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The Unmanned Aerial Vehicle (UAV) tracking tasks find extensive utility across various applications. However, current Transformer-based trackers are generally tailored for diverse scenarios and lack specific designs for UAV applications. Moreover, due to the complexity of training in tracking tasks, existing models strive to improve tracking performance within limited scales, making it challenging to directly apply lightweight designs. To address these challenges, we introduce an efficient attention mechanism known as Macaron Attention, which we integrate into the existing UAV tracking framework to enhance the model’s discriminative ability within these constraints. Specifically, our attention mechanism comprises three components, fixed window attention (FWA), local squeezing global attention (LSGA), and conventional global attention (CGA), collectively forming a Macaron-style attention implementation. Firstly, the FWA module addresses the multi-scale issue in UAVs by cropping tokens within a fixed window scale in the spatial domain. Secondly, in LSGA, to adapt to the scale variation, we employ an adaptive clustering-based token aggregation strategy and design a “window-to-window” fusion attention model to integrate global attention with local attention. Finally, the CGA module is applied to prevent matrix rank collapse and improve tracking performance. By using the FWA, LSGA, and CGA modules, we propose a brand-new tracking model named MATrack. The UAV123 benchmark is the major evaluation dataset of MATrack with 0.710 and 0.911 on success and precision, individually.

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods.

Due to their devastating ability to extract features, convolutional neural network (CNN)-based approaches have achieved tremendous success in hyperspectral image (HSI) classification. However, previous works have been dedicated to constructing deeper or wider deep learning networks to obtain exceptional classification performance, but as the layers get deeper, the gradient disappearance problem impedes the convergence stability of network models. Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. Specifically, HFENet contains two key modules: a hybrid-scale feature extraction block (HFEB) and a shuffle attention enhancement block (SAEB). HFEB is designed to excavate spectral–spatial structure information of distinct scales, types, and branches, which can augment the multiplicity of spectral–spatial features while modeling the global long-range dependencies of spectral–spatial informative features. SAEB is devised to adaptively recalibrate spectral-wise and spatial-wise feature responses to generate the purified spectral–spatial information, which effectively filters redundant information and noisy pixels and is conducive to enhancing classification performance. Compared with several sophisticated baselines, a series of experiments conducted on three public hyperspectral datasets showed that the accuracies of OA, AA, and Kappa all exceed 99%, demonstrating that the presented HFENet achieves state-of-the-art performance.

Multiple object tracking (MOT) in videos captured by unmanned aerial vehicle (UAV) is a fundamental aspect of computer vision. Recently, the one-shot tracking paradigm integrates the detection and re-identification (ReID) tasks, striking a balance between tracking accuracy and inference speed. This paradigm alleviates task conflicts and achieves remarkable results through various feature decoupling methods. However, in challenging scenarios like drone movements, lighting changes and object occlusion, it still encounters issues with detection failures and identity switches. In addition, traditional feature decoupling methods directly employ channel-based attention to decompose the detection and ReID branches, without a meticulous consideration of the specific requirements of each branch. To address the above problems, we introduce an asymmetric feature enhancement network with a global coordinate-aware enhancement (GCAE) module and an embedding feature aggregation (EFA) module, aiming to optimize the two branches independently. On the one hand, we develop the GCAE module for the detection branch, which effectively merges rich semantic information within the feature space to improve detection accuracy. On the other hand, we introduce the EFA module for the ReID branch, which highlights the significance of pixel-level features and acquires discriminative identity embedding through a local feature aggregation strategy. By efficiently incorporating the GCAE and EFA modules into the one-shot tracking pipeline, we present a novel MOT framework, named AsyUAV. Extensive experiments have demonstrated the effectiveness of our proposed AsyUAV. In particular, it achieves a MOTA of 38.3% and IDF1 of 51.7% on VisDrone2019, and a MOTA of 48.0% and IDF1 of 67.5% on UAVDT, outperforming existing state-of-the-art trackers.

Although nondestructive ultrasonic technologies have been applied in laboratory and field tests in the field of heritage conservation, few studies have quantified the relationship among the real microstructures, micromechanical properties, and macroscopic acoustic responses of earthen-site soils. This paper develops a micromechanics-based multiscale model for quantitatively exploring the ultrasonic propagation characteristics of elastic waves in untreated and consolidated earthen-site soils. Scanning electron microscope images and image processing technology are integrated into the finite-element simulation. The effects of microstructure and wave features on the acoustic characteristics of soils are quantitatively investigated under pulsive loading. The simulation results of untreated and consolidated soils are efficiently compared to ultrasonic test data. It is demonstrated that the integration of microstructure image processing and multiscale modeling can predict the ultrasonic pulse velocity well, which improves the accuracy of laboratory testing and field monitoring and better serves the evaluation and implementation of engineering practice in the field of heritage conservation.

Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic feature network (DSMSFNet) for HSI classification is proposed. This model is composed of a decompressed spectral-spatial feature extraction module (DSFEM) and a multiscale semantic feature extraction module (MSFEM). The former is devised to extract more discriminative and representative global decompressed spectral-spatial features in a lightweight extraction manner, while the latter is constructed to expand the range of available receptive fields and generate clean multiscale semantic features at a granular level to further enhance the classification performance. Compared with progressive classification approaches, abundant experimental results on three benchmark datasets prove the superiority of our developed DSMSFNet model.

A hyperspectral image (HSI) has a very high spectral resolution, which can reflect the target’s material properties well. However, the limited spatial resolution poses a constraint on its applicability. In recent years, some hyperspectral pansharpening studies have attempted to integrate HSI with PAN to improve the spatial resolution of HSI. Although some achievements have been made, there are still shortcomings, such as insufficient utilization of multi-scale spatial and spectral information, high computational complexity, and long network model inference time. To address the above issues, we propose a novel U-shaped hyperspectral pansharpening network with channel cross-concatenation and spatial–spectral attention mechanism (CCC-SSA-UNet). A novel channel cross-concatenation (CCC) method was designed to effectively enhance the fusion ability of different input source images and the fusion ability between feature maps at different levels. Regarding network design, integrating a UNet based on an encoder–decoder architecture with a spatial–spectral attention network (SSA-Net) based on residual spatial–spectral attention (Res-SSA) blocks further enhances the ability to extract spatial and spectral features. The experiment shows that our proposed CCC-SSA-UNet exhibits state-of-the-art performance and has a shorter inference runtime and lower GPU memory consumption than most of the existing hyperspectral pansharpening methods.

Due to the development of aviation hydraulic systems towards high pressure and high flow, the frequency range of pressure pulsation becomes wider, and the amplitude of pulsation increases. This puts higher requirements on the attenuation characteristics of the pressure pulsation attenuator. To reduce the damage caused by pressure pulsation to the pipeline, a Helmholtz-type pulsation attenuator (HTPA) is designed, which works through the Helmholtz resonant chamber. The theoretical model of HTPA is established by the method of lumped parameter method and distribution parametric method. The insertion loss is adopted to evaluate its attenuation characteristics. The internal pressure dynamic characteristics and attenuation effect of the HTPA are analyzed by simulation and experimentation. The results show that the pulsation attenuation rate δ was 40% after the installation of the attenuator. In the frequency range of 0–1000 Hz, the maximum insertion loss is 19 dB, which verifies the validity and correctness of the theoretical model.

Using waste clay brick powder (RBP) to partially replace cement in the preparation of concrete, is one way to recycle construction waste. In order to investigate the physical and mechanical properties and volume stability of recycled micropowder ultra-high-performance concrete (UHPC), the basic mechanical and shrinkage properties of recycled micropowder UHPC were studied at replacement rates of 10%, 20%, 30%, 40% and 50%. The results show that: (1) When the activated recycled brick powder is used to replace the cement, the compressive strength, flexural strength and splitting tensile strength of the UHPC initially increase and then decrease with the increase in the substitution rate. When the substitution rate is 10%, the 28 d compressive strength, flexural strength and splitting tensile strength of the UHPC are the highest; (2) Replacing cement with recycled brick powder can reduce the autogenous shrinkage of the UHPC, and the autogenous shrinkage rate of the UHPC decreases with the increase in the brick powder replacement rate. The drying shrinkage of the UHPC can be reduced by replacing cement with recycled brick powder. The drying shrinkage of the UHPC initially decreases, and then increases, with the increase in the replacement rate of brick powder. When the replacement rate of the brick powder was 30%, the drying shrinkage of the UHPC was the least, and this was 49.7% lower than that in the benchmark group. The prediction models of autogenous shrinkage and drying shrinkage are in good agreement with the experimental results, which can be used to predict the shrinkage development of recycled brick powder UHPC.

The traditional herb Eleutherococcus henryi Oliv. is commonly used to treat inflammatory conditions including rheumatism, arthritis, and hepatitis, as well as mental fatigue and amnesia, according to traditional Chinese medicine (TCM) theory. Savinin is a natural lignan obtained from the roots of E. henryi. The present study was undertaken to determine whether savinin can relieve LPS-induced neuroinflammation and if so, what the mechanism is. Groups of male C57BL/6 mice were administered savinin (5, 10, 20 mg/kg) and DEX (10 mg/kg) by gavage once daily for a continuous 7 days. On the 5th day of continuous pre-administration, LPS (2.5 mg/kg) was injected into the lateral ventricles of the mice for modeling 48 h. We found that treatment with savinin decreased the levels of neuroinflammatory cytokines and histopathological alterations dramatically. Consequently, it improved the LPS-induced neuroinflammatory response in mice. Furthermore, savinin inhibited the up-regulated expression of related proteins in the activated MAPK/NF-κB and NLRP3 inflammasome signaling pathways caused by LPS. Docking studies demonstrated the binding of savinin to three receptors (MAPK, NF-κB and NLRP3) using a well-fitting mode. These findings suggest that savinin may suppress neuroinflammation induced by LPS in vivo via modulating MAPK/NF-κB and NLRP3 signaling pathways.

Reused roadways are increasingly adopted in fully mechanized coalfaces with large mining heights because of the ventilation and gas drainage problems. However, the forced mechanism and grouting control technology of the reused roadway was seldom studied. Hence, in this paper, field monitoring and numerical simulation were undertaken to investigate the distribution of stress and deformation of the reused roadway, and the reasonable grouting opportunities and technological parameters were determined accordingly. Engineering application of grouting control technology with self-developed inorganic double-fluid grouting materials was conducted. The field monitoring and numerical simulation revealed that the reused roadway was significantly affected by the lateral abutment pressure during the first mining operation and by the leading abutment pressure during the second mining operation. It was characterized by lagging stable deformation during the first mining operation and ceaselessly increasing deformation during the second mining operation. The deformation range of the reused roadway during the first mining operation can be divided into three stages: initial deformation, violent deformation and plateaued deformation, while the deformation range can be divided into three distinct stages: initial deformation, slow deformation and violent deformation during the second mining operation. On the basis of the roadway deformation law, two grouting opportunities were confirmed. The first grouting opportunity was suggested in the front part of the plateaued deformation stage during the first mining. The second grouting opportunity was suggested in the slow deformation stage during the second mining. Field engineering applications showed that both the deformation range and value of the reused roadway were decreased obviously after grouting.

Bacillus subtilis was applied in peat-based soilless cultivation systems containing a mixed substrate (peat:vermiculite:perlite = 2:1:1, v/v/v) and irrigated by one-strength or four-strength Hoagland’s nutrient solution to explore whether it can alleviate inhibition by higher-nutrient solutions (four-strength) and bring benefits to improvements of quality. The results showed that higher-nutrient solutions improved the flavor quality of cucumber fruit; especially, the contents of (E,Z)-2,6-nonadienal and (E)-2-Nonenal were effectively increased, which are the special flavor substances of cucumber. B. subtilis K424 effectively improved growth performance, photosynthetic capacity, vitamin C content, soluble sugars, soluble protein, and total pectin in cucumber under higher nutrition solution conditions. Compared with the higher solution treatment, the bacterial diversity significantly increased, whereas the presence of fungi had no significant difference following the B. subtilis K424 application. Moreover, B. subtilis K424 reduced the relative abundance of Actinomadura and promoted that of the Rhodanobacter, Bacillus, Pseudomonas, Devosiaceae, and Blastobotrys genera. Redundancy analysis showed that Bacillus, Rhodanobacter, and Blastobotrys were positively correlated with the substrate enzyme of sucrase, catalase, and urease. This study provides insight that B. subtilis K424 mitigated the deleterious effects of high levels of nutrition solution on cucumber growth and quality by improving the substrate enzyme, regulating the microbial community structure, and enhancing the photosynthetic capacity.

Convolutional neural networks (CNNs) have demonstrated impressive performance and have been broadly applied in hyperspectral image (HSI) classification. However, two challenging problems still exist: the first challenge is that redundant information is averse to feature learning, which damages the classification performance; the second challenge is that most of the existing classification methods only focus on single-scale feature extraction, resulting in underutilization of information. To resolve the two preceding issues, this article proposes a multiscale cross interaction attention network (MCIANet) for HSI classification. First, an interaction attention module (IAM) is designed to highlight the distinguishability of HSI and dispel redundant information. Then, a multiscale cross feature extraction module (MCFEM) is constructed to detect spectral–spatial features at different scales, convolutional layers, and branches, which can increase the diversity of spectral–spatial features. Finally, we introduce global average pooling to compress multiscale spectral–spatial features and utilize two fully connection layers, two dropout layers to obtain the output classification results. Massive experiments on three benchmark datasets demonstrate the superiority of our presented method compared with the state-of-the-art methods.

Recently, hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have shown impressive performance. However, HSI classification still faces two challenging problems: the first challenge is that most existing classification approaches only focus on exploiting the fixed-scale convolutional kernels to extract spectral–spatial features, which leads to underutilization of information; the second challenge is that HSI contains a large amount of redundant information and noise, to a certain extent, which influences the classification performance of CNN. In order to tackle the above problems, this article proposes a multibranch crossover feature attention network (MCFANet) for HSI classification. The MCFANet involves two primary submodules: a cross feature extraction module (CFEM) and rearranged attention module (RAM). The former is devised to capture joint spectral–spatial features at different convolutional layers, scales and branches, which can increase the diversity and complementarity of spectral–spatial features, while the latter is constructed to spontaneously concentrate on recalibrating spatial-wise and spectral-wise feature responses, meanwhile exploit the shifted cascade operation to rearrange the obtained attention-enhanced features to dispel redundant information and noise, and thus, boost the classification performance. Compared with the state-of-the-art classification methods, massive experiments on four benchmark datasets demonstrate the meliority of our presented method.

Ice accretion on stratospheric airships, which affects the net buoyancy and reduces the ascent speed, thereby leading to component failure and further necessitating flight termination, has received considerable attention. In this study, the formation of ice occurred when the airship hit supercooled water droplets during the non-forming ascending process. Theoretical descriptions of an ice accretion model, a thermal model, and a dynamic model for airships were established to estimate the flight performance with ice mass. Then, validation of the simulation and the cloud chamber test was carried out, which indicated that the temperature, liquid water content (LWC), and pressure considerably influenced the ice mass. A lower temperature had a positive effect on ice accretion. The mass of ice accretion increased with the increase in LWC. Ice did not form easily under low pressure due to evaporation and sublimation. Finally, the effects of the ambient LWC and initial helium mass were analyzed. It was shown that an LWC of 0.5 g/m3 resulted in severe degradation of the ascent performance. When the initial helium mass was not sufficient, the airship landed due to ice accumulation. However, redundant inflation increased the ice mass and lowered the cruising altitude.

Convolutional neural networks are widely applied in hyperspectral image (HSI) classification and show excellent performance. However, there are two challenges: the first is that fine features are generally lost in the process of depth transfer; the second is that most existing studies usually restore to first-order features, whereas they rarely consider second-order representations. To tackle the above two problems, this article proposes a hybrid-order spectral-spatial feature network (HS2FNet) for hyperspectral image classification. This framework consists of a precedent feature extraction module (PFEM) and a feature rethinking module (FRM). The former is constructed to capture multiscale spectral-spatial features and focus on adaptively recalibrate channel-wise and spatial-wise feature responses to achieve first-order spectral-spatial feature distillation. The latter is devised to heighten the representative ability of HSI by capturing the importance of feature cross-dimension, while learning more discriminative representations by exploiting the second-order statistics of HSI, thereby improving the classification performance. Massive experiments demonstrate that the proposed network achieves plausible results compared with the state-of-the-art classification methods.

Recently, indocyanine green (ICG), as an FDA-approved dye, has been widely used for phototherapy. It is essential to obtain information on the migration and aggregation of ICG in deep tissues. However, existing fluorescence imaging platforms are not able to obtain the structural information of the tissues. Here, we prepared ICG liposomes (ICG-Lips) and built a dual-wavelength photoacoustic computed tomography (PACT) system with piezoelectric ring-array transducer to image the aggregation of ICG-Lips in tumors to guide phototherapy. Visible 780 nm light excited the photoacoustic (PA) effects of the ICG-Lips and near-infrared 1064 nm light provided the imaging of the surrounding tissues. The aggregation of ICG-Lips within the tumor and the surrounding tissues was visualized by PACT in real time. This work indicates that PACT with piezoelectric ring-array transducer has great potential in the real-time monitoring of in vivo drug distribution.

Due to end-to-end optimization characteristics and fine generalization ability, convolutional neural networks have been widely applied to hyperspectral image (HSI) classification, playing an irreplaceable role. However, previous studies struggle with two major challenges: (1) HSI contains complex topographic features, the number of labeled samples in different categories is unbalanced, resulting in poor classification for categories with few labeled samples; (2) With the deepening of neural network models, it is difficult to extract more discriminative spectral-spatial features. To address the issues mentioned above, we propose a discriminative spectral-spatial-semantic feature network based on shuffle and frequency attention mechanisms for HSI classification. There are four main parts of our approach: spectral-spatial shuffle attention module (SSAM), context-aware high-level spectral-spatial feature extraction module (CHSFEM), spectral-spatial frequency attention module (SFAM), and cross-connected semantic feature extraction module (CSFEM). First, to fully excavate the category attribute information, SSAM based on a “Deconstruction-Reconstruction” structure is designed, solving the problem of poor classification performance caused by an unbalanced number of label samples. Considering that deep spectral-spatial features are difficult to extract, CHSFEM and SFAM are constructed. The former is based on the “Horizontal-Vertical” structure to capture context-aware high-level multiscale features. The latter introduces multiple frequency components to compress channels to obtain more multifarious features. Finally, towards suppressing noisy boundaries efficiently and capturing abundant semantic information, CSFEM is devised. Numerous experiments are implemented on four public datasets: the evaluation indexes of OA, AA and Kappa on four datasets all exceed 99%, demonstrating that our method can achieve satisfactory performance and is superior to other contrasting methods.

Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance. However, hyperspectral image classification still suffers from various challenges, including abundant redundant information, insufficient spectral-spatial representation, irregular class distribution, and so forth. To address these issues, we propose a novel 2D-3D CNN with spectral-spatial multi-scale feature fusion for hyperspectral image classification, which consists of two feature extraction streams, a feature fusion module as well as a classification scheme. First, we employ two diverse backbone modules for feature representation, that is, the spectral feature and the spatial feature extraction streams. The former utilizes a hierarchical feature extraction module to capture multi-scale spectral features, while the latter extracts multi-stage spatial features by introducing a multi-level fusion structure. With these network units, the category attribute information of HSI can be fully excavated. Then, to output more complete and robust information for classification, a multi-scale spectral-spatial-semantic feature fusion module is presented based on a Decomposition-Reconstruction structure. Last of all, we innovate a classification scheme to lift the classification accuracy. Experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

It is widely accepted that the frictional pressure drop is impossible to be negative for pipe flow. However, the negative frictional pressure drops were observed for some cases of two-phase slug and churn flows in pipes, challenging the general sense of thermodynamic irreversibility. In order to solve this puzzling problem, theoretical investigations were performed for the entropy generation in slug and churn flows. It is found that the frictional pressure drop along with a buoyancy-like term contributes to the entropy generation due to mechanical energy loss for steady, incompressible slug and churn flows in vertical and inclined pipes. Experiments were conducted in a vertical pipe with diameter as 0.04 m for slug and churn flows. Most of the experimental data obtained for frictional pressure drop are negative at high gas–liquid ratios from 100 to 10,000. Entropy generation rates were calculated from experimental data. The results show that the buoyancy-like term is positive and responsible for a major part of entropy generation rate while the frictional pressure drop is responsible for a little part of entropy generation rate, because of which the overall entropy generation due to mechanical energy loss is still positive even if the frictional pressure drop is negative in vertical slug and churn flows. It is clear that the negative frictional pressure drops observed in slug and churn flows are not against the thermodynamics irreversibility.

In this paper, multibody dynamic modeling and a simulation method for the wingtip-jointing process of a new-concept composite aircraft system are investigated. When the wingtips of two aircraft are jointed, the resultant wingtip-jointed aircraft is regarded as variable-geometry multiple rigid bodies, and a seven-degree-of-freedom non-linear dynamic model is established by mathematical derivation. The slip-meshing method is adopted to analyze the unsteady aerodynamic influence. We also present specific aerodynamic database acquisition methods under the quasi-steady assumption. Based on this, the simulation results indicate that the longitudinal and lateral movements are highly jointed and complex. A new composite aircraft system is investigated, in order to meet the balance requirement. With the lift–drag ratio (K) considered, the piecewise cubic Hermite interpolation (PCHIP) method, with a sufficient sample size, was utilized to help the cruise strategy optimization analysis under fixed altitude and speed conditions. Meanwhile, distribution of cruise characteristics with different sampling values of composite flight characteristic parameters were also analyzed. The research results can be used as a reference for new-concept composite aircraft model establishment, simulation, and multibody dynamic characteristic investigation.

Mechanical properties of glass fiber reinforced polymer (GFRP) composites degrade under the combined effects of mechanical load and alkaline solution, affecting the service ability and safety of GFRP reinforced structures. In this study, GFRP bars were loaded with cyclic tension at different stress levels and immersed in alkaline solution for days to investigate the tensile properties and degradation law of GFRP bars. The degradation mechanisms were studied at micro-, meso- and macro-scales with scanning electron microscopy (SEM) and three-dimensional X-ray microscopy, respectively. The results show that tensile strength and degradation rate of GFRP bars are mainly dependent on the different stress levels and alkaline solution. When stress level is higher, the tensile strength degrades more quickly, especially in the early stages of soaking. With the loading and immersion time, the elastic modulus and Poisson’s ratio increase at first and then decrease. The ultimate tensile strain is relatively stable, whereas the ultimate elongation is significantly reduced. A strength-degradation model was proposed and fit well with experimental data, demonstrating that the model can be applied to predict tensile strength degradation under combined effects of the load and alkaline solution.

5–6 MHz PMNT/epoxy 1–3 composites were prepared by a modified dice-and-fill method. They exhibit excellent properties for ultrasonic transducer applications, such as ultrahigh thickness electromechanical coupling coefficient kt (85.7%), large piezoelectric coefficient d33 (1209 pC/N), and relatively low acoustic impedance Z (1.82 × 107 kg/(m2·s)). Besides, two types of Time-of-Flight Diffraction (TOFD) ultrasonic transducers have been designed, fabricated, and characterized, which have different matching layer schemes with the acoustic impedance of 4.8 and 5.7 × 106 kg/(m2·s), respectively. In the detection on a backwall of 12.7 mm polystyrene, the former exhibits higher detectivity, the relative pulse-echo sensitivity and −6 dB relative bandwidth are −21.93 dB and 102.7%, respectively, while the later exhibits broader bandwidth, the relative pulse-echo sensitivity and −6 dB relative bandwidth are −24.08 dB and 117.3%, respectively. These TOFD ultrasonic transducers based on PMNT/epoxy 1–3 composite exhibit considerably improved performance over the commercial PZT/epoxy 1–3 composite TOFD ultrasonic transducer.

Doppler sonographic measurement of flow velocity in the basal cerebral arteries through the intact skull was developed using a pulsed Doppler technique and 2 MHz emitting frequency. Relaxor-based ferroelectric single crystals Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT) were chosen to be the piezoelectric transducer material due to their ultrahigh piezoelectric coefficients, high electromechanical coupling coefficients and low dielectric loss. The pulse-echo response of the transducer was measured using the conventional pulse-echo method in a water bath at room temperature. The −6 dB bandwidth of the transducer is 68.4% and the sensitivity is −17.4 dB. In order to get a good match between transducer and detection system, different transmission powers have been regulated by changing the impedance of the transmitting electric circuit. In the middle cerebral artery (MCA) measurement photograph results, as the transmission power is increasing, the detection results become clearer and clearer. A comparison at the same transmission power for different transducers shows that the detection photograph obtained by the crystal transducer was clearer than that obtained with a commercial transducer, which should make it easier for doctors to find the cerebral arteries.

Graphene aerogels have many advantages, such as low density, high elasticity and strong adsorption. They are considered to be widely applicable in many fields. At present, the most valuable research area aims to find a convenient and effective way to prepare graphene aerogels with excellent properties. In this work graphene/carbon nanotube aerogels are prepared through hydrothermal reduction, freeze-drying and high temperature heat treatment with the blending of graphene oxide and carbon nanotubes. A new reducing agent-ascorbic acid is selected to explore the best preparation process. The prepared aerogels have compression and resilience and oil absorption properties due to the addition of carbon nanotubes as designed.

It has been proven that certain biomarkers in people’s breath have a relationship with diseases and blood glucose levels (BGLs). As a result, it is possible to detect diseases and predict BGLs by analysis of breath samples captured by e-noses. In this paper, a novel optimized medical e-nose system specified for disease diagnosis and BGL prediction is proposed. A large-scale breath dataset has been collected using the proposed system. Experiments have been organized on the collected dataset and the experimental results have shown that the proposed system can well solve the problems of existing systems. The methods have effectively improved the classification accuracy.