Environmental policy uncertainty and green transformation dilemma of Chinese enterprises.

With the intensification of environmental issues, environmental policies have played an increasingly important role in the Chinese economic system. The previous literature focuses on the impact of environmental policies on the green transformation of enterprises but pays little attention to policy-related environmental risks. In this way the impact of environmental policy uncertainty on enterprise green transformation remains a black box, which forms the initial motivation of this essay. In this study, under the real options theory assumption, we use Word2vec machine learning technology and text information from the annual reports of Chinese listed companies to measure environmental policy uncertainty at the enterprise level and examine its impact on enterprise green transformation and mechanism. Results indicate that environmental policy uncertainty has a significant negative impact on the green innovation and total factor productivity of the Chinese enterprises, which can lead to a green upgrading dilemma. The mechanism analysis shows that EPU suppresses enterprise green transformation by crowding out research and experimental development investment and intensifying enterprise financialization. In addition, The moderating effect analysis reveals that strong external market competition, capital market attention, and environmental information transparency can alleviate the negative impact of environmental policy uncertainty on enterprise green transformation to a certain extent. Furthermore, the short-sighted behavior of enterprise management can strengthen the negative impact of environmental policy uncertainty on green transformation. Finally, policy implications are provided to shed light on this essay's theoretical and practical values: the government should maintain the clarity, consistency, and stability of environmental policies to provide strong guarantees for the green transformation of enterprises.This paper broadens the scope of research on green transformation, offers new development philosophy for enterprises' green transformation, and provides new directions for the synergistic development of environmental policies and green transformation.

Green credit guideline and enterprise export green-sophistication.

Green credit is a major policy innovation to guide enterprises to participate in environmental governance actively. This study uses the data of Chinese A-share listed companies from 2007 to 2016, takes the green credit guideline (GCG) issued in 2012 as a quasi-natural experiment, and uses a difference in difference (DID) model to test the effect of GCG on the enterprises' export green-sophistication (EGS) and its internal and external mechanisms. The study finds that GCG improves enterprises' EGS and research and development (R&D) investment is the intermediation channel for GCG to affect EGS. Results of heterogeneity analysis show that the role of GCG in promoting EGS is significantly reflected in enterprises that the government does not subsidize, enterprises in areas with a low degree of financial marketization development, state-owned enterprises, and enterprises with a high degree of equity incentive.

Incidence and mortality of ovarian cancer at the global, regional, and national levels, 1990-2017.

BACKGROUND: Ovarian cancer (OC) is a commonly diagnosed gynecologic cancer. Knowing the incidence and mortality rates of OC is critical to understanding the disease burden and updating prevention strategies. METHODS: We retrieved the age-standardized incidence and mortality rates (ASIR and ASMR, respectively) of OC from the Global Burden of Disease study online database. Estimated average percentage change (EAPC) was used to quantify the trends of OC incidence and mortality from 1990 to 2017. RESULTS: Worldwide, the number of incident cases and deaths from OC increased from 152.1 and 95.5 thousand in 1990 to 286.1 and 176.0 thousand in 2017, respectively. Both the ASIR and ASMR decreased slightly during the study period (EAPC = -0.10, 95% CI, -0.16, -0.03; EAPC = -0.32, 95% CI, -0.38, -0.27). The greatest decreases of ASIR and ASMR were observed in Western Europe (EAPC = -1.22, 95% CI, -1.31, -1.14; EAPC = -1.31, 95% CI, -1.37, -1.25). A total of 137, 10, and 48 countries or territories experienced an increase, remained stable, and experienced a decrease in OC ASIR, respectively, between 1990 and 2017. For ASMR, a total of 129, 9, and 57 countries or territories experienced an increase, remained stable, and experienced a decrease, respectively, during the same period. The greatest increases in the ASIR and the ASMR were found in countries located in the Caribbean and Latin America. CONCLUSIONS: The incidence and mortality of OC significantly decreased in developed countries. However, remarkable increases were observed in more than two-thirds of all countries, suggesting that OC will be more frequently diagnosed in developing countries.

Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature.

Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score. Moreover, the proposed approach extracts convolutional features from a deep convolutional neural network (DCNN) to accurately estimate the position and scale of the target. Experimental results demonstrate that the proposed tracker has achieved satisfactory performance in a wide range of challenging tracking scenarios.

An Automatic Multi-Target Independent Analysis Framework for Non-Planar Infrared-Visible Registration.

In this paper, we propose a novel automatic multi-target registration framework for non-planar infrared-visible videos. Previous approaches usually analyzed multiple targets together and then estimated a global homography for the whole scene, however, these cannot achieve precise multi-target registration when the scenes are non-planar. Our framework is devoted to solving the problem using feature matching and multi-target tracking. The key idea is to analyze and register each target independently. We present a fast and robust feature matching strategy, where only the features on the corresponding foreground pairs are matched. Besides, new reservoirs based on the Gaussian criterion are created for all targets, and a multi-target tracking method is adopted to determine the relationships between the reservoirs and foreground blobs. With the matches in the corresponding reservoir, the homography of each target is computed according to its moving state. We tested our framework on both public near-planar and non-planar datasets. The results demonstrate that the proposed framework outperforms the state-of-the-art global registration method and the manual global registration matrix in all tested datasets.

Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking.

Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion. Moreover, an optimization strategy based on the Gauss-Seidel method was developed for obtaining robust and efficient online learning. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms state-of-the-art trackers in object tracking benchmarks (OTBs).

Comparative proteomics reveals that fatty acid metabolism is involved in myocardial adaptation to chronic hypoxic injury.

Congenital heart disease (CHD) is the most serious form of heart disease, and chronic hypoxia is the basic physiological process underlying CHD. Some patients with CHD do not undergo surgery, and thus, they remain susceptible to chronic hypoxia, suggesting that some protective mechanism might exist in CHD patients. However, the mechanism underlying myocardial adaptation to chronic hypoxia remains unclear. Proteomics was used to identify the differentially expressed proteins in cardiomyocytes cultured under hypoxia for different durations. Western blotting assays were used to verify protein expression. A Real-Time Cell Analyzer (RTCA) was used to analyze cell growth. In this study, 3881 proteins were identified by proteomics. Subsequent bioinformatics analysis revealed that proteins were enriched in regulating oxidoreductase activity. Functional similarity cluster analyses showed that chronic hypoxia resulted in proteins enrichment in the mitochondrial metabolic pathway. Further KEGG analyses found that the proteins involved in fatty acid metabolism, the TCA cycle and oxidative phosphorylation were markedly upregulated. Moreover, knockdown of CPT1A or ECI1, which is critical for fatty acid degradation, suppressed the growth of cardiomyocytes under chronic hypoxia. The results of our study revealed that chronic hypoxia activates fatty acid metabolism to maintain the growth of cardiomyocytes.

[Determination of three dechloranes in environmental water by magnetic solid-phase extraction-gas chromatography-negative chemical ionization mass spectrometry based on the covalent organic framework material].

Dechloranes are additive-type chlorine flame retardants that are widely used in processing industrial products, such as electronic equipment and textiles. Dechloranes, which can enter the human body through various routes, pose significant health risks because of their toxicity, persistence, and bioaccumulation. In 2023, dechlorane plus was listed in the Stockholm Convention on Persistent Organic Pollutants. In the same year, China recognized this compound as a priority-controlled substance. Dechloranes are commonly found at trace levels in water, which is extremely harmful to the environment and human health. Therefore, the development of detection methods for dechloranes is crucial. Magnetic solid-phase extraction (MSPE) has attracted considerable attention because of its low organic solvent consumption, simplicity of adsorbent separation, and ease of operation. In general, the selectivity and efficiency of MSPE depend on the characteristics of the adsorbent. Covalent organic frameworks (COFs) have regular porosity, structural predictability and stability, high specific surface areas, and adjustable pore sizes, which are advantageous for a wide range of separation and analysis applications. In this study, Fe3O4 magnetic nanoparticles and a COF material (TpBD) were combined to prepare Fe3O4@TpBD as an adsorbent for dechloranes. Subsequently, an effective method for analyzing dechlorane in environmental water was established by coupling MSPE with gas chromatography-negative chemical ionization mass spectrometry (GC-NCI/MS). The successful synthesis of Fe3O4@TpBD was confirmed using transmission electron microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and vibrating sample magnetometry. A single-factor method was used to optimize the extraction conditions, including the Fe3O4@TpBD dosage, pH of water sample, elution solvent type and volume, extraction time, elution time, and ionic strength. The target analytes were separated on a TG-5SILMS column (30 m×0.25 mm×0.25 µm) and quantified using the external standard method in the selected-ion monitoring (SIM) mode. Under the optimal extraction conditions, the method validation results showed a linear range of 2-1000 ng/L. The limits of detection (LODs) and quantification (LOQs) were 0.18-0.27 ng/L and 0.60-0.92 ng/L, respectively, for the three analytes. The intra-day and inter-day precisions at three spiked levels were 4.2%-16.2% and 6.9%-15.7%, respectively. This method was successfully applied to the determination of dechloranes in environmental water samples (laboratory tap water, reservoir water, wastewater treatment plant effluent, and landfill leachate treatment effluent). The recoveries of the three dechloranes at different spiked levels ranged from 77.8% to 113.3% with relative standard deviations (RSDs) of 2.5%-16.3% (n=3). With the advantages of operational simplicity, high sensitivity, and good reproducibility, the proposed method is suitable for the qualitative and quantitative determination of dechloranes in environmental water.

Room-Temperature Magneto-Photoresponse in All-2D Optoelectronic Devices for In-Sensor Vision Systems.

Interplay between magnetism and photoelectric properties introduces the effective control of photoresponse in optoelectronic devices via magnetic field, termed as magneto-photoresponse. It enriches the application scenarios and shows potential to construct in-sensor vision systems for artificial intelligence with gate-free architecture. However, achieving a simultaneous existence of room-temperature magnetism and notable photoelectric properties in semiconductors is a great challenge. Here, the room-temperature magneto-photoresponse is accomplished in all-2D optoelectronic devices, employing 2D ferromagnet Fe3GaTe2 as the source and drain, with WSe2 forming the channel. The interplay between room-temperature magnetism and photoelectric properties is realized by introducing the unique magneto-band structure effect from 2D interface, resulting in magneto-tunable charge transfer between Fe3GaTe2 and WSe2. The photocurrent in this 2D optoelectronic device exhibits robust response to both the direction and amplitude of external magnetic fields. Utilizing constructed 2D optoelectronic devices with magneto-photoresponse, traditional gate-controlled phototransistors are replaced and a prototype in-sensor vision system with visual adaptation, significantly improving the recognition accuracy to over four times in low-contrast environments is established. These findings pave a way for achieving high-temperature magneto-photoresponse, thereby guiding the construction of robust in-sensor vision systems toward high performance and broad applications.

Can network infrastructure construction improve the low-carbon innovation of Chinese enterprises?

This study considers the implementation of the "Broadband China" strategy as an exogenous policy shock and examines the impact of network infrastructure construction (NIC) on the low-carbon innovation (LCI) of enterprises and its underlying mechanisms by using a progressive difference-in-difference model based on the data of Chinese listed enterprises from 2009 to 2020. This study finds that NIC can improve the LCI of enterprises. After the elimination of the sample selection bias and selection of the urban slope as the exogenous instrumental variable, the conclusions remained robust. The results of the mechanism test show that upgrading the human capital level, reducing transaction costs, and alleviating financing constraints are the three important paths through which NIC can help enterprises improve their LCI level. The heterogeneity analysis determines that NIC has considerable comparative advantages for enterprises with executives who have a financial background and enterprises with high knowledge stock. In addition, LCI improvement can further enhance enterprise value. The research conclusions can broaden the microscopic research perspective of enterprise transformation and upgrading theory and provide reliable empirical evidence for China's low-carbon economic transformation.

Can the green finance improve export green-sophistication? New insights from China.

The green financial policy is one of the important policy tools for China to achieve the national carbon peak goal and carbon neutrality through financial means. How financial development affects the growth of international trade has been an important research topic. This paper uses the Pilot Zones for Green Finance Reform and Innovations (PZGFRI) implemented in 2017 as a natural experiment drawing on the relevant data of Chinese provinces' panel data from 2010 to 2019. It adopts a difference in difference (DID) model to assess the impact of green finance on export green-sophistication (EGS). The results report that the PZGFRI significantly improves EGS, and the result remains robust after robustness checks such as parallel trend and placebo. The PZGFRI improves EGS by boosting total factor productivity, industrial structure upgrading, and green technology innovation. Moreover, the role of PZGFRI in promoting EGS is significantly reflected in the central and western regions and the regions with low-marketization levels. This study confirms that green finance is an important factor influencing the quality improvement of China's exports, which provides effective evidence from the reality level for China to vigorously promote the construction of a green financial system in recent years.

Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning.

BACKGROUND: The morphological structure phenotype of maize tassel plays an important role in plant growth, reproduction, and yield formation. It is an important step in the distinctness, uniformity, and stability (DUS) testing to obtain maize tassel phenotype traits. Plant organ segmentation can be achieved with high-precision and automated acquisition of maize tassel phenotype traits because of the advances in the point cloud deep learning method. However, this method requires a large number of data sets and is not robust to automatic segmentation of highly adherent organ components; thus, it should be combined with point cloud processing technology. RESULTS: An innovative method of incomplete annotation of point cloud data was proposed for easy development of the dataset of maize tassels,and an automatic maize tassel phenotype analysis system: MaizeTasselSeg was developed. The tip feature of point cloud is trained and learned based on PointNet + + network, and the tip point cloud of tassel branch was automatically segmented. Complete branch segmentation was realized based on the shortest path algorithm. The Intersection over Union (IoU), precision, and recall of the segmentation results were 96.29, 96.36, and 93.01, respectively. Six phenotypic traits related to morphological structure (branch count, branch length, branch angle, branch curvature, tassel volume, and dispersion) were automatically extracted from the segmentation point cloud. The squared correlation coefficients (R2) for branch length, branch angle, and branch count were 0.9897, 0.9317, and 0.9587, respectively. The root mean squared error (RMSE) for branch length, branch angle, and branch count were 0.529 cm, 4.516, and 0.875, respectively. CONCLUSION: The proposed method provides an efficient scheme for high-throughput organ segmentation of maize tassels and can be used for the automatic extraction of phenotypic traits of maize tassel. In addition, the incomplete annotation approach provides a new idea for morphology-based plant segmentation.

Research on the evolutionary history of the morphological structure of cotton seeds: a new perspective based on high-resolution micro-CT technology.

Cotton (Gossypium hirsutum L.) seed morphological structure has a significant impact on the germination, growth and quality formation. However, the wide variation of cotton seed morphology makes it difficult to achieve quantitative analysis using traditional phenotype acquisition methods. In recent years, the application of micro-CT technology has made it possible to analyze the three-dimensional morphological structure of seeds, and has shown technical advantages in accurate identification of seed phenotypes. In this study, we reconstructed the seed morphological structure based on micro-CT technology, deep neural network Unet-3D model, and threshold segmentation methods, extracted 11 basics phenotypes traits, and constructed three new phenotype traits of seed coat specific surface area, seed coat thickness ratio and seed density ratio, using 102 cotton germplasm resources with clear year characteristics. Our results show that there is a significant positive correlation (P< 0.001) between the cotton seed size and that of the seed kernel and seed coat volume, with correlation coefficients ranging from 0.51 to 0.92, while the cavity volume has a lower correlation with other phenotype indicators (r<0.37, P< 0.001). Comparison of changes in Chinese self-bred varieties showed that seed volume, seed surface area, seed coat volume, cavity volume and seed coat thickness increased by 11.39%, 10.10%, 18.67%, 115.76% and 7.95%, respectively, while seed kernel volume, seed kernel surface area and seed fullness decreased by 7.01%, 0.72% and 16.25%. Combining with the results of cluster analysis, during the hundred-year cultivation history of cotton in China, it showed that the specific surface area of seed structure decreased by 1.27%, the relative thickness of seed coat increased by 8.70%, and the compactness of seed structure increased by 50.17%. Furthermore, the new indicators developed based on micro-CT technology can fully consider the three-dimensional morphological structure and cross-sectional characteristics among the indicators and reflect technical advantages. In this study, we constructed a microscopic phenotype research system for cotton seeds, revealing the morphological changes of cotton seeds with the year in China and providing a theoretical basis for the quantitative analysis and evaluation of seed morphology.

Monolithic 3D Integration of Analog RRAM-Based Computing-in-Memory and Sensor for Energy-Efficient Near-Sensor Computing.

In the era of the Internet of Things, vast amounts of data generated at sensory nodes impose critical challenges on the data-transfer bandwidth and energy efficiency of computing hardware. A near-sensor computing (NSC) architecture places the processing units closer to the sensors such that the generated data can be processed almost in situ with high efficiency. This study demonstrates the monolithic three-dimensional (M3D) integration of a photosensor array, analog computing-in-memory (CIM), and Si complementary metal-oxide-semiconductor (CMOS) logic circuits, named M3D-SAIL. This approach exploits the high-bandwidth on-chip data transfer and massively parallel CIM cores to realize an energy-efficient NSC architecture. The 1st layer of the Si CMOS circuits serves as the control logic and peripheral circuits. The 2nd layer comprises a 1 k-bit one-transistor-one-resistor (1T1R) array with InGaZnOx field-effect transistor (IGZO-FET) and resistive random-access memory (RRAM) for analog CIM. The 3rd layer comprises multiple IGZO-FET-based photosensor arrays for wavelength-dependent optical sensing. The structural integrity and function of each layer are comprehensively verified. Furthermore, NSC is implemented using the M3D-SAIL architecture for a typical video keyframe-extraction task, achieving a high classification accuracy of 96.7% as well as a 31.5× lower energy consumption and 1.91× faster computing speed compared to its 2D counterpart.

Edge learning using a fully integrated neuro-inspired memristor chip.

Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. We developed a fully integrated memristor chip with the improvement learning ability and low energy cost. The schemes in the STELLAR architecture, including its learning algorithm, hardware realization, and parallel conductance tuning scheme, are general approaches that facilitate on-chip learning by using a memristor crossbar array, regardless of the type of memristor device. Tasks executed in this study included motion control, image classification, and speech recognition.

Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning.

In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.

An in-memory computing architecture based on a duplex two-dimensional material structure for in situ machine learning.

The growing computational demand in artificial intelligence calls for hardware solutions that are capable of in situ machine learning, where both training and inference are performed by edge computation. This not only requires extremely energy-efficient architecture (such as in-memory computing) but also memory hardware with tunable properties to simultaneously meet the demand for training and inference. Here we report a duplex device structure based on a ferroelectric field-effect transistor and an atomically thin MoS2 channel, and realize a universal in-memory computing architecture for in situ learning. By exploiting the tunability of the ferroelectric energy landscape, the duplex building block demonstrates an overall excellent performance in endurance (>1013), retention (>10 years), speed (4.8 ns) and energy consumption (22.7 fJ bit-1 µm-2). We implemented a hardware neural network using arrays of two-transistors-one-duplex ferroelectric field-effect transistor cells and achieved 99.86% accuracy in a nonlinear localization task with in situ trained weights. Simulations show that the proposed device architecture could achieve the same level of performance as a graphics processing unit under notably improved energy efficiency. Our device core can be combined with silicon circuitry through three-dimensional heterogeneous integration to give a hardware solution towards general edge intelligence.

Hydrothermal Aging Mechanisms of All-Steel Radial Tire Composites.

This work focused on the effects of the hydrothermal environment on the aging of all-steel radial tire (ASRT) composites. Composite specimens were conditioned by immersion in deionized water at 30, 60 and 90 °C. Its water absorption, thermal and mechanical properties (tensile strength, elasticity modulus, elongation at break and interfacial shear strength), morphological structure, as well as molecular cross-linking reaction were investigated before and after aging. Results indicated that there was no dynamic equilibrium of water absorption of ASRT composites after deviating from the Fickian model. The molecular cross-linking density of the rubber matrix showed an increase in the early stage of aging. Then, the mechanical properties suffered of a drop due to the degradation of the rubber matrix and the poor interface between the steel fiber and rubber matrix. Additionally, a systematic hygrothermal aging mechanism was proposed.

Rapid detection of HPV16/18 based on a CRISPR-Cas13a/Cas12a dual-channel system.

PURPOSE: cervical cancer is the leading cause of cancer deaths in women in the developing world, with high-risk HPV16 and HPV18 accounting for approximately 70% of all cervical cancers. Early detection of HPV, especially high-risk HPV types, is essential to prevent disease progression. METHODS: in this study, we established a highly sensitive and specific nucleic acid assay based on a CRISPR-Cas13a/Cas12a dual-channel system combined with multiplex RAA for rapid detection and typing of HPV16/18, which provides a new idea for cervical cancer screening. To meet the application of field testing, we designed a portable fluorescence imaging assay that can judge the test results directly with the naked eye or through cell phone imaging. RESULTS: the lower limit of detection for both HPV16 and HPV18 based on the CRISPR-Cas12a/Cas13a dual-channel assay was 100 copies per µL. The dual-channel assay was validated with 55 clinical samples, showing 97.06% sensitivity, 100% specificity, 100% positive predictive value, and 96.55% negative predictive value. The results of the portable fluorescence imaging assay were fully comparable to those of the real-time fluorescent RAA-based CRISPR-Cas12a/Cas13a dual-channel assay. CONCLUSIONS: this developed portable dual gene assay platform may provide new technical support for cervical cancer screening in resource-limited settings.

Diagnostic value of endometrial volume and flow parameters under 3D ultrasound acquisition in combination with serum CA125 in endometrial lesions.

OBJECTIVE: This study aims to discuss the differential diagnosis value of endometrial volume and flow parameters in combination with serum carbohydrate antigen 125 (CA125) in endometrial benign and malignant lesions. MATERIALS AND METHODS: The data of 250 patients with endometrial lesions were retrospectively analyzed. Carbohydrate antigen 125 (CA125) was determined before the operation. The morphology, hemodynamics, volume and flow parameters of the endometrium were measured by transvaginal three-dimensional-power Doppler angiography (3D-PDA). The endometrial volume (EV), 3D-PDA vascular index (VI), flow index (FI) and vascularization flow index (VFI) were calculated using the virtual organ computer-aided analysis software (VOCAL). RESULTS: According to the pathological results, 202 patients (80.8%) had benign endometrial lesions and 48 patients (19.2%) had endometrial cancer (EC). The endometrium of EC patients was thicker (15.64 ± 7.26 mm vs. 9.24 ± 5.06 mm, P < 0.001), the endometrial volume was larger (9.23 ± 4.08 ml vs. 2.26 ± 3.42 ml, P < 0.001), and the flow parameters VI, FI and VFI were higher, when compared to those of benign lesions (P < 0.001). The area under the receiver operating characteristic curve (AUROCC) of VI receptors was 0.86, while the AUC of endometrial thickness (ET) was only 0.66. Therefore, the best variable for distinguishing benign and malignant endometrial lesions was VI. The level of CA125 in the EC group significantly increased (40.57 ± 17.45 vs. 17.87 ± 7.64, P < 0.001), and the level of CA125 increased (P < 0.05) with the increase in clinical grade, degree of tumor differentiation, and pelvic lymph node metastasis (P < 0.05). However, the difference in myometrial invasion was not statistically significant (P > 0.05). CONCLUSION: Transvaginal 3D-PDA can clearly show the morphological and hemodynamic characteristics of endometrial lesions, and assist in the detection of EC in combination with serum CA125. This may have important clinical application value.