITO) glass substrates to create a dual-functional pH-glucose sensing device. Extended-gate field-effect tran...
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In our study, β-Ga2O3 particles of various morphologies deposited on FTO glass substrates through hydrothermal method under different pH conditions and pre-stirring temperatures. The particles examined using XRD, FES...
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We present a novel application of biomimetic spiny microneedles for an anchoring drug deposit (SMAD) enabling long-term localized treatment of lesions within the GI tract. Mechanical characterizations in ex-vivo intes...
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Our surroundings’ auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC...
Our surroundings’ auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models—three, four, and five classes—with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively.
Integrated photodetectors are crucial for their high speed, sensitivity, and efficient power consumption. In these devices, photocurrent generation is primarily attributed to the photovoltaic (PV) effect, driven by el...
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The number of scientific papers related to biology is rapidly increasing, and with it, the number of pathway figures containing crucial biological information. However, manually annotating these figures is a time-cons...
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ISBN:
(数字)9798350377613
ISBN:
(纸本)9798350377620
The number of scientific papers related to biology is rapidly increasing, and with it, the number of pathway figures containing crucial biological information. However, manually annotating these figures is a time-consuming process, and current optical character recognition (OCR) methods are not effective in identifying gene names within these figures. To improve gene name recognition from pathway figures, we utilized a Siamese network that maps image segments to a library of pictures containing known gene name, like object recognition in many photo applications. We combined the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net) to create a CNN triple loss function. We trained and tested the developed TSPP-Net network using 563 gene images from 45 pathway pictures, 1000 images of alphanumeric characters and gene names from HUGO and KEGG. To benchmark against major available OCR technologies, we used 20 randomly selected curated pathway genes. Additionally, we compared several models, such as VGG19, VGG16, Resnet-50 Resnet-18, Densnet-121, and Xception for the Siamese backbone network model. VGG16 achieved the best performance with an accuracy of 93%, which is much higher than the results obtained using OCR. Our code is publicly available at https://***/MuhammadAzam636/Siamese-Network.
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