The Tenth Anniversary of Free Trade Pilot Zones in China:Fundamental Experiences and Enhancement Strategies

Since the 18th National Congress of the Communist Party of China,the country has established 21 Free Trade Pilot Zones(FTZs),achieving significant pioneering results in reform and opening up and creating a strong demonstrative effect nationwide.The basic experience from a decade of FTZs includes:adhering to the centralized and unified leadership of the CPC Central Committee;combining top-level design with encouragement of grassroots innovation;leveraging the distinct characteristics and strengths of FTZs to form a differentiated development pattern;maintaining the integration of opening up with domestic reforms;using openness to drive reforms;and organically combining openness with national security assurance.Under the current and future new circumstances,China’s FTZs face new challenges and tasks.In accordance with the directives of the 20th National Congress of the Communist Party of China,an enhancement strategy for the FTZs needs to be implemented.This involves the following:First,accurately understanding and responding to the changing situation to create strategic opportunities.Second,shifting paradigms to implement innovation-driven strategies,using the new development pattern concept to guide the reform experiments and construction of the FTZs.Third,granting more autonomy to FTZs for reforms,pursuing progress while maintaining stability,and solidly advancing the reform experiments in the FTZs.Fourth,orderly expanding the opening up of the service sector and cautiously advancing the internationalization of the renminbi.Fifth,promoting innovative development in trade to build a strong trade nation.Sixth,establishing synergy with bilateral FTZs,Belt and Road cooperation,and national diplomatic strategies to enhance the linkage effect.

Effects of QD2R Numeracy Enhancement Strategy on the Mathematics Performance of Grade 10 Students

Learning mathematics requires an effective and strategic teaching approach.This study aimed to assess the mathematics performance of the learners with the implementation of the numeracy enhancement strategy QD2R(Questions,Drills,Repetition,and Recitation)and to propose a strategy implementation plan to elevate their performances.This study employed the use of a quasi-experimental research design,purposive sampling with 70 Grade 10 students of Lian National High School who were distributed equally to control and treatment groups.The pre-test and post-test results were statistically analyzed using independent and paired sample t-tests,and a survey questionnaire was examined by getting the mean and standard deviation.The results indicated that better performance was achieved by the students from the treatment group compared to the students from the control group,as revealed by the Mean Percentage Score(MPS)results,mean scores,and P values of their pre-test and post-test scores.The learners’perception of the implementation of this strategy was to a great extent,wherein it was perceived to be more helpful in concepts related to understanding the lesson compared to concepts related to developing their attitude and skills.Moreover,the proposed implementation plan of numeracy enhancement strategy QD2R had three expected outcomes:elevated understanding and performance in mathematics lessons;modified strategy to focus on the development of attitude and skills towards mathematics;and refined and well-implemented QD2R strategy in teaching mathematics.Relative to these expected outcomes,appropriate measures,timeframe,and resources of each were comprehensively formulated.

基于ADE优化的IPMSM全速域无传感器控制

为了实现内置式永磁同步电机(IPMSM)全速域的无传感器控制和切换速域的平滑过渡,提出了一种基于自适应差分进化(ADE)算法优化的复合控制方法。分别在零低速域、中高速域采用旋转高频电压注入法和滑模观测器法来对电机转速和转子位置进行估算,并在切换速域采用基于ADE算法的权重系数优化法来实现上述两种控制方法的平滑切换,从而实现IPMSM全速域无传感器控制。仿真结果表明:提出的复合控制方法能够实现电机全速域的无感控制和切换速域的平滑过渡,且具有良好的稳定性。

A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

Finesse measurement for high-power optical enhancement cavity

Finesse is a critical parameter for describing the characteristics of an optical enhancement cavity(OEC). This paper first presents a review of finesse measurement techniques, including a comparative analysis of the advantages, disadvantages, and potential limitations of several main methods from both theoretical and practical perspectives. A variant of the existing method called the free spectral range(FSR) modulation method is proposed and compared with three other finesse measurement methods, i.e., the fast-switching cavity ring-down(CRD) method, the rapidly swept-frequency(SF) CRD method, and the ringing effect method. A high-power OEC platform with a high finesse of approximately 16000 is built and measured with the four methods. The performance of these methods is compared, and the results show that the FSR modulation method and the fast-switching CRD method are more suitable and accurate than the other two methods for high-finesse OEC measurements. The CRD method and the ringing effect method can be implemented in open loop using simple equipment and are easy to perform. Additionally, recommendations for selecting finesse measurement methods under different conditions are proposed, which benefit the development of OEC and its applications.

Image enhancement with intensity transformation on embedding space

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images.In order to address this limitation,a simple yet effective approach for image enhancement is proposed.The proposed algorithm based on the channel-wise intensity transformation is designed.However,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours.To this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding space.Then,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced images.Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space metrics.Specifically,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch similarity.Also,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

Is recovery enhancement after gastric cancer surgery really a safe approach for elderly patients?

BACKGROUND This study aimed to evaluate the safety of enhanced recovery after surgery(ERAS)in elderly patients with gastric cancer(GC).AIM To evaluate the safety of ERAS in elderly patients with GC.METHODS The PubMed,EMBASE,and Cochrane Library databases were used to search for eligible studies from inception to April 1,2023.The mean difference(MD),odds ratio(OR)and 95%confidence interval(95%CI)were pooled for analysis.The quality of the included studies was evaluated using the Newcastle-Ottawa Scale scores.We used Stata(V.16.0)software for data analysis.RESULTS This study consists of six studies involving 878 elderly patients.By analyzing the clinical outcomes,we found that the ERAS group had shorter postoperative hospital stays(MD=-0.51,I2=0.00%,95%CI=-0.72 to-0.30,P=0.00);earlier times to first flatus(defecation;MD=-0.30,I²=0.00%,95%CI=-0.55 to-0.06,P=0.02);less intestinal obstruction(OR=3.24,I2=0.00%,95%CI=1.07 to 9.78,P=0.04);less nausea and vomiting(OR=4.07,I2=0.00%,95%CI=1.29 to 12.84,P=0.02);and less gastric retention(OR=5.69,I2=2.46%,95%CI=2.00 to 16.20,P=0.00).Our results showed that the conventional group had a greater mortality rate than the ERAS group(OR=0.24,I2=0.00%,95%CI=0.07 to 0.84,P=0.03).However,there was no statistically significant difference in major complications between the ERAS group and the conventional group(OR=0.67,I2=0.00%,95%CI=0.38 to 1.18,P=0.16).CONCLUSION Compared to those with conventional recovery,elderly GC patients who received the ERAS protocol after surgery had a lower risk of mortality.

Hybrid Optimization Algorithm for Handwritten Document Enhancement

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Plasma density enhancement in radio-frequency hollow electrode discharge

The plasma density enhancement outside hollow electrodes in capacitively coupled radio-frequency(RF) discharges is investigated by a two-dimensional(2D) particle-in-cell/Monte-Carlo collision(PIC/MCC) model. Results show that plasma exists inside the cavity when the sheath inside the hollow electrode hole is fully collapsed, which is an essential condition for the plasma density enhancement outside hollow electrodes. In addition, the existence of the electron density peak at the orifice is generated via the hollow cathode effect(HCE), which plays an important role in the density enhancement. It is also found that the radial width of bulk plasma at the orifice affects the magnitude of the density enhancement, and narrow radial plasma bulk width at the orifice is not beneficial to obtain high-density plasma outside hollow electrodes.Higher electron density at the orifice, combined with larger radial plasma bulk width at the orifice,causes higher electron density outside hollow electrodes. The results also imply that the HCE strength inside the cavity cannot be determined by the magnitude of the electron density outside hollow electrodes.

Is it a normal phenomenon for pediatric patients to have brain leptomeningeal contrast enhancement on 3-tesla magnetic resonance imaging?

Determining whether sevoflurane sedation in children leads to“pseudo”prominent leptomeningeal contrast enhancement(pLMCE)on 3 Tesla magnetic resonance imaging will help reduce overdiagnosis by radiologists and clarify the pathophysiological changes of pLMCE.

Recess-free enhancement-mode AlGaN/GaN RF HEMTs on Si substrate

Enhancement-mode(E-mode)GaN-on-Si radio-frequency(RF)high-electron-mobility transistors(HEMTs)were fabri-cated on an ultrathin-barrier(UTB)AlGaN(<6 nm)/GaN heterostructure featuring a naturally depleted 2-D electron gas(2DEG)channel.The fabricated E-mode HEMTs exhibit a relatively high threshold voltage(VTH)of+1.1 V with good uniformity.A maxi-mum current/power gain cut-off frequency(fT/fMAX)of 31.3/99.6 GHz with a power added efficiency(PAE)of 52.47%and an out-put power density(Pout)of 1.0 W/mm at 3.5 GHz were achieved on the fabricated E-mode HEMTs with 1-μm gate and Au-free ohmic contact.

Topic highlight on texture and color enhancement imaging in gastrointestinal diseases

Olympus Corporation developed texture and color enhancement imaging(TXI)as a novel image-enhancing endoscopic technique.This topic highlights a series of hot-topic articles that investigated the efficacy of TXI for gastrointestinal disease identification in the clinical setting.A randomized controlled trial demonstrated improvements in the colorectal adenoma detection rate(ADR)and the mean number of adenomas per procedure(MAP)of TXI compared with those of white-light imaging(WLI)observation(58.7%vs 42.7%,adjusted relative risk 1.35,95%CI:1.17-1.56;1.36 vs 0.89,adjusted incident risk ratio 1.48,95%CI:1.22-1.80,respectively).A cross-over study also showed that the colorectal MAP and ADR in TXI were higher than those in WLI(1.5 vs 1.0,adjusted odds ratio 1.4,95%CI:1.2-1.6;58.2%vs 46.8%,1.5,1.0-2.3,respectively).A randomized controlled trial demonstrated non-inferiority of TXI to narrow-band imaging in the colorectal mean number of adenomas and sessile serrated lesions per procedure(0.29 vs 0.30,difference for non-inferiority-0.01,95%CI:-0.10 to 0.08).A cohort study found that scoring for ulcerative colitis severity using TXI could predict relapse of ulcerative colitis.A cross-sectional study found that TXI improved the gastric cancer detection rate compared to WLI(0.71%vs 0.29%).A cross-sectional study revealed that the sensitivity and accuracy for active Helicobacter pylori gastritis in TXI were higher than those of WLI(69.2%vs 52.5%and 85.3%vs 78.7%,res-pectively).In conclusion,TXI can improve gastrointestinal lesion detection and qualitative diagnosis.Therefore,further studies on the efficacy of TXI in clinical practice are required.

Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.

Identifying the enhancement mechanism of Al/MoO_(3) reactive multilayered films on the ignition ability of semiconductor bridge using a one-dimensional gas-solid two-phase flow model

Energetic Semiconductor bridge(ESCB)based on reactive multilayered films(RMFs)has a promising application in the miniature and intelligence of initiator and pyrotechnics device.Understanding the ignition enhancement mechanism of RMFs on semiconductor bridge(SCB)during the ignition process is crucial for the engineering and practical application of advanced initiator and pyrotechnics devices.In this study,a one-dimensional(1D)gas-solid two-phase flow ignition model was established to study the ignition process of ESCB to charge particles based on the reactivity of Al/MoO_(3) RMFs.In order to fully consider the coupled exothermic between the RMFs and the SCB plasma during the ignition process,the heat release of chemical reaction in RMFs was used as an internal heat source in this model.It is found that the exothermal reaction in RMFs improved the ignition performance of SCB.In the process of plasma rapid condensation with heat release,the product of RMFs enhanced the heat transfer process between the gas phase and the solid charge particle,which accelerated the expansion of hot plasma,and heated the solid charge particle as well as gas phase region with low temperature.In addition,it made up for pressure loss in the gas phase.During the plasma dissipation process,the exothermal chemical reaction in RMFs acted as the main heating source to heat the charge particle,making the surface temperature of the charge particle,gas pressure,and gas temperature rise continuously.This result may yield significant advantages in providing a universal ignition model for miniaturized ignition devices.

基于ADE-PML方法的快速计算目标电磁场分布研究

在电磁场分布计算领域,目标的电磁分布计算是一个重要且复杂的问题。目前,常用的计算方法有有限元法、有限差分法、时域积分方程法等。这些方法都有各自的优缺点,但是无论哪种方法都需要进行离散化、求解和后处理等,存在计算量大、计算时间长、计算精度低等问题。为了解决这些问题,一些新的计算方法被提出,如自适应离散化误差修正技术,自适应边界条件技术等。这些技术能够有效地提高计算精度和计算效率,但是在具体工程应用中,仍然存在一些问题,例如处理复杂目标的电磁场分布计算难度仍然较大、计算时间较长等。通过基于ADE-PML方法研究快速计算复杂目标的电磁场分布,进一步提高电磁场分布计算效率,有助于复杂目标电磁分布工程应用分析。

反复呼吸道感染儿童维生素ADE水平及其与免疫功能的相关性分析

目的:讨论反复呼吸道感染儿童维生素ADE水平及其与免疫功能的相关性。方法:选择2021年05月~2022年05月治疗的反复呼吸道感染儿童300例作为实验组,选择同期的健康儿童300例作为对照组。对两组儿童进行维生素A、维生素D、维生素E、免疫功能指标的检测,并分析结果。结果:两组的维生素A、维生素D水平,CD4 、CD4 /CD8 、lgA、lgG相比,组间差异显著(P<0.05)。实验组中缺乏维生素A的儿童高于对照组,差异显著(P<0.05)。维生素A与CD4 、CD4 /CD8 等免疫功能具有明显的相关性。结论:反复呼吸道感染儿童的发病与缺乏维生素A、维生素D具有重要的关系,患儿中缺乏维生素A与免疫功能降低具有相关关系。

基于改进Cascade RCNN的输电线路防振锤脱落检测方法

无人机巡检输电线路时,因拍摄角度和距离问题,容易出现被输电线遮挡和远距离拍摄的防振锤脱落目标,导致目标特征被遮挡且分辨率较低,且部分防振锤出现滑移现象,导致目标识别准确率降低。针对以上问题,提出一种基于改进Cascade RCNN的防振锤脱落检测网络。第一,设计了对比学习网络,将正负样本与真实样本的特征进行对比学习,利用对比损失函数训练网络,使其能更加关注到被遮挡的防振锤脱落目标,提升其特征提取能力;第二,进行了分类器增强操作,筛选出网络级联结构中回归效果较好的感兴趣区域并送入最后的分类回归队列中,提高了分类器的分类能力,进而提升检测目标的分类分数;第三,设计了并行注意力机制模块,整合网络提取的特征,增大关键特征的权重,使网络关注到图像中更关键的区域;在特征金字塔中,将双线性插值方法代替为反卷积,提升特征还原能力。经交叉验证实验结果表明,改进后的模型召回率、精确率和平均精度达到了97.5%,91.0%和92.0%,相比基线模型分别提高了6.9%,28.4%和8.0%。