We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical feedback.Due to the inevitable noise durin...We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical feedback.Due to the inevitable noise during the photoelectric detection and analog-digital conversion,the varying of output optical power would change the signal to noise ratio,then impact time delay signature identification and the random bit generation.Our results show that,when the optical power is less than-14 dBm,with the decreasing of the optical power,the actual identified time delay signature degrades and the entropy of the chaotic signal increases.Moreover,the extracted random bit sequence with lower optical power is more easily pass through the randomness testing.展开更多
The Internet of Things(IoT)is a network system that connects physical devices through the Internet,allowing them to interact.Nowadays,IoT has become an integral part of our lives,offering convenience and smart functio...The Internet of Things(IoT)is a network system that connects physical devices through the Internet,allowing them to interact.Nowadays,IoT has become an integral part of our lives,offering convenience and smart functionality.However,the growing number of IoT devices has brought about a corresponding increase in cybersecurity threats,such as device vulnerabilities,data privacy concerns,and network susceptibilities.Integrating blockchain technology with IoT has proven to be a promising approach to enhance IoT security.Nevertheless,the emergence of quantum computing poses a significant challenge to the security of traditional classical cryptography used in blockchain,potentially exposing it to quantum cyber-attacks.To support the growth of the IoT industry,mitigate quantum threats,and safeguard IoT data,this study proposes a robust blockchain solution for IoT that incorporates both classical and post-quantum security measures.Firstly,we present the Quantum-Enhanced Blockchain Architecture for IoT(QBIoT)to ensure secure data sharing and integrity protection.Secondly,we propose an improved Proof of Authority consensus algorithm called“Proof of Authority with Random Election”(PoARE),implemented within QBIoT for leader selection and new block creation.Thirdly,we develop a publickey quantum signature protocol for transaction verification in the blockchain.Finally,a comprehensive security analysis of QBIoT demonstrates its resilience against cyber threats from both classical and quantum adversaries.In summary,this research introduces an innovative quantum-enhanced blockchain solution to address quantum security concernswithin the realmof IoT.The proposedQBIoT framework contributes to the ongoing development of quantum blockchain technology and offers valuable insights for future research on IoT security.展开更多
The discovery of high-temperature superconductivity near 80K in bilayer nickelate La_(3)Ni_(2)O_(7)under high pressures has renewed the exploration of superconducting nickelate in bulk materials.The extension of super...The discovery of high-temperature superconductivity near 80K in bilayer nickelate La_(3)Ni_(2)O_(7)under high pressures has renewed the exploration of superconducting nickelate in bulk materials.The extension of superconductivity in other nickelates in a broader family is also essential.Here,we report the experimental observation of superconducting signature in trilayer nickelate La_(4)Ni_(3)O_(10)under high pressures.By using a modified solgel method and post-annealing treatment under high oxygen pressure,we successfully obtained polycrystalline La_(4)Ni_(3)O_(10)samples with different transport behaviors at ambient pressure.Then we performed high-pressure electrical resistance measurements on these samples in a diamond-anvil-cell apparatus.Surprisingly,the signature of possible superconducting transition with a maximum transition temperature(T_(c))of about 20K under high pressures is observed,as evidenced by a clear drop of resistance and the suppression of resistance drops under magnetic fields.Although the resistance drop is sample-dependent and relatively small,it appears in all of our measured samples.We argue that the observed superconducting signal is most likely to originate from the main phase of La_(4)Ni_(3)O_(10).Our findings will motivate the exploration of superconductivity in a broader family of nickelates and shed light on the understanding of the underlying mechanisms of high-T_(c) superconductivity in nickelates.展开更多
Purpose:This study aims to evaluate the accuracy of authorship attributions in scientific publications,focusing on the fairness and precision of individual contributions within academic works.Design/methodology/approa...Purpose:This study aims to evaluate the accuracy of authorship attributions in scientific publications,focusing on the fairness and precision of individual contributions within academic works.Design/methodology/approach:The study analyzes 81,823 publications from the journal PLOS ONE,covering the period from January 2018 to June 2023.It examines the authorship attributions within these publications to try and determine the prevalence of inappropriate authorship.It also investigates the demographic and professional profiles of affected authors,exploring trends and potential factors contributing to inaccuracies in authorship.Findings:Surprisingly,9.14%of articles feature at least one author with inappropriate authorship,affecting over 14,000 individuals(2.56%of the sample).Inappropriate authorship is more concentrated in Asia,Africa,and specific European countries like Italy.Established researchers with significant publication records and those affiliated with companies or nonprofits show higher instances of potential monetary authorship.Research limitations:Our findings are based on contributions as declared by the authors,which implies a degree of trust in their transparency.However,this reliance on self-reporting may introduce biases or inaccuracies into the dataset.Further research could employ additional verification methods to enhance the reliability of the findings.Practical implications:These findings have significant implications for journal publishers,Beyond authorship:Analyzing contributions in PLOS ONE and Maddi,A.,&the challenges of appropriate attribution highlighting the necessity for robust control mechanisms to ensure the integrity of authorship attributions.Moreover,researchers must exercise discernment in determining when to acknowledge a contributor and when to include them in the author list.Addressing these issues is crucial for maintaining the credibility and fairness of academic publications.Originality/value:This study contributes to an understanding of critical issues within academic authorship,shedding light on the prevalence and impact of inappropriate authorship attributions.By calling for a nuanced approach to ensure accurate credit is given where it is due,the study underscores the importance of upholding ethical standards in scholarly publishing.展开更多
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
Major depressive disorder(MDD)with suicidal ideation or behaviour(MDSI)is associated with an increased risk of future suicide.The timely identification of suicide risk in patients with MDD and the subsequent implement...Major depressive disorder(MDD)with suicidal ideation or behaviour(MDSI)is associated with an increased risk of future suicide.The timely identification of suicide risk in patients with MDD and the subsequent implementation of interventions are crucially important to reduce their suffering and save lives.However,the early diagnosis of MDSI remains challenging across the world,as no objective diagnostic method is currently available.In China,the challenge is greater due to the social stigma associated with mental health problems,leading many patients to avoid reporting their suicidal ideation.Additionally,the neural mechanisms underlying MDSl are stll unclear,which may hamper the development of effective interventions.We thus conducted this narrative review to summarise the existing neuroimaging studies of MDSI in Chinese patients,including those involving structural magnetic resonance imaging(MRI),functional MRl,neuronal electrophysiological source imaging of the brain dynamics with electroencephalography and magnetoencephalography.By synthesising the current research efforts in neuroimaging studies of Chinese patients with MDSl,we identified potential objective neuroimaging biomarkers,which may aid in the early identification of patients with MDSI who are at high suicide-related risk.Our findings also offer insights into the complex neural mechanisms underlying MDSI and suggest promising therapeutic targets.Furthermore,we propose future directions to discover novel imaging signatures,improve patient care,as well as help psychiatrists and clinical investigators plan their future research.展开更多
A metropolitan city such as Los Angeles (LA) is an ideal study site with a very high population density, and it houses at least 3 treatment plants where sewage is treated preliminarily and then progressing to tertiary...A metropolitan city such as Los Angeles (LA) is an ideal study site with a very high population density, and it houses at least 3 treatment plants where sewage is treated preliminarily and then progressing to tertiary treatment before discharging into the LA River. We will gain a better understanding of the water quality in the LA River and the nitrate load in the watershed system by examining the influence of waste water treatment plants (WWTPs). The goal of this study is to pinpoint the exact source of nitrate in the LA River using the isotope signatures. We have selected sampling locations both upstream and downstream of the WWTP. This serves to monitor nitrate levels, aiding in the assessment of treatment plant effectiveness, pinpointing nitrate pollution sources, and ensuring compliance with environmental regulations. The research explores the isotopic composition of NO3 in relation to atmospheric nitrogen and Vienna Standard Mean Ocean Water, shedding light on the contributions from various sources such as manure, sewage, soil organic nitrogen, and nitrogen fertilizers. Specifically, there is a change in the δ15NAir value between the dry and wet seasons. The isotope values in the Tillman WWTP sample changed between dry and wet seasons. Notably, the presence of nitrate originating from manure and sewage is consistent across seasons, emphasizing the significant impact of anthropogenic and agricultural activities on water quality. This investigation contributes to the broader understanding of nitrogen cycling in urban water bodies, particularly in the context of wastewater effluent discharge. The findings hold implications for water quality management and highlight the need for targeted interventions to mitigate the impact of nitrogen-containing compounds on aquatic ecosystems. Overall, the study provides a valuable framework for future research and environmental stewardship efforts aimed at preserving the health and sustainability of urban water resources. This data informs decisions regarding additional treatment or mitigation actions to safeguard downstream water quality and ecosystem health.展开更多
Introduction Types ofpaper Contributions falling into the following categories will be considered for publication:Reviews,Technical papers,Theoretical papers,and Editorial.Please ensure that you select the appropriate...Introduction Types ofpaper Contributions falling into the following categories will be considered for publication:Reviews,Technical papers,Theoretical papers,and Editorial.Please ensure that you select the appropriate article type from the list of options when making your submission.Authors contributing to special issues should ensure that they select the special issue article type from this list.展开更多
Chinese Phaysics Letters(CPL)is a peer-reviewed,inter-national and multidisciplinary journal sponsored by the Chi-nese Phaysical Society(CPS)and Institute of Physics,CAS,and hosted online by IOP Publishing Ltd.Launche...Chinese Phaysics Letters(CPL)is a peer-reviewed,inter-national and multidisciplinary journal sponsored by the Chi-nese Phaysical Society(CPS)and Institute of Physics,CAS,and hosted online by IOP Publishing Ltd.Launched in 1984 asthe flagship journal of CPS,CPL has become one of the mostprestigious periodicals published in China,and been among thegood choices for worldwide physicists to disseminate their mostimportant breakthroughs.展开更多
Chinese Physics Letters(CPL)is a peer-reviewed,international and multidisciplinary journal sponsored by the Chinese Physical Society(CPS)and Institute of Physics,CAS,and hosted online by IOP Publishing Ltd.Launched in...Chinese Physics Letters(CPL)is a peer-reviewed,international and multidisciplinary journal sponsored by the Chinese Physical Society(CPS)and Institute of Physics,CAS,and hosted online by IOP Publishing Ltd.Launched in 1984 as the flagship journal of CPS,CPL has become one of the most prestigious periodicals published in China.展开更多
Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key mot...Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.展开更多
Location awareness in wireless networks is essential for emergency services,navigation,gaming,and many other applications.This article presents a method for source localization based on measuring the amplitude-phase d...Location awareness in wireless networks is essential for emergency services,navigation,gaming,and many other applications.This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station.The existing scatterers in the target area create unique scattered field interference at each source location.The unique field interference at each source location results in a unique field signature at the base station which is used for source localization.In the proposed method,the target area is divided into a grid with a step of less than half the wavelength.Each grid node is characterized by its field signature at the base station.Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization.The normalization of the field signatures avoids the need for time synchronization between the base station and the source.When a source transmits signals,the generated field signature at the base station is normalized and then correlated with the stored fingerprints.The maximum correlation value is given by the node to which the source is the closest.Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization.The proposed method is potentially applicable for indoor localization and navigation of mobile robots.展开更多
General Journal of Beijing Institute of Technology(JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Informat...General Journal of Beijing Institute of Technology(JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.展开更多
In some schemes, quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. By considering the communication overhead, quantum operation complexity, verification efficiency and ot...In some schemes, quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. By considering the communication overhead, quantum operation complexity, verification efficiency and other relevant factors in practical situations, this article proposes a non-entangled quantum blind signature scheme based on dense encoding. The information owner utilizes dense encoding and hash functions to blind the information while reducing the use of quantum resources. After receiving particles, the signer encrypts the message using a one-way function and performs a Hadamard gate operation on the selected single photon to generate the signature. Then the verifier performs a Hadamard gate inverse operation on the signature and combines it with the encoding rules to restore the message and complete the verification.Compared with some typical quantum blind signature protocols, this protocol has strong blindness in privacy protection,and higher flexibility in scalability and application. The signer can adjust the signature operation according to the actual situation, which greatly simplifies the complexity of the signature. By simultaneously utilizing the secondary distribution and rearrangement of non-entangled quantum states, a non-entangled quantum state representation of three bits of classical information is achieved, reducing the use of a large amount of quantum resources and lowering implementation costs. This improves both signature verification efficiency and communication efficiency while, at the same time, this scheme meets the requirements of unforgeability, non-repudiation, and prevention of information leakage.展开更多
General Journal of Bejing Institute of Technology(JBIT)is a periodical publication on science andtechnology published by Beijing Institute of Technology under the sponsorship of the Ministry ofIndustry and Information...General Journal of Bejing Institute of Technology(JBIT)is a periodical publication on science andtechnology published by Beijing Institute of Technology under the sponsorship of the Ministry ofIndustry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.展开更多
The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more ...The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy leakage.As a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy protection.However,introducing signature technology leads to additional signature verification overhead.In the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature verification.Therefore,it is necessary to design an efficient multi-signature verification scheme while ensuring security.In this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature verification.In our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data analysis.Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.展开更多
General Journal of Beijing Institute of Technology(JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Informat...General Journal of Beijing Institute of Technology(JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.展开更多
General Journal of Bejing Institute of Technology(JBIT)is a periodical publication on science andtechnology published by Beijing Institute of Technology under the sponsorship of the Ministry ofIndustry and Information...General Journal of Bejing Institute of Technology(JBIT)is a periodical publication on science andtechnology published by Beijing Institute of Technology under the sponsorship of the Ministry ofIndustry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.展开更多
Plant protein beverage adulteration occurs frequently,which may cause health problems for consumers due to the hidden allergens.Hence,a novel method was developed for authentication by ultra-performance liquid chromat...Plant protein beverage adulteration occurs frequently,which may cause health problems for consumers due to the hidden allergens.Hence,a novel method was developed for authentication by ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS).Almond,peanut,walnut and soybean were hydrolyzed,followed by separation by NanoLC-Triple TOF MS.The obtained fingerprints were identified by ProteinPilotTM combined with Uniprot,and 16 signature peptides were selected.Afterwards,plant protein beverages treated by trypsin hydrolysis were analyzed with UPLC-MS/MS.This method showed a good linear relationship with R2>0.99403.The limit of quantification(LOQ)were 0.015,0.01,0.5 and 0.05 g/L for almond,peanut,walnut and soybean,respectively.Mean recoveries ranged from 84.77%to 110.44%with RSDs<15%.The developed method was successfully applied to the adulteration detection of 31 plant protein beverages to reveal adulteration and false labeling.Conclusively,this method could provide technical support for authentication of plant protein beverages to protect the rights and health of consumers.展开更多
BACKGROUND Gastric cancer(GC)is a highly aggressive malignancy with a heterogeneous nature,which makes prognosis prediction and treatment determination difficult.Inflammation is now recognized as one of the hallmarks ...BACKGROUND Gastric cancer(GC)is a highly aggressive malignancy with a heterogeneous nature,which makes prognosis prediction and treatment determination difficult.Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours.Inflammation also affects the prognosis of GC patients.Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis.However,the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear.AIM To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients.was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database.GC patients from the GSE26253 cohort were used for validation.Univariate and multivariate Cox analyses were used to determine the independent prognostic factors,and a prognostic nomogram was established.The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram.The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram.Gene set enrichment analysis was performed to explore the potential regulatory pathways involved.The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT.Finally,we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents.RESULTS A prognostic model consisting of three inflammatory-related genes(MRPS17,GUF1,and PDK4)was constructed.Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients.According to the risk score,GC patients were stratified into high-and low-risk groups,and patients in the high-risk group had significantly worse prognoses according to age,sex,TNM stage and Lauren type.Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging.Finally,the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group,indicating a link between inflammation-related genes and drug sensitivity.CONCLUSION In conclusion,we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.展开更多
基金Project supported in part by the National Natural Science Foundation of China(Grant Nos.62005129 and 62175116)。
文摘We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical feedback.Due to the inevitable noise during the photoelectric detection and analog-digital conversion,the varying of output optical power would change the signal to noise ratio,then impact time delay signature identification and the random bit generation.Our results show that,when the optical power is less than-14 dBm,with the decreasing of the optical power,the actual identified time delay signature degrades and the entropy of the chaotic signal increases.Moreover,the extracted random bit sequence with lower optical power is more easily pass through the randomness testing.
基金supported by National Key RD Program of China(Grant No.2022YFB3104402,the Research on Digital Identity Trust System for Massive Heterogeneous Terminals in Road Traffic System)the Fundamental Research Funds for the Central Universities(Grant Nos.3282023015,3282023035,3282023051)National First-Class Discipline Construction Project of Beijing Electronic Science and Technology Institute(No.3201012).
文摘The Internet of Things(IoT)is a network system that connects physical devices through the Internet,allowing them to interact.Nowadays,IoT has become an integral part of our lives,offering convenience and smart functionality.However,the growing number of IoT devices has brought about a corresponding increase in cybersecurity threats,such as device vulnerabilities,data privacy concerns,and network susceptibilities.Integrating blockchain technology with IoT has proven to be a promising approach to enhance IoT security.Nevertheless,the emergence of quantum computing poses a significant challenge to the security of traditional classical cryptography used in blockchain,potentially exposing it to quantum cyber-attacks.To support the growth of the IoT industry,mitigate quantum threats,and safeguard IoT data,this study proposes a robust blockchain solution for IoT that incorporates both classical and post-quantum security measures.Firstly,we present the Quantum-Enhanced Blockchain Architecture for IoT(QBIoT)to ensure secure data sharing and integrity protection.Secondly,we propose an improved Proof of Authority consensus algorithm called“Proof of Authority with Random Election”(PoARE),implemented within QBIoT for leader selection and new block creation.Thirdly,we develop a publickey quantum signature protocol for transaction verification in the blockchain.Finally,a comprehensive security analysis of QBIoT demonstrates its resilience against cyber threats from both classical and quantum adversaries.In summary,this research introduces an innovative quantum-enhanced blockchain solution to address quantum security concernswithin the realmof IoT.The proposedQBIoT framework contributes to the ongoing development of quantum blockchain technology and offers valuable insights for future research on IoT security.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1403201)the National Natural Science Foundation of China(Grant Nos.12204231,12061131001,52072170,and 11927809)the Strategic Priority Research Program(B)of Chinese Academy of Sciences(Grant No.XDB25000000).
文摘The discovery of high-temperature superconductivity near 80K in bilayer nickelate La_(3)Ni_(2)O_(7)under high pressures has renewed the exploration of superconducting nickelate in bulk materials.The extension of superconductivity in other nickelates in a broader family is also essential.Here,we report the experimental observation of superconducting signature in trilayer nickelate La_(4)Ni_(3)O_(10)under high pressures.By using a modified solgel method and post-annealing treatment under high oxygen pressure,we successfully obtained polycrystalline La_(4)Ni_(3)O_(10)samples with different transport behaviors at ambient pressure.Then we performed high-pressure electrical resistance measurements on these samples in a diamond-anvil-cell apparatus.Surprisingly,the signature of possible superconducting transition with a maximum transition temperature(T_(c))of about 20K under high pressures is observed,as evidenced by a clear drop of resistance and the suppression of resistance drops under magnetic fields.Although the resistance drop is sample-dependent and relatively small,it appears in all of our measured samples.We argue that the observed superconducting signal is most likely to originate from the main phase of La_(4)Ni_(3)O_(10).Our findings will motivate the exploration of superconductivity in a broader family of nickelates and shed light on the understanding of the underlying mechanisms of high-T_(c) superconductivity in nickelates.
文摘Purpose:This study aims to evaluate the accuracy of authorship attributions in scientific publications,focusing on the fairness and precision of individual contributions within academic works.Design/methodology/approach:The study analyzes 81,823 publications from the journal PLOS ONE,covering the period from January 2018 to June 2023.It examines the authorship attributions within these publications to try and determine the prevalence of inappropriate authorship.It also investigates the demographic and professional profiles of affected authors,exploring trends and potential factors contributing to inaccuracies in authorship.Findings:Surprisingly,9.14%of articles feature at least one author with inappropriate authorship,affecting over 14,000 individuals(2.56%of the sample).Inappropriate authorship is more concentrated in Asia,Africa,and specific European countries like Italy.Established researchers with significant publication records and those affiliated with companies or nonprofits show higher instances of potential monetary authorship.Research limitations:Our findings are based on contributions as declared by the authors,which implies a degree of trust in their transparency.However,this reliance on self-reporting may introduce biases or inaccuracies into the dataset.Further research could employ additional verification methods to enhance the reliability of the findings.Practical implications:These findings have significant implications for journal publishers,Beyond authorship:Analyzing contributions in PLOS ONE and Maddi,A.,&the challenges of appropriate attribution highlighting the necessity for robust control mechanisms to ensure the integrity of authorship attributions.Moreover,researchers must exercise discernment in determining when to acknowledge a contributor and when to include them in the author list.Addressing these issues is crucial for maintaining the credibility and fairness of academic publications.Originality/value:This study contributes to an understanding of critical issues within academic authorship,shedding light on the prevalence and impact of inappropriate authorship attributions.By calling for a nuanced approach to ensure accurate credit is given where it is due,the study underscores the importance of upholding ethical standards in scholarly publishing.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
文摘Major depressive disorder(MDD)with suicidal ideation or behaviour(MDSI)is associated with an increased risk of future suicide.The timely identification of suicide risk in patients with MDD and the subsequent implementation of interventions are crucially important to reduce their suffering and save lives.However,the early diagnosis of MDSI remains challenging across the world,as no objective diagnostic method is currently available.In China,the challenge is greater due to the social stigma associated with mental health problems,leading many patients to avoid reporting their suicidal ideation.Additionally,the neural mechanisms underlying MDSl are stll unclear,which may hamper the development of effective interventions.We thus conducted this narrative review to summarise the existing neuroimaging studies of MDSI in Chinese patients,including those involving structural magnetic resonance imaging(MRI),functional MRl,neuronal electrophysiological source imaging of the brain dynamics with electroencephalography and magnetoencephalography.By synthesising the current research efforts in neuroimaging studies of Chinese patients with MDSl,we identified potential objective neuroimaging biomarkers,which may aid in the early identification of patients with MDSI who are at high suicide-related risk.Our findings also offer insights into the complex neural mechanisms underlying MDSI and suggest promising therapeutic targets.Furthermore,we propose future directions to discover novel imaging signatures,improve patient care,as well as help psychiatrists and clinical investigators plan their future research.
文摘A metropolitan city such as Los Angeles (LA) is an ideal study site with a very high population density, and it houses at least 3 treatment plants where sewage is treated preliminarily and then progressing to tertiary treatment before discharging into the LA River. We will gain a better understanding of the water quality in the LA River and the nitrate load in the watershed system by examining the influence of waste water treatment plants (WWTPs). The goal of this study is to pinpoint the exact source of nitrate in the LA River using the isotope signatures. We have selected sampling locations both upstream and downstream of the WWTP. This serves to monitor nitrate levels, aiding in the assessment of treatment plant effectiveness, pinpointing nitrate pollution sources, and ensuring compliance with environmental regulations. The research explores the isotopic composition of NO3 in relation to atmospheric nitrogen and Vienna Standard Mean Ocean Water, shedding light on the contributions from various sources such as manure, sewage, soil organic nitrogen, and nitrogen fertilizers. Specifically, there is a change in the δ15NAir value between the dry and wet seasons. The isotope values in the Tillman WWTP sample changed between dry and wet seasons. Notably, the presence of nitrate originating from manure and sewage is consistent across seasons, emphasizing the significant impact of anthropogenic and agricultural activities on water quality. This investigation contributes to the broader understanding of nitrogen cycling in urban water bodies, particularly in the context of wastewater effluent discharge. The findings hold implications for water quality management and highlight the need for targeted interventions to mitigate the impact of nitrogen-containing compounds on aquatic ecosystems. Overall, the study provides a valuable framework for future research and environmental stewardship efforts aimed at preserving the health and sustainability of urban water resources. This data informs decisions regarding additional treatment or mitigation actions to safeguard downstream water quality and ecosystem health.
文摘Introduction Types ofpaper Contributions falling into the following categories will be considered for publication:Reviews,Technical papers,Theoretical papers,and Editorial.Please ensure that you select the appropriate article type from the list of options when making your submission.Authors contributing to special issues should ensure that they select the special issue article type from this list.
文摘Chinese Phaysics Letters(CPL)is a peer-reviewed,inter-national and multidisciplinary journal sponsored by the Chi-nese Phaysical Society(CPS)and Institute of Physics,CAS,and hosted online by IOP Publishing Ltd.Launched in 1984 asthe flagship journal of CPS,CPL has become one of the mostprestigious periodicals published in China,and been among thegood choices for worldwide physicists to disseminate their mostimportant breakthroughs.
文摘Chinese Physics Letters(CPL)is a peer-reviewed,international and multidisciplinary journal sponsored by the Chinese Physical Society(CPS)and Institute of Physics,CAS,and hosted online by IOP Publishing Ltd.Launched in 1984 as the flagship journal of CPS,CPL has become one of the most prestigious periodicals published in China.
文摘Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.
基金supported by the Tomsk State University Competitiveness Improvement Program under Grant No.2.4.2.23 IG.
文摘Location awareness in wireless networks is essential for emergency services,navigation,gaming,and many other applications.This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station.The existing scatterers in the target area create unique scattered field interference at each source location.The unique field interference at each source location results in a unique field signature at the base station which is used for source localization.In the proposed method,the target area is divided into a grid with a step of less than half the wavelength.Each grid node is characterized by its field signature at the base station.Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization.The normalization of the field signatures avoids the need for time synchronization between the base station and the source.When a source transmits signals,the generated field signature at the base station is normalized and then correlated with the stored fingerprints.The maximum correlation value is given by the node to which the source is the closest.Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization.The proposed method is potentially applicable for indoor localization and navigation of mobile robots.
文摘General Journal of Beijing Institute of Technology(JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.
基金Project supported by the National Natural Science Foundation of China (Grant No. 61762039)。
文摘In some schemes, quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. By considering the communication overhead, quantum operation complexity, verification efficiency and other relevant factors in practical situations, this article proposes a non-entangled quantum blind signature scheme based on dense encoding. The information owner utilizes dense encoding and hash functions to blind the information while reducing the use of quantum resources. After receiving particles, the signer encrypts the message using a one-way function and performs a Hadamard gate operation on the selected single photon to generate the signature. Then the verifier performs a Hadamard gate inverse operation on the signature and combines it with the encoding rules to restore the message and complete the verification.Compared with some typical quantum blind signature protocols, this protocol has strong blindness in privacy protection,and higher flexibility in scalability and application. The signer can adjust the signature operation according to the actual situation, which greatly simplifies the complexity of the signature. By simultaneously utilizing the secondary distribution and rearrangement of non-entangled quantum states, a non-entangled quantum state representation of three bits of classical information is achieved, reducing the use of a large amount of quantum resources and lowering implementation costs. This improves both signature verification efficiency and communication efficiency while, at the same time, this scheme meets the requirements of unforgeability, non-repudiation, and prevention of information leakage.
文摘General Journal of Bejing Institute of Technology(JBIT)is a periodical publication on science andtechnology published by Beijing Institute of Technology under the sponsorship of the Ministry ofIndustry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.
基金supported by National Natural Science Foundation of China under Grant No.61972360Shandong Provincial Natural Science Foundation of China under Grant Nos.ZR2020MF148,ZR2020QF108.
文摘The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy leakage.As a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy protection.However,introducing signature technology leads to additional signature verification overhead.In the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature verification.Therefore,it is necessary to design an efficient multi-signature verification scheme while ensuring security.In this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature verification.In our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data analysis.Simulation results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.
文摘General Journal of Beijing Institute of Technology(JBIT)is a periodical publication on science and technology published by Beijing Institute of Technology under the sponsorship of the Ministry of Industry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.
文摘General Journal of Bejing Institute of Technology(JBIT)is a periodical publication on science andtechnology published by Beijing Institute of Technology under the sponsorship of the Ministry ofIndustry and Information Technology of the People's Republic of China.JBIT was inaugurated in 1992.
基金supported by the High-level Talent Funding Project of Hebei Province(A202005015)Youth Top Talent Support Plan of Hebei Province.
文摘Plant protein beverage adulteration occurs frequently,which may cause health problems for consumers due to the hidden allergens.Hence,a novel method was developed for authentication by ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS).Almond,peanut,walnut and soybean were hydrolyzed,followed by separation by NanoLC-Triple TOF MS.The obtained fingerprints were identified by ProteinPilotTM combined with Uniprot,and 16 signature peptides were selected.Afterwards,plant protein beverages treated by trypsin hydrolysis were analyzed with UPLC-MS/MS.This method showed a good linear relationship with R2>0.99403.The limit of quantification(LOQ)were 0.015,0.01,0.5 and 0.05 g/L for almond,peanut,walnut and soybean,respectively.Mean recoveries ranged from 84.77%to 110.44%with RSDs<15%.The developed method was successfully applied to the adulteration detection of 31 plant protein beverages to reveal adulteration and false labeling.Conclusively,this method could provide technical support for authentication of plant protein beverages to protect the rights and health of consumers.
文摘BACKGROUND Gastric cancer(GC)is a highly aggressive malignancy with a heterogeneous nature,which makes prognosis prediction and treatment determination difficult.Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours.Inflammation also affects the prognosis of GC patients.Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis.However,the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear.AIM To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients.was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database.GC patients from the GSE26253 cohort were used for validation.Univariate and multivariate Cox analyses were used to determine the independent prognostic factors,and a prognostic nomogram was established.The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram.The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram.Gene set enrichment analysis was performed to explore the potential regulatory pathways involved.The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT.Finally,we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents.RESULTS A prognostic model consisting of three inflammatory-related genes(MRPS17,GUF1,and PDK4)was constructed.Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients.According to the risk score,GC patients were stratified into high-and low-risk groups,and patients in the high-risk group had significantly worse prognoses according to age,sex,TNM stage and Lauren type.Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging.Finally,the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group,indicating a link between inflammation-related genes and drug sensitivity.CONCLUSION In conclusion,we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.
