A frequency servo SoC with output power stabilization loop technology for miniaturized atomic clocks

A frequency servo system-on-chip(FS-SoC)featuring output power stabilization technology is introduced in this study for high-precision and miniaturized cesium(Cs)atomic clocks.The proposed power stabilization loop(PSL)technique,incorporating an off-chip power detector(PD),ensures that the output power of the FS-SoC remains stable,mitigating the impact of power fluctuations on the atomic clock's stability.Additionally,a one-pulse-per-second(1PPS)is employed to syn-chronize the clock with GPS.Fabricated using 65 nm CMOS technology,the measured phase noise of the FS-SoC stands at-69.5 dBc/Hz@100 Hz offset and-83.9 dBc/Hz@1 kHz offset,accompanied by a power dissipation of 19.7 mW.The Cs atomic clock employing the proposed FS-SoC and PSL obtains an Allan deviation of 1.7×10^(-11) with 1-s averaging time.

Development and technology status of energy storage in depleted gas reservoirs

Utilizing energy storage in depleted oil and gas reservoirs can improve productivity while reducing power costs and is one of the best ways to achieve synergistic development of"Carbon Peak–Carbon Neutral"and"Underground Resource Utiliza-tion".Starting from the development of Compressed Air Energy Storage(CAES)technology,the site selection of CAES in depleted gas and oil reservoirs,the evolution mechanism of reservoir dynamic sealing,and the high-flow CAES and injection technology are summarized.It focuses on analyzing the characteristics,key equipment,reservoir construction,application scenarios and cost analysis of CAES projects,and sorting out the technical key points and existing difficulties.The devel-opment trend of CAES technology is proposed,and the future development path is scrutinized to provide reference for the research of CAES projects in depleted oil and gas reservoirs.

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization ability.Given this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature extraction.This module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating mechanism.Combined with dynamic convolution with scale information,the network can extract more differentiated feature information.The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information.Dynamic convolution and pixel-based gating mechanisms enhance the module’s adaptability.Finally,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module.The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

Pyramid Separable Channel Attention Network for Single Image Super-Resolution

Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.

Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-imagefree phase retrieval from single-shot hologram

Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

Development status and prospect of underground thermal energy storage technology

Underground Thermal Energy Storage(UTES)store unstable and non-continuous energy underground,releasing stable heat energy on demand.This effectively improve energy utilization and optimize energy allocation.As UTES technology advances,accommodating greater depth,higher temperature and multi-energy complementarity,new research challenges emerge.This paper comprehensively provides a systematic summary of the current research status of UTES.It categorized different types of UTES systems,analyzes the applicability of key technologies of UTES,and evaluate their economic and environmental benefits.Moreover,this paper identifies existing issues with UTES,such as injection blockage,wellbore scaling and corrosion,seepage and heat transfer in cracks,etc.It suggests deepening the research on blockage formation mechanism and plugging prevention technology,improving the study of anticorrosive materials and water treatment technology,and enhancing the investigation of reservoir fracture network characterization technology and seepage heat transfer.These recommendations serve as valuable references for promoting the high-quality development of UTES.

6G New Multiple Access Technology

As a basic technology at physical layer of mobile communications,non-orthogonal multiple access has been attracting wide attention across the academia and the industry.During the standardization of the fifth-generation(5G)of mobile communications,3GPP conducted preliminary study on non-orthogonal multiple access without reaching the consensus to standardize the technology.

Diagnosis and treatment of refractory infectious diseases using nanopore sequencing technology:Three case reports

BACKGROUND Infectious diseases are still one of the greatest threats to human health,and the etiology of 20%of cases of clinical fever is unknown;therefore,rapid identification of pathogens is highly important.Traditional culture methods are only able to detect a limited number of pathogens and are time-consuming;serologic detection has window periods,false-positive and false-negative problems;and nucleic acid molecular detection methods can detect several known pathogens only once.Three-generation nanopore sequencing technology provides new options for identifying pathogens.CASE SUMMARY Case 1:The patient was admitted to the hospital with abdominal pain for three days and cessation of defecation for five days,accompanied by cough and sputum.Nanopore sequencing of the drainage fluid revealed the presence of orallike bacteria,leading to a clinical diagnosis of bronchopleural fistula.Cefoperazone sodium sulbactam treatment was effective.Case 2:The patient was admitted to the hospital with fever and headache,and CT revealed lung inflammation.Antibiotic treatment for Streptococcus pneumoniae,identified through nanopore sequencing of cerebrospinal fluid,was effective.Case 3:The patient was admitted to our hospital with intermittent fever and an enlarged neck mass that had persisted for more than six months.Despite antibacterial treatment,her symptoms worsened.The nanopore sequencing results indicate that voriconazole treatment is effective for Aspergillus brookii.The patient was diagnosed with mixed cell type classical Hodgkin's lymphoma with infection.CONCLUSION Three-generation nanopore sequencing technology allows for rapid and accurate detection of pathogens in human infectious diseases.

Faster split-based feedback network for image super-resolution

Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning.This work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space.Specifically,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback mechanism.Then,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer parameters.Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.

The Philosophy of He(和):Examined from the Perspectives of Technology and Culture

Pre-modern Chinese crafts,such as iron smelting,cookery,medicine,and the production of vehicles,bows,and arrows indicate the traditional Chinese view of technology as being organic,holistic,and comprehensive.This view of technology is guided by the concept of he(和)and employs the means and methods of he,thus achieving the purport of he.In Chinese,the character he(和)possesses positive connotations.It originated from the meaning of"to season;to add flavoring to"(调和)and that of flavors being"perfectly harmonious"(和美).From this sensory experience,he gradually extended to the abstract levels of materiality,humanity,sociality,"order"(wei位),and "power,situation,force"(shi势).Finally,he was elevated to the supreme level of"qi of great harmony"(taihe zhi qi太和之气),which is comparable to the concept of dao(道).The philosophy of he has exerted wide impact on such areas as technology,art,national character,cultural psychology,and behavior patterns,and has become an integral part of China's inherent culture.The paradoxes and deviations of he hold their own profound philosophical implications that merit further exploration.As humanity confronts significant challenges,such as how we can coexist with others,with technology,and with nature,the ancient Eastern wisdom embodied in he continues to possess practical characteristics and value.

Enhancing emerging technology discovery in nanomedicine by integrating innovative sentences using BERT and NLDA

Purpose:Nanomedicine has significant potential to revolutionize biomedicine and healthcare through innovations in diagnostics,therapeutics,and regenerative medicine.This study aims to develop a novel framework that integrates advanced natural language processing,noise-free topic modeling,and multidimensional bibliometrics to systematically identify emerging nanomedicine technology topics from scientific literature.Design/methodology/approach:The framework involves collecting full-text articles from PubMed Central and nanomedicine-related metrics from the Web of Science for the period 2013-2023.A fine-tuned BERT model is employed to extract key informative sentences.Noiseless Latent Dirichlet Allocation(NLDA)is applied to model interpretable topics from the cleaned corpus.Additionally,we develop and apply metrics for novelty,innovation,growth,impact,and intensity to quantify the emergence of novel technological topics.Findings:By applying this methodology to nanomedical publications,we identify an increasing emphasis on research aligned with global health priorities,particularly inflammation and biomaterial interactions in disease research.This methodology provides deeper insights through full-text analysis and leading to a more robust discovery of emerging technologies.Research limitations:One limitation of this study is its reliance on the existing scientific literature,which may introduce publication biases and language constraints.Additionally,manual annotation of the dataset,while thorough,is subject to subjectivity and can be time-consuming.Future research could address these limitations by incorporating more diverse data sources,and automating the annotation process.Practical implications:The methodology presented can be adapted to explore emerging technologies in other scientific domains.It allows for tailored assessment criteria based on specific contexts and objectives,enabling more precise analysis and decision-making in various fields.Originality/value:This study offers a comprehensive framework for identifying emerging technologies in nanomedicine,combining theoretical insights and practical applications.Its potential for adaptation across scientific disciplines enhances its value for future research and decision-making in technology discovery.

Science and Technology Commissioners Supporting High-Quality Development Project for Hundreds of Counties, Thousands of Towns, and Myriads of Villages in the Context of Rural Revitalization

This paper discusses the important role of science and technology commissioners in the high-quality development of hundreds of counties,thousands of towns,and myriads of villages in the context of rural revitalization,including building bridges,accelerating the transformation of achievements,promoting the value-added of the whole agricultural industry chain,and promoting the rapid development of rural industrial economy.It also discusses the working achievements of science and technology commissioners,in order to promote further development of rural revitalization in Guangdong Province.

Application and prospects of proteomic technology in inflammation:a review

Proteomics is a new technology that has been widely applied in the field of life and health science.It effectively addresses issues related to the impact of dietary structure on organs,tissues,and cells,as well as the changes in proteins in various organs,tissues,and cells under disease conditions.The differential proteins identified through proteomics can serve as disease biomarkers and target proteins affecting health and can be used for disease diagnosis and health regulation.In this paper,the application of proteomics in the field of infl ammation in recent years was summarized,especially in the therapeutic target and mechanism of action,which opens up a new way for more effective prevention,diagnosis,and treatment of inflammation,and provides medical protection for human life and health.

The Impact of Health Information Technology on Hospital Performance: A Systematic Integrative Literature Review

Objective: To review, categorise, and synthesise findings from literature on health information technology (HIT) functionalities, HIT use, and the impact of HIT on hospital performance. Materials and Methods: We conducted a systematic integrative literature review based on a compre-hensive database search. To organise, categorise and synthesise the ex-isting literature, we adopted the affordance actualization theory. To align the literature with our research framework, we used four categories: 1) the functionalities of HIT and how these functionalities are measured;2) use and immediate outcomes of HIT functionalities;3) different perfor-mance indicators and how HIT functionalities affect them;and 4) what hospital characteristics influence the outcome of hospital performance. Results: Fifty-two studies were included. We identified four types of HIT. Only ten studies (19.2%) define the use of HIT by explicitly meas-uring the use rate of HIT. We identified five dimensions of hospital per-formance indicators. Every dimension showed mixed results;however, in general, HIT has a positive impact on mortality and patient readmis-sions. We found several hospital characteristics that may affect the rela-tionship between HIT and hospital-level outcomes. Discussion: Further efforts should focus on embedded research on HIT functionalities, use and effects of HIT implementations with more performance indicators and adjusted for hospital characteristics. Conclusion: The proposed framework could help hospitals and researchers make decisions regard-ing the functionalities, use and effects of HIT implementation in hospi-tals. Given our research outcomes, we suggest future research opportuni-ties to improve understanding of how HIT affects hospital performance. .

The Value of Modern Information Technology in Nursing Emergencies

In first aid, traditional information interchange has numerous shortcomings. For example, delayed information and disorganized departmental communication cause patients to miss out on critical rescue time. Information technology is becoming more and more mature, and as a result, its use across numerous industries is now standard. China is still in the early stages of developing its integration of emergency medical services with modern information technology;despite our progress, there are still numerous obstacles and constraints to overcome. Our goal is to integrate information technology into every aspect of emergency patient care, offering robust assistance for both patient rescue and the efforts of medical personnel. Information may be communicated in a fast, multiple, and effective manner by utilizing modern information technology. This study aims to examine the current state of this field’s development, current issues, and the field’s future course of development.

A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.

A tensile wearable SHF antenna with efficient communication in defense beacon technology

The study projects a flexible and compact wearable pear-shaped Super High Frequency(SHF)antenna that can provide detailed location recognition and tracking applicable to defense beacon technology.This mini aperture with electrical dimensions of 0.12λ_(0)×0.22λ_(0)×0.01λ_(0)attains a vast bandwidth over 3.1-34.5 GHz Super High Frequency(SHF)frequency band at S_(11)≤-10 dB,peak gain of 7.14 dBi and proportionately homogeneous radiation pattern.The fractional bandwidth(%BW)acquired is 168%that envelopes diversified frequency spectrum inclusive of X band specifically targeted to all kinds of defense and military operations.The proposed antenna can be worn on a soldier's uniform and hence the Specific Absorption Rate simulation is accomplished.The Peak SAR Value over 1 g of tissue is 1.48 W/kg and for 10 g of tissue is 0.27 W/kg well under the safety standards.The flexibility is proven by analyzing the full electromagnetic simulations for various bending conditions.Time response analysis is attained with its Fidelity Factor and Group Delay.Communication excellence is determined using Link Budget Analysis and it is seen that margin at 100 Mbps is 62 m and at 200 Mbps is 59 m.Prototype is fabricated along with experimental validation.All the results show harmony in shaping the antenna to provide critical situational awareness and data sharing capabilities required in defense beacon technology for location identification.