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Developing innovative sonoreactors to enhance acoustic processing efficiency holds immense importance in the field of sonochemistry. Traditional immersed sonoreactors (TISs) mainly produce cavitation at the probe tip, with a relatively weak cavitation around the probe, resulting in posing challenges for high-efficiency cavitation treatment. Here we propose an acoustic black hole immersed sonoreactor (ABHIS) in longitudinal-flexural coupled vibration, enabling high-efficiency cavitation treatment by unleashing the cavitation potential of the probe. The symmetrical structure of the probe is altered to introduce a coupling of flexural vibration mode, and an acoustic black hole (ABH) profile is integrated to further enhance both flexural wave number and amplitude. In this paper, we present a systematic theoretical design method for ABHIS and compare its performance with TIS using finite element method (FEM). An ABHIS prototype is fabricated and subjected to experimental tests and cavitation observation. The results demonstrate that our theoretical analysis model accurately predicts the frequency characteristics of ABHIS. The proposed ABHIS exhibits satisfactory dynamic characteristics, with significantly increased vibration displacement and acoustic radiation ability compared to TIS. Importantly, the ABH design significantly expands ultrasonic cavitation regions and enhances acoustic radiation intensity of ABHIS, resulting in a substantial improvement in acoustic processing efficiency.

Clarifying ecological status and dynamic changes is crucial to ecological conservation and high-quality development of the Yellow River basin. However, due to the limitations of the existing model's solution mechanism, current ecological environment assessment models have poor automation capabilities. So the research tends to select images from limited periods, making it difficult to conduct batch calculations of high-frequency, long-term sequence, and multi-time scales of ecological environment quality automatically, dynamically, and rapidly. This study proposed a new Spatial-Temporal Comprehensive Evaluation of Eco-environment Quality Index (SCEQI) model. Then taking Henan section of the Yellow River as an example, 960 images from 2001 to 2021 were used to calculate the comprehensive ecological environment quality quickly and in batches. Subsequently, comprehensive ecological environment quality data at different time scales (monthly, quarterly, and annually) were obtained using the average value method in SCEQI. The results showed that the regional ecological environment quality exhibited significant temporal heterogeneity. Spring is the best, followed by autumn and summer, and winter is the worst. In terms of monthly ecological environment quality, the ranking from best to worst was May, September, October, November, December, January, August, April, July, March, June, and February, exhibiting a pattern of ‘double peaks’ and ‘double valleys’. To further elucidate the spatial-temporal distribution pattern, fluctuations, change trends, and future evolution trends of the ecological environment quality, we conducted coefficient of variation fluctuation analysis, Theil-Sen's slope estimation, Mann-Kendall test, and Hurst index calculation. This study realized rapid and batch calculations of ecological environment quality, thereby enabling comprehensive research on ecological environment quality with high-frequency, long time-sequence, and multiple time-scale. And the findings can provide scientific methodologies and data support for formulating policies of ecological conservation and high-quality development in the Yellow River basin.

ZrO2-WO3 mixed metal oxides are important solid acid catalysts, and typically, ammonia gas calcination reduces their acidity. However, in this study, we introduce an updated NH3 calcination technique that can increase the acidity of mesoporous ZrO2-WO3 solid acids. Their structures and acid properties were thoroughly characterized through X-ray diffraction (XRD), N2 adsorption measurement, pyridine-adsorbed infrared spectroscopy (Py-IR), temperature-programmed desorption of ammonia (NH3-TPD), X-ray photoelectron spectroscopy (XPS), high-resolution transmission electron microscopy (HRTEM) and ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis-DRS) analysis. The resulting materials exhibit remarkable activity in the Friedel-Crafts (F-C) reaction of anisole and benzyl alcohol. Our investigations showed that the subnanometer WOx clusters act as the most active sites for the F-C reaction, and NH3-air calcination is essential for the formation of WOx clusters. This treatment not only enhances acidity but also provides a novel method of obtaining acidic ZrO2-WO3 mixed metal oxides. Most importantly, it presents a fresh approach for regulating the formation of WO3 clusters and catalytic activity.

Shape is one of the core features of the buildings which are the main elements of the map. The building shape recognition is widely used in many spatial applications. Due to the irregularity of the building contour, it is still challenging for building shape recognition. Inspired by graph signal processing theory, we propose a deep graph filter neural network (DGFN) for the shape recognition of buildings in maps. Firstly, we regard shape recognition as a combination of subjective and objective graph signal filtering process. Secondly, we construct a shape features extraction framework from the perspective of shape details, shape structure and shape local information. Thirdly, DGFN model can fulfill the tasks of shape classification and shape embedding of building at the same time. Finally, multi angle experiments verify our viewpoint of shape recognition mechanism, and the comparison with similar algorithms proves the high accuracy and availability of DGFN model.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another for extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we use a set of metrics considering both quantitative and qualitative aspects of the road network. On road types with similar sample sizes, the HAN model outperforms other models in both transductive and inductive tasks. Its accuracy (ACC) is higher by 1.62% and 0.67%, and its F1-score is higher by 1.43% and 0.81%, respectively. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

The mitre squid Uroteuthis chinensis is distributed widely in the Chinese coastal areas and contributes to the majority of the Chinese neritic squid fishery, especially in the South China Sea. However, little has been investigated about its life history traits, despite its commercial importance. In this study, using samples of U. chinensis collected through bottom trawlers from December 2019 to March 2021 in the northwestern South China Sea, biological traits including hatching date, growth pattern and dorsal mantle length at maturity of male individuals were explored by age determination based on statolith microstructure analysis. The results indicated that the U. chinensis showed a year-round spawning pattern with three main cohorts (spring, summer and autumn) that can be identified according to their hatching season. The range of the dorsal mantle length (DML) was 117–259 mm for females and 70–312 for males, and the body weight ranged from 55.1 to 480.5 g for females and from 19.3 to 560.2 g for males; the age ranges were estimated between 82 and 173 days for females and between 76 and 175 days for males. As for the length–weight relationship, males possessed a larger mantle length than females, while the body weight of females increased more compared to males at the same mantle length. The population recruits to the fishing ground, with individuals reaching sexual maturity at around 3 months, and the lifespan is less than 200 days. The growth model is well described by the exponential equation, which revealed that spring and autumn cohorts showed a higher growth rate than summer cohorts. As for male individuals, the smallest dorsal mantle length at maturity (DML50%) was recorded in the spring cohort (DML50% = 116 mm) compared with the summer (DML50% = 129 mm) and autumn cohorts (DML50% = 149 mm). This study provides key and updated fishery biological information of U. chinensis in the South China Sea and contributes to the understanding of U. chinensis resources.

Glucose from plants or food industry wastes is a cost-effective hole scavenger for photocatalytic water splitting, but the related H2 evolution rate is always restricted by the fast recombination of photo electron-hole pairs and the poor intrinsic activity of traditional Pt cocatalysts. Herein, we fabricated Au-Pt heterostructure cocatalyst that consist of one ∼3 nm Au core and several ∼1 nm Pt islands on ultrathin g-C3N4 through one-pot photo-deposition. The heterostructure exhibited superior H2 evolution activity to monometallic Au or Pt nanoparticles. Our experiments and theoretical calculations demonstrated that the enhanced activity was caused by the synergistic effect between Au and Pt, rather than the well-known plasmonic effect of Au. The heterostructure not only favored the trapping of photo electrons from g-C3N4, but also had suitable adsorption strength of H*, exhibiting high intrinsic activity towards H2 evolution. Our research provides a strategy to boost photocatalytic H2 evolution by regulating the synergy of bimetallic cocatalysts.

We studied the layer structure of bubbles just below water/air and water/EPE (Expand aple poly ephylene) interfaces using high-speed photography. The layer structure was generated by floating spherical clusters, the source bubbles of which were identified to come from the attachment of bubble nuclei at the interface, the floating of bubbles in the bulk liquid, or bubbles generated on the surface of the ultrasonic transducer. The boundary shape affected the layer structure, which assumed a similar profile below the water/EPE interface. We developed a simplified model composed of a bubble column and bubble chain to describe interface impacts and the interaction of bubbles in a typical branching structure. We found that the resonant frequency of the bubbles is smaller than that of an isolated single bubble. Moreover, the primary acoustic field plays an important role in the generation of the structure. A higher acoustic frequency and pressure were found to shorten the distance between the structure and the interface. A hat-like layer structure of bubbles was more likely to exist in the low-frequency (28 and 40 kHz) intense inertial cavitation field, in which bubbles oscillate violently. By contrast, structures composed of discrete spherical clusters were more likely to form in the relatively weak cavitation field at 80 kHz, in which stable and inertial cavitation coexisted. The theoretical predictions were in good agreement with the experimental observations.

Since 2007, Ulva prolifera disasters have occurred every year in the South Yellow Sea of China, the largest green tide disaster in the world. The inter-annual differences make monitoring and early warning for such disasters difficult. This study used remote sensing data (2015–2019) to determine its spatio-temporal variations in all life cycles. The results showed a lay effect between the NDVI-mean and the coverage area of U. prolifera. The spatio-temporal distribution of U. prolifera showed stages and regional differences. From late April to early May, U. prolifera first emerged near the Subei Shoal. After development in the middle of the Yellow Sea, U. prolifera broke out in the eastern sea area of Shandong and Jiangsu, declined in the Shandong sea area, and disappeared near Qingdao. The cycle lasted for approximately 90 days. The sea surface temperature was the necessary condition for the disaster, and the sea wind field was the main driving force for its horizontal drift. This study overcomes the poor timing and continuity of remote sensing data in the monitoring of U. prolifera. It provides a theoretical reference for forecasting the outbreak period of U. prolifera and can aid policy-makers to avert such disasters in advance. HIGHLIGHTS There is a significant hysteresis effect between NDVIM and the U. prolifera coverage area.; U. prolifera disasters at different growth stages are geographically regional.; Sea surface temperature and sea wind field are the main influencing factors of U. prolifera disaster.; NDVIM can provide a scientific theoretical reference for predicting the future outbreak period of U. prolifera.;

Cephalopod fisheries in the China Seas have witnessed an increasing trend in the catches of coastal cephalopods since the 1990s, with Loliginidae squid emerging as the main commercial target species. However, climate change and overfishing have led to a dramatic reduction in Loliginidae squid resources, highlighting the need to improve monitoring, protection, and management of this species. The Loligo beka and Uroteuthis duvaucelii are widely distributed along the coastal areas of the China Seas, and have commercial and ecological importance. Despite having overlapping distributions, similar life histories, and a strong dependence on the marine environment, there is limited knowledge about their growth and responses to environmental changes, hindering the effective management of their resources. In this study, we investigated the interspecies and intra-species differences in condition factor and their responses to temperature changes by analyzing data collected from wide coastal areas of the China Seas from June 2019 to November 2020. The findings showed that both species exhibited allometric growth and reproduced throughout the year, with two main breeding peaks. There were significant monthly variations and latitude differences in the intra-species growth, with a higher proportion of small-sized individuals (between 5 and 10 g for L. beka and between 10 and 20 g for U. duvaucelii) in low-latitude waters. The latitudinal differences in body weight and distribution between and within the two species were mainly due to natural habitats, especially temperature. Our mixed effect model results demonstrated that both species’ body weight increased with increasing temperature, suggesting that Loliginidae squid have significant environmental adaptability and can be used as an indicator species for studying environmental changes in the China Seas. These findings have significant implications for understanding the population dynamics, species development, and regionally specific management of Loliginidae squid fisheries in the China Seas.

The evolution of acoustic Lichtenberg figure (ALF) in ultrasound fields is studied using high-speed photography. It is observed that bubbles travel along the branch to the aggregation region of an ALF, promoting the possibility of large bubble or small cluster formation. Large bubbles move away from the aggregation region while surrounding bubbles are attracted into this structure, and a bubble transportation cycle arises in the cavitation field. A simplified model consisting of a spherical cluster and a chain of bubbles is developed to explain this phenomenon. The interaction of the two units is analyzed using a modified expression for the secondary Bjerknes force in this system. The model reveals that clusters can attract bubbles on the chain within a distance of 2 mm, leading to a bubble transportation process from the chain to the bubble cluster. Many factors can affect this process, including the acoustic pressure, frequency, bubble density, and separation distance. The larger the bubble in the cluster, the broader the attraction region. Therefore, the presence of large bubbles might enhance the process in this system. Local disturbances in bubble density could destroy the ALF structure. The predictions of the model are in good agreement with the experimental phenomena.

According to classical nucleation theory, a gas nucleus can grow into a cavitation bubble when the ambient pressure is negative. Here, the growth process of a gas nucleus in a micro-cavity was simplified to two “events”, and the full confinement effect of the surrounding medium of the cavity was considered by including the bulk modulus in the equation of state. The Rayleigh–Plesset-like equation of the cavitation bubble in the cavity was derived to model the radial oscillation and translational motion of the cavitation bubble in the local acoustic field. The numerical results show that the nucleation time of the cavitation bubble is sensitive to the initial position of the gas nucleus. The cavity size affects the duration of the radial oscillation of the cavitation bubble, where the duration is shorter for smaller cavities. The equilibrium radius of a cavitation bubble grown from a gas nucleus increases with increasing size of the cavity. There are two possible types of translational motion: reciprocal motion around the center of the cavity and motion toward the cavity wall. The growth process of gas nuclei into cavitation bubbles is also dependent on the compressibility of the surrounding medium and the magnitude of the negative pressure. Therefore, gas nuclei in a liquid cavity can be excited by acoustic waves to form cavitation bubbles, and the translational motion of the cavitation bubbles can be easily observed owing to the confining influence of the medium outside the cavity.

The performance of Low-Density Parity-Check (LDPC)-coded Orthogonal Frequency Division Multiplexing (OFDM) is investigated over turbulence channels in underwater wireless optical communications (UWOC). The relation between the bit error ratio (BER) and parameters such as the scintillation coefficient, signal-to-noise ratio (SNR), length of LDPC code, and order of OFDM is quantified by simulation. Results show that while the OFDM with subcarrier quadrature amplitude modulation (QAM-OFDM) has slightly better anti-turbulence performance than the OFDM with subcarrier phase shift keying modulation (PSK-OFDM), the LDPC-coded QAM-OFDM has a much better performance than the QAM-OFDM and the LDPC-coded PSK-OFDM, and, at SNR = 12, it decreases the BER by four orders of magnitude compared to the 16QAM-OFDM system when the scintillation coefficient σξ2 = 0.05.

The timely assessment of muscle fatigue makes sense for reducing the risk of musculoskeletal injury during exercise. In general, muscle fatigue is accompanied by physiological changes. These changes affect the acoustic transmission properties of the skeletal muscles. This study investigated the variation of the acoustic transmission spectrum (ATS) of human upper arm muscles during sustained static contractions (SC). Based on the B-ultrasound image and radiofrequency (RF) ultrasonic echoes, we abstracted the RF echo signals from the subcutaneous fatty/fascia (SFF) and deep fascia/bone (DFB) interfaces. By dividing the echo spectrum of the DFB by the spectrum of the SFF, we obtained the ATS of the upper arm muscles. Then, by fitting the ATS with both the linear function (A(f) = af + b) and power-law function (A(f) = αƒβ), we analyzed the variations of a, b, α, and β along with the SC process of skeletal muscle. As muscle fatigue increases, the slope a decreases and the intercept b increases linearly; the α increases exponentially and β decreases linearly. In addition, the variation magnitude of ATS relates to the maximum voluntary contraction (MVC) force and the strength of the SC motion. These results suggest that a comprehensive analysis of ATS is a potential metric for assessing muscle fatigue.

Zearalenone(ZEA) is a kind of mycotoxin widely existing in nature, its toxic effects can lead to the reproductive disorders in humans and animals. The aim of this study was to investigate the mechanism of scutellarin against ovarian granulosa cell(GCs) injury induced by ZEA based on network pharmacology, molecular docking method. The results show that 293 drug targets of scutellarin were found from PhamMapper database, and 583 disease targets were selected from Genecards database. Finally, 57 scutellarin targets were obtained for the repair of GCs injury with gene intersection. The protein-protein interaction(PPI), gene ontology(GO) and kyoto encyclopedia of genes and genomes(KEGG) analysis indicated that MAPK signaling pathway was most likely activated by scutellarin. Scutellarin with JNK or Caspase-3 had minimal and negative free binding energy in molecular docking analysis, indicating that they might be the acting targets of scutellarin. Cell viability was significantly decreased in ZEA treated cells. However, GCs viability, the level of estradiol(E2) and progesterone(P4) were significantly increased with addition of scutellarin to ZEA treated cells. Western blot analysis showed that scutellarin significantly reduced the expression of JNK, c-jun and Cleaved-caspasee-3 in GCs compared with ZEA treatment. In conclusion, scutellarin could alleviate the ovarian GCs injury by down-regulating the expression of JNK, c-jun and Cleaved-caspase-3 through the activation of MAPK/JNK signaling pathway. Our results will provide a theoretical foundation for the treatment of reproductive disorders with scutellarin.

Ultrasound images of muscle fascicles have been widely used to investigate muscle properties for diagnosis and rehabilitation assessment.The existing automatic fascicle detection and measure methods are based on line detection techniques which do not exactly coincide with the true state of the muscle images. This affects their detection and measure accuracy and depresses their robustness to background interference. In this work, a novel discrete transform namely Belt Linear Summation (BLS) transform is proposed. Unlike the line transform techniques which calculate the sum of pixels on a straight line in images, the BLS transform intends to determine the weighted summation of a belt of pixel values. Based on BLS transform, an automatic fascicle detection and measure method is designed. The performance of the proposed method is compared to the recent automatic fascicle detection and measure methods using both simulated images and clinical images. Experimental results show that the proposed method is robust to background and noise interference, accurate in terms of muscle pennation angle measurement, and feasible for analyzing clinical data.

Due to the influence of the atmospheric environment and pointing errors, the performance of free space optical communication is greatly limited. In this paper, we propose a parallel multi-hop hybrid free space optical (FSO)/radio frequency (RF) system to improve the system performance. The FSO sub-link and RF sub-link are modeled by Gamma–Gamma turbulence with pointing errors and Nakagami-m distributions, respectively. Based on the selective combination scheme, the probability density function (PDF) and cumulative distribution function (CDF) of the output signal-to-noise ratio (SNR) of the hybrid FSO/RF one-hop or direct link are obtained. Then, the PDF and CDF of the output SNR of the parallel multi-hop hybrid system are derived with the decoded forward (DF) protocol considered. Finally, the expressions of the average bit error rate (ABER) and outage probability are derived for the parallel multi-hop hybrid system, the hybrid FSO/RF direct link, and the FSO-only direct link. The results show that the parallel multi-hop hybrid system can effectively mitigate the negative impact of atmospheric turbulence and pointing errors and can significantly improve the system performance.

With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average.

In classical orthogonal frequency division multiplexing (OFDM) systems, inserting the cyclic prefix (CP) is necessary before each symbol to overcome the multi-path effect, which, however, occupies numerous time-frequency radio resources, resulting in hampered spectrum efficiency. To address this issue, in this paper, symbol repetition aware OFDM (SR-OFDM) is developed to lower the overhead of CP. In the proposed SR-OFDM, multiple symbols share the same CP with which we examine that the multi-path channels can also be overcome by a simple single-tap equalization without causing any interference. Moreover, after the discrete Fourier transform at the receiver, different symbols are proved to be separated in the time domain, which is beneficial for lowering the demodulation complexity. Furthermore, it is revealed that the above conclusions still hold even under timing synchronization errors, which makes the proposed SR-OFDM favorable in real systems. Extensive simulations validate the efficacy of our proposed SR-OFDM system under the multi-path channels with or without timing synchronization errors.

Based on the data collected by four trawl surveys during 2016–2017, we applied biomass size spectrum (BSS) and abundance–biomass comparison (ABC) curve to assess the status of fish communities’ status in Daya Bay, China. Our findings indicated a unimodal pattern and biomass size ranged from −2 to 10 grain levels and the pattern of the Sheldon-type BSS of fish in Daya Bay. Moreover, fishes in the range of four to eight size class were relatively abundant. The highest peak belonged to the two to four grain level (log2 size bins), mainly consisting of Leiognathus brevirostris, Callionymus meridionalis, Callionymus koreanus, Evynnis cardinalis, Trachurus japonicus, and other small fishes. The curves of the BSS in spring and winter were relatively flat and comprised a large curvature. The summer and autumn curves were comparatively steep, and the seasonal curvature was small. The curvatures of the curve were mainly related to a large number of small Evynnis cardinalis and a small number of large-sized Harpadon nehereus and Leiognathus ruconius. In our study, it was observed that the number and the size of the breeding population, trophic levels, migration habits, and other life history characteristics, as well as anthropogenic disturbances (especially overfishing), significantly affected the peak shape, slope, or curvature of the fish BSS, with overfishing being the main factor. The ABC curve exhibited that Daya Bay was in a critical state of disturbance throughout the year. The spring, summer, and autumn were in severe disturbance, while the winter was in moderate disturbance.

The main forms of poly-γ-glutamic acid (γ-PGA) applied in agriculture include agricultural γ-PGA and γ-PGA super absorbent polymer (SAP). Laboratory experiments were conducted with a check treatment CK (no γ-PGA added) and two different forms of γ-PGA added to sandy loam soil (T and TM stand for γ-PGA and γ-PGA SAP) at four different soil mass ratios (0.05% (1), 0.10% (2), 0.15% (3) and 0.20% (4)) to determine their effects on sandy loam soil hydro-physical properties. Both of them could reduce the cumulative infiltration of soil water. The total available water (TAW) which the soil water content (SWC) from field water capacity (FC) to permanent wilting point (PWP) after γ-PGA added into sandy loam soil had no significant different compared with CK, and the TAW was highest at the treatment of γ-PGA with 0.10% addition amount into sandy loam soil. However, the TAW of sandy loam soil increased dramatically with the γ-PGA SAP addition amount increasing. TM3 had the highest soil water absorption among the treatments with γ-PGA SAP. The T1 to T4 treatments with γ-PGA addition slightly prolonged retention time (RT) when SWC varied from FC to PWP compared with CK. For γ-PGA SAP addition treatments, the time for SWC varied from FC to PWP was 1.48 times (TM1), 1.88 times (TM2), 2.01 times (TM3) and 2.87 times (TM4) longer than that of CK, respectively. The results of this study will provide further information for the use of these materials in agricultural application.

Acoustic holography is an essential tool for controlling sound waves, generating highly complex and customizable sound fields, and enabling the visualization of sound fields. Based on acoustic sieve metasurfaces (ASMs), this paper proposes a theoretical design approach for zero-thickness broadband holograms. The ASM is a zero-thickness rigid screen with a large number of small holes that allow sound waves to pass through and produce the desired real image in the target plane. The hole arrangement rules are determined using a genetic algorithm and the Rayleigh–Sommerfeld theory. Because the wave from a hole has no extra phase or amplitude modulation, the intractable modulation dispersion can be physically avoided, allowing the proposed ASM-based hologram to potentially function in any frequency band as long as the condition of paraxial approximation is satisfied. Using a numerical simulation based on the combination of the finite element method (FEM) and the boundary element method (BEM), this research achieves broadband holographic imaging with a good effect. The proposed theoretical zero-thickness broadband hologram may provide new possibilities for acoustic holography applications.

Buildings are important entity objects of cities, and the classification of building shapes plays an indispensable role in the cognition and planning of the urban structure. In recent years, some deep learning methods have been proposed for recognizing the shapes of building footprints in modern electronic maps. Furthermore, their performance depends on enough labeled samples for each class of building footprints. However, it is impractical to label enough samples for each type of building footprint shapes. Therefore, the deep learning methods using few labeled samples are more preferable to recognize and classify the building footprint shapes. In this paper, we propose a relation network based method for the recognization of building footprint shapes with few labeled samples. Relation network, composed of embedding module and relation module, is a metric based few-shot method which aims to learn a generalized metric function and predict the types of the new samples according to their relation with the prototypes of these few labeled samples. To better extract the shape features of the building footprints in the form of vector polygons, we have taken the TriangleConv embedding module to act as the embedding module of the relation network. We validate the effectiveness of our method based on a building footprint dataset with 10 typical shapes and compare it with three classical few-shot learning methods in accuracy. The results show that our method performs better for the classification of building footprint shapes with few labeled samples. For example, the accuracy reached 89.40% for the 2-way 5-shot classification task where there are only two classes of samples in the task and five labeled samples for each class.

The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conducted to learn the feature representations of the buildings and recognize their shapes based on graph neural networks. Due to the principles of graph neural networks, it is necessary to construct a graph to represent the adjacency relationships between the points (i.e., the vertices of the polygons shaping the buildings), and extract a list of geometric features for each point. This paper proposes a deep point convolutional network to recognize building shapes, which executes the convolution directly on the points of the buildings without constructing the graphs and extracting the geometric features of the points. A new convolution operator named TriangleConv was designed to learn the feature representations of each point by aggregating the features of the point and the local triangle constructed by the point and its two adjacency points. The proposed method was evaluated and compared with related methods based on a dataset consisting of 5010 vector buildings. In terms of accuracy, macro-precision, macro-recall, and macro-F1, the results show that the proposed method has comparable performance with typical graph neural networks of GCN, GAT, and GraphSAGE, and point cloud neural networks of PointNet, PointNet++, and DGCNN in the task of recognizing and classifying building shapes in map space.

Abstract Background Type 1 diabetes mellitus (T1DM) induces serious skeletal muscle atrophy. Low-intensity pulsed ultrasound (LIPUS) is a common treatment for skeletal muscle injury and is effective in accelerating the rate of muscle growth. However, to the best of our knowledge, whether LIPUS can improve skeletal muscle atrophy in type 1 diabetic rats has not been investigated. Methods The rats were randomly divided into four groups: the normal control group (NC); the sham-treated diabetic control group (DC); the diabetic, insulin-treated group (DI) as a positive control; and the diabetic LIPUS therapy group (DL). The DL rats were treated with LIPUS (1 MHz, 30 mW/cm2) on the gastrocnemius for 20 min/day. Results After 6 weeks, the rats in the DC group showed severe muscle atrophy. However, LIPUS significantly improved type 1 diabetes-induced muscle atrophy, as evidenced by significantly enhanced muscle cross-sectional area, muscle mass, and strength. Moreover, compared with the DC group, LIPUS significantly activated Akt and upregulated the expression of mTOR, and LIPUS downregulated the expression of MSTN, its receptor ActRIIB, and FoxO1. Conclusions These results indicate that LIPUS improved muscle atrophy induced by type 1 diabetes, and the MSTN/Akt/mTOR&FoxO1 signaling pathway may play a role in this improvement.

The estimation of shear wave velocity is very important in ultrasonic shear wave elasticity imaging (SWEI). Since the stability and accuracy of ultrasonic testing equipment have been greatly improved, in order to further improve the accuracy of shear wave velocity estimation and increase the quality of shear wave elasticity maps, we propose a novel real-time curve tracing (RTCT) technique to accurately reconstruct the motion trace of shear wave fronts. Based on the curve fitting of each frame shear wave, the propagation velocity of two-dimensional shear waves can be estimated. In this paper, shear wave velocity estimation and shear wave image reconstruction are implemented for homogeneous regions and stiff spherical inclusion regions with different elasticity, respectively. The experimental result shows that the proposed shear wave velocity estimation method based on the real-time curve tracing method has advantages in accuracy and anti-noise performance. Moreover, by eliminating artifacts of shear wave videos, the velocity map acquired can restore the shape of inclusions better. The real-time curve tracing method can provide a new idea for the estimation of shear wave velocity and elastic parameters.

Background: To analyze the characteristic performance of magnetic resonance spectroscopy (MRS) and susceptibility weighted imaging (SWI) in cerebral alve-olar echinococcosis (CAE). Methods:We retrospectively analyzed 10 clinical-identified CAE cases MR per-formance, and summarized the MRS and SWI performance of CAE. Results:The 10 cases of CAE all had the history of primary HAE, among who 6 cases had single lesion (60%), while the rest 4 cases had multiple lesions (40%); and 4 cases were concomitant with lung metastases. MRI performance: T2WI lesions were coal-like low-signal shadow, with multiple small vesicles in-side the lesions; MRS performance: NAA, Cho and Cr significantly reduced, an abnormally high and steep crest was found at 1.4 ppm; the phase diagram and strength diagram of SWI showed isointensity. Conclusion:The MRS and SWI of CAE could provide important supplemental information for the diagnosis of CAE, especially the abnormally high and steep crest at 1.4 ppm provide the reliable image basis for the diagnosis and differen-tial diagnosis of CAE.