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Electrochemical biosensors incorporate a recognition element and an electronic transducer for the highly sensitive detection of analytes in body fluids. Importantly, they can provide rapid readouts and they can be integrated into portable, wearable and implantable devices for point-of-care diagnostics; for example, the personal glucose meter enables at-home assessment of blood glucose levels, greatly improving the management of diabetes. In this Review, we discuss the principles of electrochemical biosensing and the design of electrochemical biosensor devices for health monitoring and disease diagnostics, with a particular focus on device integration into wearable, portable and implantable systems. Finally, we outline the key engineering challenges that need to be addressed to improve sensing accuracy, enable multiplexing and one-step processes, and integrate electrochemical biosensing devices in digital health-care pathways.Electrochemical biosensors can be integrated into wearable, portable and implantable devices for health monitoring and disease diagnosis. This Review discusses the design and integration of different types of electrochemical biosensors for the detection of analytes related to health and disease, and outlines engineering challenges that need to be addressed to enable clinical translation of electrochemical biosensor-based point-of-care devices.

In the contemporary, fiercely competitive marketplace, companies must adeptly navigate the complexities of understanding and fulfilling user needs to succeed. By mining potential user needs from User Generated Content (UGC) on social media platforms, businesses can design products that resonate with users’ needs, thereby swiftly capturing market share. When predicting user needs in this paper, the collected UGC is first processed through operations such as deduplication, word segmentation, and stop-word removal. Subsequently, Latent Dirichlet Allocation (LDA) is employed to extract product attribute features from UGC, cluster them to identify user needs and classify documents accordingly. The Bidirectional Encoder Representations from Transformers (BERT) model is then utilized for word vector feature extraction of the categorized documents, while also taking into account user interaction metrics to perform sentiment analysis of user needs using Long Short-Term Memory (LSTM). Finally, a Correlation Analysis-Vector Autoregressive-Markov (CA-VAR-Markov) model is constructed to forecast the evolution of user needs, and the Analytical Kano (A-Kano) model is applied for an in-depth analysis to propose strategies for product design optimization. In the case study, this paper takes the UGC from “Autohome” as an example to predict the user needs for the NIO EC6. Compared with LSTM and ARIMA, the prediction results are more accurate. Based on the prediction results and combined with the A-KANO model, suggestions are put forward for the optimization of the NIO EC6. The final results prove that the methods for identifying and predicting user needs proposed in this paper can effectively predict the development trend of user needs, providing a reference for enterprises to optimize their products.

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496   ×   496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

Lateral organ boundaries (LOB) domain (LBD) genes, a gene family encoding plant-specific transcription factors, play important roles in plant growth and development. At present, though there have been a number of genome-wide analyses on LBD gene families and functional studies on individual LBD proteins, the diverse functions of LBD family members still confuse researchers and an effective strategy is required to summarize their functional diversity. To further integrate and improve our understanding of the phylogenetic classification, functional characteristics and regulatory mechanisms of LBD proteins, we review and discuss the functional characteristics of LBD proteins according to their classifications under a phylogenetic framework. It is proved that this strategy is effective in the anatomy of diverse functions of LBD family members. Additionally, by phylogenetic analysis, one monocot-specific and one eudicot-specific subclade of LBD proteins were found and their biological significance in monocot and eudicot development were also discussed separately. The review will help us better understand the functional diversity of LBD proteins and facilitate further studies on this plant-specific transcription factor family.

Background Elevated temperature as a result of global climate warming, either in form of sudden heatwave (heat shock) or prolonged warming, has profound effects on the growth and development of plants. However, how plants differentially respond to these two forms of elevated temperatures is largely unknown. Here we have therefore performed a comprehensive comparison of multi-level responses of Arabidopsis leaves to heat shock and prolonged warming. Results The plant responded to prolonged warming through decreased stomatal conductance, and to heat shock by increased transpiration. In carbon metabolism, the glycolysis pathway was enhanced while the tricarboxylic acid (TCA) cycle was inhibited under prolonged warming, and heat shock significantly limited the conversion of pyruvate into acetyl coenzyme A. The cellular concentration of hydrogen peroxide (H 2 O 2 ) and the activities of antioxidant enzymes were increased under both conditions but exhibited a higher induction under heat shock. Interestingly, the transcription factors, class A1 heat shock factors (HSFA1s) and dehydration responsive element-binding proteins (DREBs), were up-regulated under heat shock, whereas with prolonged warming, other abiotic stress response pathways, especially basic leucine zipper factors (bZIPs) were up-regulated instead. Conclusions Our findings reveal that Arabidopsis exhibits different response patterns under heat shock versus prolonged warming, and plants employ distinctly different response strategies to combat these two types of thermal stress.

Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

The wellbore of a sucker–rod pumping well experiences a multi–phase flow consisting of oil, gas, and water. The flow pattern and pump discharge pressure are greatly impacted by oil well production, which in turn significantly affects the simulation results of longitudinal vibration in the sucker–rod string. When calculating the discharge pressure in a hydrostatic column containing both oil and water (HC), the pressure is not affected by the oil well’s production. This thereby avoids interference between vibrations in the sucker–rod string’s longitudinal direction and the flow from the wellbore. Considering the coupling characteristics between the longitudinal vibration of the sucker–rod string and the wellbore flow, a mathematical model of the sucker–rod pumping system (CMSRS) and a mathematical model of the downhole energy efficiency parameters were established. In detail, the CMSRS comprises two parts: the discharge pressure mathematical models of multi–phase flow dynamics (MD) and the wave equation of the longitudinal vibration of the sucker–rod string. A numerical simulation model of the sucker–rod pumping system was constructed based on a mathematical model. We compared the experimental results, the simulation results of the CMSRS and the simulation results of the sucker–rod string based on the oil–water two–phase hydrostatic column (SMSRS) and found good agreement, indicating the feasibility of the CMSRS. The simulation details show the following: (1) The HC model’s discharge pressure exceeds that of the MD model by more than 33.52%. The polished rod load for the CMSRS is 18.01% lower than that of the SMSRS, and the pump input power for the CMSRS is 36.23% lower than that of the SMSRS. (2) The effective power simulation model based on the energy balance relationship is essentially the same as the effective power calculated by the model based on multi–phase flow effective power. This validates the accuracy of the multi–phase flow effective power model. (3) The limitations of the industry standard effective power model are that (i) the effective head is the net lift height of the fluid in the wellbore reduced to the oil and water phases rather than the effective lift height based on the energy balance relationship and (ii) the power of the gas phase delivered by the pumping pump is disregarded, and only the effective power of the pump delivering the oil–water mixture is considered. (4) The influence of the wellbore parameters on the wellbore efficiency and sub–efficiency is systematically analyzed. The analysis results have an important significance in the guidance of energy saving in pumping wells.

Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex structure of bridges, automatically detecting bridge damage is a challenging task. In the field of crack classification and segmentation, convolutional neural networks have offer advantages, but ordinary networks cannot completely solve the environmental impact problems in reality. To further overcome these problems, in this paper a new algorithm to detect surface damage called EMA-DenseNet is proposed. The main contribution of this article is to redesign the structure of the densely connected convolutional networks (DenseNet) and add the expected maximum attention (EMA) module after the last pooling layer. The EMA module is obviously helpful to the bridge damage feature extraction. Besides, we use a new loss function which considers the connectivity of pixels, it has been proved to be effective in reducing the break point of fracture prediction and improving the accuracy. To train and test the model, we captured many images from multiple bridges located in Zhejiang (China), and then built a dataset of bridge damage images. First, experiments were carried out on an open concrete crack dataset. The mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and frames per second (FPS) of the EMA-DenseNet are 87.42%, 92.59%, 81.97% and 25.4, respectively. Then we also conducted experiments on a more challenging bridge damage dataset, the MIoU, where MPA, precision and FPS were 79.87%, 86.35%, 74.70% and 14.6, respectively. Compared with the current state-of-the-art algorithms, the proposed algorithm is more accurate and robust in bridge damage detection.

Plants can autonomously adjust their growth direction based on the gravitropic response to maximize energy acquisition, despite lacking nerves and muscles. Endowing soft robots with gravitropism may facilitate the development of self‐regulating systems free of electronics, but remains elusive. Herein, acceleration‐regulated soft actuators are described that can respond to the gravitational field by leveraging the unique fluidity of liquid metal in its self‐limiting oxide skin. The soft actuator is obtained by magnetic printing of the fluidic liquid metal heater circuit on a thermoresponsive liquid crystal elastomer. The Joule heat of the liquid metal circuit with gravity‐regulated resistance can be programmed by changing the actuator's pose to induce the flow of liquid metal. The actuator can autonomously adjust its bending degree by the dynamic interaction between its thermomechanical response and gravity. A gravity‐interactive soft gripper is also created with controllable grasping and releasing by rotating the actuator. Moreover, it is demonstrated that self‐regulated oscillation motion can be achieved by interfacing the actuator with a monostable tape spring, allowing the electronics‐free control of a bionic walker. This work paves the avenue for the development of liquid metal‐based reconfigurable electronics and electronics‐free soft robots that can perceive gravity or acceleration. Gravity‐responsive soft actuators are created using liquid metal and thermoresponsive liquid crystal elastomer. The Joule heat of the liquid metal circuit with gravity‐regulated resistance can be programmed by changing the actuator's pose to induce the flow of liquid metal. A gravity‐adaptive actuator, a gravity‐interactive gripper, and a self‐regulated snapping oscillator or walker are also demonstrated.

To thoroughly investigate the patterns of crack development and the mechanisms of degradation damage in shallow, highly weathered mudstone subjected to repeated dry-wet cycles, a series of laboratory tests were conducted. Initially, the progression of surface cracks on the specimens was documented through fixed-point photography. Image processing techniques using ImageJ software were employed, with crack ratio and fractal dimension as evaluation indices, to quantitatively analyze the relationship between fracture development and the dry-wet effect at both macro and mesoscales. Secondly, stress-strain characteristics of mudstone under different numbers of dry-wet cycles were obtained through direct shear tests. Mesoscopic parameters, crack ratio and fractal dimension, were introduced to establish a damage constitutive model for mudstone under the coupled action of dry-wet cycles and loading. The results indicate that both cohesion and internal friction angle exhibit a stepwise degradation pattern with increasing dry-wet cycles. After 9 cycles, the loss rate of cohesion is significantly higher than that of the internal friction angle. Both crack ratio and fractal dimension increase with the number of dry-wet cycles but decrease with increasing compaction degree. After 3 dry-wet cycles, the basic skeletal structure of the cracks has been preliminarily formed, gradually developing into a reticulated crack network. The stress-strain characteristics can be roughly divided into elastic deformation, elastoplastic deformation, and plastic deformation stages. Furthermore, the stress state of mudstone gradually transitions from strain hardening to strain softening with increasing dry-wet cycles. The established meso-damage constitutive model demonstrates a strong correlation with experimental data, effectively capturing the deformation and failure processes of shallow, highly weathered mudstone subjected to dry-wet cycles. Notably, the model exhibits the highest fitting accuracy under low-stress conditions, underscoring its robust applicability in characterizing the deformation and failure characteristics of shallow weathered mudstone in such environments.The research findings provide theoretical references for the optimal design of reinforcement for shallow weathered mudstone cut slopes in practical engineering.