Visual inspection has traditionally been used for structural health monitoring. However, assessments conducted by trained inspectors or using contact sensors on structures for monitoring are costly ...and inefficient because of the number of inspectors and sensors required. To date, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has become popular, but UAVs require skilled pilots or a global positioning system (GPS) for autonomous flight. Unfortunately, GPS cannot be used by a UAV for autonomous flight near some parts of certain structures (e.g., beneath a bridge), but these are the critical locations that should be inspected to monitor and maintain structural health. To address this difficulty, this article proposes an autonomous UAV method using ultrasonic beacons to replace the role of GPS, a deep convolutional neural network (CNN) for damage detection, and a geo‐tagging method for the localization of damage. Concrete cracks, as an example of structural damage, were successfully detected with 97.7% specificity and 91.9% sensitivity, by processing video data collected from an autonomous UAV.
The distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. We used flexible organic electronics to mimic the functions ...of a sensory nerve. Our artificial afferent nerve collects pressure information (1 to 80 kilopascals) from clusters of pressure sensors, converts the pressure information into action potentials (0 to 100 hertz) by using ring oscillators, and integrates the action potentials from multiple ring oscillators with a synaptic transistor. Biomimetic hierarchical structures can detect movement of an object, combine simultaneous pressure inputs, and distinguish braille characters. Furthermore, we connected our artificial afferent nerve to motor nerves to construct a hybrid bioelectronic reflex arc to actuate muscles. Our system has potential applications in neurorobotics and neuroprosthetics.
Recently, crack segmentation studies have been investigated using deep convolutional neural networks. However, significant deficiencies remain in the preparation of ground truth data, consideration ...of complex scenes, development of an object-specific network for crack segmentation, and use of an evaluation method, among other issues. In this paper, a novel semantic transformer representation network (STRNet) is developed for crack segmentation at the pixel level in complex scenes in a real-time manner. STRNet is composed of a squeeze and excitation attention-based encoder, a multi head attention-based decoder, coarse upsampling, a focal-Tversky loss function, and a learnable swish activation function to design the network concisely by keeping its fast-processing speed. A method for evaluating the level of complexity of image scenes was also proposed. The proposed network is trained with 1203 images with further extensive synthesis-based augmentation, and it is investigated with 545 testing images (1280 × 720, 1024 × 512); it achieves 91.7%, 92.7%, 92.2%, and 92.6% in terms of precision, recall, F1 score, and mIoU (mean intersection over union), respectively. Its performance is compared with those of recently developed advanced networks (Attention U-net, CrackSegNet, Deeplab V3+, FPHBN, and Unet++), with STRNet showing the best performance in the evaluation metrics-it achieves the fastest processing at 49.2 frames per second.
Human cortical organoids (hCOs), derived from human embryonic stem cells (hESCs), provide a platform to study human brain development and diseases in complex three-dimensional tissue. However, ...current hCOs lack microvasculature, resulting in limited oxygen and nutrient delivery to the inner-most parts of hCOs. We engineered hESCs to ectopically express human ETS variant 2 (ETV2). ETV2-expressing cells in hCOs contributed to forming a complex vascular-like network in hCOs. Importantly, the presence of vasculature-like structures resulted in enhanced functional maturation of organoids. We found that vascularized hCOs (vhCOs) acquired several blood-brain barrier characteristics, including an increase in the expression of tight junctions, nutrient transporters and trans-endothelial electrical resistance. Finally, ETV2-induced endothelium supported the formation of perfused blood vessels in vivo. These vhCOs form vasculature-like structures that resemble the vasculature in early prenatal brain, and they present a robust model to study brain disease in vitro.
An electronic (e‐) skin is expected to experience significant wear and tear over time. Therefore, self‐healing stretchable materials that are simultaneously soft and with high fracture energy, that ...is high tolerance of damage or small cracks without propagating, are essential requirements for the realization of robust e‐skin. However, previously reported elastomers and especially self‐healing polymers are mostly viscoelastic and lack high mechanical toughness. Here, a new class of polymeric material crosslinked through rationally designed multistrength hydrogen bonding interactions is reported. The resultant supramolecular network in polymer film realizes exceptional mechanical properties such as notch‐insensitive high stretchability (1200%), high toughness of 12 000 J m−2, and autonomous self‐healing even in artificial sweat. The tough self‐healing materials enable the wafer‐scale fabrication of robust and stretchable self‐healing e‐skin devices, which will provide new directions for future soft robotics and skin prosthetics.
An extremely tough and water‐insensitive self‐healing elastomer crosslinked through multistrength hydrogen bonding interactions is described. The resultant crosslinking network in polymer film realizes exceptional mechanical properties such as notch‐insensitive high stretchability (1200%), a high toughness of 12 000 J m−2, and autonomous self‐healing even in artificial sweat. The tough self‐healing materials enable the wafer‐scale fabrication of robust and stretchable self‐healing e‐skin devices.
Several studies have aimed to predict and control carbon emissions from coal‐fired power plants. However, the highly complex combustion mechanisms in coal‐fired power plant boilers pose a significant ...challenge in direct modeling and optimization. To tackle this challenge, this study introduced a data‐driven approach along with model‐based process optimization to mitigate NOx emissions from coal‐fired power plants. The process involved collecting a 5‐month operational dataset containing 67 controllable parameters from a 500 MW coal‐fired power plant. Steady‐state data was isolated from the load output using a moving average method, followed by the application of an isolation forest algorithm to detect and remove anomalies. Correlation analysis was then used to evaluate parameter relationships and eliminate redundant ones. Subsequently, a NOx prediction model was developed, combining an extra tree regressor data‐driven prediction model with particle swarm optimization to optimize the most influential controllable parameters for reducing NOx emissions. Testing the proposed model across four different target loads consistently resulted in a reduction of over 20% in NOx emissions by optimizing boiler combustion parameters, representing a significant achievement in optimizing coal‐fired combustion.
In today’s world, renewable energy sources are increasingly integrated with nonrenewable energy sources into electric grids and pose new challenges because of their intermittent and variable nature. ...Energy prediction using soft-computing techniques plays a vital role in addressing these challenges. As electricity consumption is closely linked to other energy sources such as natural gas and oil, forecasting electricity consumption is essential for making national energy policies. In this paper, we utilize various data mining techniques, including preprocessing historical load data and the load time series’s characteristics. We analyzed the power consumption trends from renewable energy sources and nonrenewable energy sources and combined them. A novel machine learning-based hybrid approach, combining multilayer perceptron (MLP), support vector regression (SVR), and CatBoost, is proposed in this paper for power forecasting. A thorough comparison is made, taking into account the results obtained using other prediction methods.
The human foot is easily deformed owing to the innate form of the foot or an incorrect walking posture. Foot deformations not only pose a threat to foot health but also cause fatigue and pain when ...walking; therefore, accurate diagnoses of foot deformations are required. However, the measurement of foot deformities requires specialized personnel, and the objectivity of the diagnosis may be insufficient for professional medical personnel to assess foot deformations. Thus, it is necessary to develop an objective foot deformation classification model. In this study, a model for classifying foot types is developed using image and numerical foot pressure data. Such heterogeneous data are used to generate a fine-tuned visual geometry group-16 (VGG16) and K-nearest neighbor (k-NN) models, respectively, and a stacking ensemble model is finally generated to improve accuracy and robustness by combining the two models. Through k-fold cross-validation, the accuracy and robustness of the proposed method have been verified by the mean and standard deviation of the f1 scores (0.9255 and 0.0042), which has superior performance compared to single models generated using only numerical or image data. Thus, the proposed model provides the objectivity of diagnosis for foot deformation, and can be used for analysis and design of foot healthcare products.
Short-chained, aromatic phenethylammonium bromide (PEABr) bulky cation engineering mitigate the surface defects and tune the optical refractive index (n) and the CsPbBr3 perovskite quantum dots that ...substantially improved the optoelectronic properties, PL stability, enabling perovskite light-emitting diode to boost external quantum efficiency from ~1% to ~7%.
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•CsPbBr3 PeQDs are passivated using PEABr cations via facile spin-coating methods.•PEABr passivation mitigates surface defects and tune the index n of CsPbBr3 PeQDs.•PEABr passivation improves the optical coupling and yield high luminescence.•The passivated CsPbBr3 PeQDs films deliver improved efficiency and stability.
All inorganic CsPbBr3 perovskite quantum dots (PeQDs) have emerged as great candidates for next-generation perovskite quantum dots light-emitting diodes (PeQLEDs) applications due to their excellent optoelectronic and light-emitting properties. However, the performance of CsPbBr3 based PeQLEDs is hindered by (i) the long-chain, synthetic insulating ligands on PeQDs surfaces and (ii) the inherently high refractive index (n) of the PeQDs that often leads to internal light confinement loss. These major shortcomings are addressed by introducing a short-chain ammonium moiety, namely phenethylammonium bromide (PEABr), via spin-coating to passivate the surface of the PeQDs films. PEABr passivation can effectively annihilate the intrinsic bromide vacancies of PeQDs and simultaneously tune the refractive index of the PeQDs films. The reduced n-mismatch between the emitter and the charge transporting layers suppresses the waveguide loss after PEABr passivation and significantly elevates the external quantum efficiency (EQE) and maximum luminance of the PeQLEDs from ~ 1.0% to ~ 6.85% and ~ 1300 cd m−2 to ~ 13000 cd m−2, respectively. More importantly, the environmental stability of the PeQDs also improves remarkably following PEABr passivation. The alky cation engineering demonstrated herein is a facile yet efficient approach to simultaneously boost the performance and stability of CsPbBr3 PeQDs.
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