Biospinning of hierarchical fibers for a self-sensing actuator

Xu, Chenxue; Jiang, Zhenlin; Wang, Baoxiu; Chen, Jiapeng; Sun, Tao; Fu, Fanfan; Wang, Chaosheng; Wang, Huaping

doi:10.1016/j.cej.2024.150014

Cited by 15 publications

(3 citation statements)

References 51 publications

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“…During training, the best model weights ( Hou et al, 2017 ; Wu C. et al, 2019 ; Wu W. et al, 2019 ; Zhang X. et al, 2024 ; Zhang J. et al, 2024 ) are saved using a checkpoint callback based on validation accuracy, which monitors the performance on a validation set separated from the training data ( Lu et al, 2022 ). The network weights are optimized during the training phase to minimize the loss function ( Lu et al, 2024 ) and increase the accuracy of pixel-wise classification ( Miao et al, 2023 ; Mou et al, 2023 ; Xu et al, 2024 ). The model is trained using batches of photos with matching segmentation masks, as shown in Figures 4 , 5 .…”

Section: Methodsmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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IntroductionDuring the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.MethodsAdvances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.ResultsThe performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.DiscussionFurthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

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Section: Methodsmentioning

confidence: 99%

Remote intelligent perception system for multi-object detection

Alazeb,

Chughtai,

Al Mudawi

et al. 2024

Front. Neurorobot.

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“…We trained the MLP using a backpropagation algorithm with a stochastic gradient descent optimizer [99,100]. A categorical cross-entropy [101][102][103] loss function was employed, suitable for the multi-class classification challenges presented by our datasets. The key elements of our training process included:…”

Section: Training Processmentioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

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The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

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“…Nanofibers have captured significant attention in recent times due to their distinct characteristics and versatile applications across various domains like biomedicine, electronics, and environmental engineering [ 83 ]. Electrospinning is a technique widely used for creating NFs due to its cost-effectiveness and flexibility.…”

Section: Icariin-loaded Nanoplatformsmentioning

confidence: 99%

Promoting osteogenesis and bone regeneration employing icariin-loaded nanoplatforms

Mohammadzadeh,

Zarei,

Abbasi

et al. 2024

J Biol Eng

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There is an increasing demand for innovative strategies that effectively promote osteogenesis and enhance bone regeneration. The critical process of bone regeneration involves the transformation of mesenchymal stromal cells into osteoblasts and the subsequent mineralization of the extracellular matrix, making up the complex mechanism of osteogenesis. Icariin’s diverse pharmacological properties, such as anti-inflammatory, anti-oxidant, and osteogenic effects, have attracted considerable attention in biomedical research. Icariin, known for its ability to stimulate bone formation, has been found to encourage the transformation of mesenchymal stromal cells into osteoblasts and improve the subsequent process of mineralization. Several studies have demonstrated the osteogenic effects of icariin, which can be attributed to its hormone-like function. It has been found to induce the expression of BMP-2 and BMP-4 mRNAs in osteoblasts and significantly upregulate Osx at low doses. Additionally, icariin promotes bone formation by stimulating the expression of pre-osteoblastic genes like Osx, RUNX2, and collagen type I. However, icariin needs to be effectively delivered to bone to perform such promising functions.Encapsulating icariin within nanoplatforms holds significant promise for promoting osteogenesis and bone regeneration through a range of intricate biological effects. When encapsulated in nanofibers or nanoparticles, icariin exerts its effects directly at the cellular level. Recalling that inflammation is a critical factor influencing bone regeneration, icariin's anti-inflammatory effects can be harnessed and amplified when encapsulated in nanoplatforms. Also, while cell adhesion and cell migration are pivotal stages of tissue regeneration, icariin-loaded nanoplatforms contribute to these processes by providing a supportive matrix for cellular attachment and movement. This review comprehensively discusses icariin-loaded nanoplatforms used for bone regeneration and osteogenesis, further presenting where the field needs to go before icariin can be used clinically.

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Biospinning of hierarchical fibers for a self-sensing actuator

Cited by 15 publications

References 51 publications

Remote intelligent perception system for multi-object detection

Remote intelligent perception system for multi-object detection

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Promoting osteogenesis and bone regeneration employing icariin-loaded nanoplatforms

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