Large-scale geodetic data acquisition is fundamental to infrastructure lifecycle management,construction quality control,urban digital twins,and hazard monitoring,yet conventional surveying workflows remain labor-inte...Large-scale geodetic data acquisition is fundamental to infrastructure lifecycle management,construction quality control,urban digital twins,and hazard monitoring,yet conventional surveying workflows remain labor-intensive and difficult to scale in complex or hazardous environments.The industrial robot technology is proving to be an enabling technology in providing repeatable,high-throughput,and safety-conscious geodetic acquisition through its ability to offer controllable motion,stable sensor deployment,and autonomy coupled with perception stacks.The review itself is a synthesis of the recent studies on robot-based geodetic acquisition from the platform workflow application perspective.We summarize in the priority industrial robot platforms which have potential applications in geodesy,distinction being made between those based on autonomous mobile robots,mobile manipulators,fixed-base manipulators,cooperative multi-robot arrangements,and the design considerations underlying their construction:geometric stability,payload loading,and tightly constrained safety of operation.We then consider sensing configurations,principles of calibration and synchronization,as well as acquisition strategies that regulate the completeness of data and measurement consistency.The foundations of core processing are examined in light of georeferencing,registration,Simultaneous Localization and Mapping(SLAM)-based localization,and uncertainty propagation,which are essential to achieve survey-grade outputs.The evidence of application is discussed in the framework of infrastructure monitoring,construction,industrial facilities,urban/corridor mapping,mining,and indoor/underground settings,showing areas of obvious robotics advantage in repeatability and risk mitigation,as well as conditions of limitation because of the Global Navigation Satellite System(GNSS)denial,drift,calibration sensitivity,and inconsistent evaluation practices.Lastly,we determine research priorities such as benchmark datasets and metrics,accuracy-motivated autonomy,strong multisensor fusion with uncertainty results,and a closer association with Building Information Modeling(BIM)/digital twin pipelines.展开更多
In the anticorrosive coating line of a welded tube plant, the current status and existing problems of the medium-frequency induction heating equipment were discussed.Partial renovations of the power control cabinet ha...In the anticorrosive coating line of a welded tube plant, the current status and existing problems of the medium-frequency induction heating equipment were discussed.Partial renovations of the power control cabinet have been conducted.Parameters such as the DC current, DC voltage, intermediate frequency power, heating temperature, and the positioning signal at the pipe end were collected.A data acquisition and processing system, which can process data according to user needs and provide convenient data processing functions, has been developed using LabVIEW software.This system has been successfully applied in the coating line for the automatic control of high-power induction heating equipment, production management, and digital steel tube and/or digital delivery.展开更多
Background:Magnetic resonance imaging(MRI)is of considerable importance due to its wide range of applications in both scientific research and clinical diagnostics.Acquiring high-quality MRI data is of paramount import...Background:Magnetic resonance imaging(MRI)is of considerable importance due to its wide range of applications in both scientific research and clinical diagnostics.Acquiring high-quality MRI data is of paramount importance.Super-resolution reconstruction serves as a post-acquisition method capable of improving MRI data quality.Current methods predominantly utilize convolutional neural networks in super-resolution reconstruction.However,convolutional layers have inherent limitations in capturing extensive spatial dependencies due to their localized nature.Methods:We developed a new methodology that enables rapid and high-quality MRI data acquisition through a novel super-resolution approach.We proposed an innovative architecture using transformers to exploit long-range spatial dependencies present in images,allowing for an unsupervised learning framework specifically designed for super-resolution tasks tailored to individual subject.We validated our approach using both simulated data and clinical data comprising 40 scans acquired with a 3-T MRI system.Results:We obtained images with T2 contrast at an isotropic spatial resolution of 500μm in just 4 min of imaging time,and simultaneously,the signal-to-noise ratio and contrast-to-noise ratio were improved by 13.23% and 18.45%,respectively,in comparison to current leading super-resolution techniques.Conclusions:The results demonstrated that incorporating long-range spatial dependencies substantially improved super-resolution reconstruction,thereby allowing for the acquisition of high-quality MRI data with reduced imaging time.展开更多
A state-of-the-art detector array with a digital data acquisition system has been developed for charged-particle decay studies,includingβ-delayed protons,αdecay,and direct proton emissions from exotic proton-rich nu...A state-of-the-art detector array with a digital data acquisition system has been developed for charged-particle decay studies,includingβ-delayed protons,αdecay,and direct proton emissions from exotic proton-rich nuclei.The digital data acquisition system enables precise synchronization and processing of complex signals from various detectors,such as plastic scintillators,silicon detectors,and germaniumγdetectors.The system's performance was evaluated using theβdecay of^(32)Ar and its neighboring nuclei,produced via projectile fragmentation at the first Radioactive Ion Beam Line in Lanzhou(RIBLL1).Key measurements,including the half-life,charged-particle spectrum,andγ-ray spectrum,were obtained and compared with previous results for validation.Using the implantation–decay method,the isotopes of interest were implanted into two doublesided silicon strip detectors,where their subsequent decays were measured and correlated with preceding implantations using both position and time information.This detection system has potential for further applications,including the study ofβ-delayed charged-particle decay and direct proton emissions from even more exotic proton-rich nuclei.展开更多
Dengue virus(DENV)remains a pervasive global health threat,further complicated by the occurrence of neutropenia-a distinct clinical feature indicative of an altered host immune response,closely correlated with progres...Dengue virus(DENV)remains a pervasive global health threat,further complicated by the occurrence of neutropenia-a distinct clinical feature indicative of an altered host immune response,closely correlated with progressive disease deterioration and increased severity.Nevertheless,the molecular mechanisms underlying dengue-associated neutropenia remain inadequately elucidated.In this study,the comprehensive plasma proteomic profiling of dengue fever(DF)patients,DF patients with neutropenia(DFN),and healthy controls(HC)was systematically analyzed using a deep dataindependent acquisition(DIA)workflow combined with LC-MS/MS analysis,to elucidate key cellular pathways and identify promising biomarkers.DFN patients exhibited significant dual hematological alterations,with notable changes in both platelet and neutrophil counts,reflecting a complex disturbance in hematological homeostasis during dengue progression.DIA analysis quantified 2475 proteins,revealing widespread proteomic alterations among the DF,DFN,and HC subjects.Differential analysis highlighted significant fluctuations in proteins related to cytoskeletal organization,metabolic regulation,and intracellular signaling.Enrichment analyses implicated pathways such as focal adhesion,platelet activation,and PI3K-Akt signaling.Machine learning methods further identified a panel of four biomarkers-CNST,DSTN,DUSP3,and PDIA5-with high predictive accuracy for dengue diagnosis and subgroup differentiation.In conclusion,this study advances our understanding of dengue’s plasma proteomic landscape and underscores the synergistic potential of DIA-based proteomics and machine learning in unveiling host-response mechanisms,thereby informing early diagnosis and targeted therapeutic strategies.展开更多
While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as...While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as complex 3D survey planning,low signal-tonoise ratio raw data,inadequate near-surface velocity modeling,and imaging inaccuracy have long hindered the advancement of seismic exploration across this region.Through a problem-solving approach rooted in geological target analysis,this research systematically investigates the behavioral patterns of nodal seismometer-based high-density seismic acquisition in loess plateau.Tailored advancements in waveform enhancement and depth velocity modelling methodologies have been engineered.Field validations confirm that the optimized workflow demonstrates marked improvements in amplitude preservation and imaging resolution,offering novel insights for future reservoir characterization endeavors.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
This study examines whether and how cues focusing enhances Chinese-speaking English learners’engagement in comparison,thereby facilitating their acquisition of English articles within xu-based comparative continuatio...This study examines whether and how cues focusing enhances Chinese-speaking English learners’engagement in comparison,thereby facilitating their acquisition of English articles within xu-based comparative continuation writing tasks.Fifty English majors from a Chinese university were randomly assigned to three groups and each group was required to complete a comparative continuation task with one of three conditions:paired cues(cues presented in pairs),randomized cues(cues presented in random order),or implicit cues(no explicit cues provided).All participants undertook pretests,posttests,and delayed tests on English article knowledge,and ten of them volunteered to take follow-up interviews.The results indicate that:1)paired cues were more effective than randomized or implicit cues in promoting the acquisition of English articles;and 2)learners in the paired cues condition produced more target-like article usage in their continuation writings compared to those in the other two conditions.The effectiveness of paired cues is attributed to an enhanced contrast effect,which prompts learners to identify similarities and differences between cues within each pair,relates cue explanations and examples with actual article usage in the reading text,and reflects upon and compares their own article productions against those in the provided reading text.The study concludes that the process of learning through continuation is fundamentally supported by learners’capacity for comparison,reinforcing its role as a core element of xu-competence.展开更多
This interview examines the theoretical foundations,pedagogical applications,developmental trajectory,and future directions of the xu-argument.Professor Wang Chuming offers a comprehensive account of the xu-argument,c...This interview examines the theoretical foundations,pedagogical applications,developmental trajectory,and future directions of the xu-argument.Professor Wang Chuming offers a comprehensive account of the xu-argument,clarifying its theoretical framework,the learning mechanisms underlying xu,and its interface with international theories of second language acquisition(SLA).From the perspective of the xu-argument,he proposes novel interpretations of core issues in SLA.Drawing on the development of the xu-argument,Wang further discusses the essence,directions,and methodology of innovation in SLA theory.He emphasizes that theoretical advances must capture and illuminate underlying natural laws,arguing that innovative approaches are typically rooted in deep reflection on common sense.He also calls for theoretical innovation in SLA in the Chinese context,advocating a robust research paradigm that shifts from local observation to global theoretical generalization,thereby promoting bottom-up theoretical development.In closing,he highlights the promising prospects for SLA theory in the era of artificial intelligence.展开更多
This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data e...This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.展开更多
Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potent...Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potential to process complex datasets and support decision-making in OC diagnosis.Nevertheless,traditional ML models tend to be biased,overfitting,noisy,and less generalized.Moreover,their black-box nature reduces interpretability and limits their practical clinical applicability.In this study,we introduce an explainable ensemble learning(EL)model,TreeX-Stack,based on a stacking architecture that employs tree-based learners such as Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),and Extreme Gradient Boosting(XGBoost)as base learners,and Logistic Regression(LR)as the meta-learner to enhance ovarian cancer(OC)diagnosis.Local Interpretable ModelAgnostic Explanations(LIME)are used to explain individual predictions,making the model outputs more clinically interpretable and applicable.The model is trained on the dataset that includes demographic information,blood test,general chemistry,and tumor markers.Extensive preprocessing includes handling missing data using iterative imputation with Bayesian Ridge and addressing multicollinearity by removing features with correlation coefficients above 0.7.Relevant features are then selected using the Boruta feature selection method.To obtain robust and unbiased performance estimates during hyperparameter tuning,nested cross-validation(CV)with grid search is employed,and all experiments are repeated five times to ensure statistical reliability.TreeX-Stack demonstrates excellent diagnostic performance,achieving an accuracy of 0.9027,a precision of 0.8673,a recall of 0.9391,and an F1-score of 0.9012.Feature-importance analyses using LIME and permutation importance highlight Human Epididymis Protein 4(HE4)as the most significant biomarker for OC.The combination of high predictive performance and interpretability makes TreeX-Stack a reliable tool for clinical decision support in OC diagnosis.展开更多
Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and...Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.展开更多
To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative t...To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.展开更多
tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years f...tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.展开更多
0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has...0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.展开更多
Nuclear magnetic resonance(NMR)spectroscopy is a powerful tool for analyzing molecular structure and composition.However,traditional NMR experiments suffer from long acquisition times,especially in multidimensional NM...Nuclear magnetic resonance(NMR)spectroscopy is a powerful tool for analyzing molecular structure and composition.However,traditional NMR experiments suffer from long acquisition times,especially in multidimensional NMR spectroscopy.This problem,to some extent,limits broader applications of NMR techniques.Various methods have been proposed to accelerate sampling,including non-uniform sampling(NUS),multi-FID acquisition(MFA),Hadamard encoding,Fourier encoding,spatial encoding Ultrafast 2D NMR(UF2DNMR),and so on.The review focuses on rapid sampling methods developed in contemporary China,introducing their fundamental principles and applications while discussing their respective advantages and disadvantages.展开更多
Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.Howev...Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.展开更多
Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy a...Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.展开更多
With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-...With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.展开更多
Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(O...Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.展开更多
基金funded by the 2026 School-level Sci-entific Research Project(wzdzrzd202611)the 2024 Anhui Province University Scientific Research Projects(2024AH052016,2024AH052017).
文摘Large-scale geodetic data acquisition is fundamental to infrastructure lifecycle management,construction quality control,urban digital twins,and hazard monitoring,yet conventional surveying workflows remain labor-intensive and difficult to scale in complex or hazardous environments.The industrial robot technology is proving to be an enabling technology in providing repeatable,high-throughput,and safety-conscious geodetic acquisition through its ability to offer controllable motion,stable sensor deployment,and autonomy coupled with perception stacks.The review itself is a synthesis of the recent studies on robot-based geodetic acquisition from the platform workflow application perspective.We summarize in the priority industrial robot platforms which have potential applications in geodesy,distinction being made between those based on autonomous mobile robots,mobile manipulators,fixed-base manipulators,cooperative multi-robot arrangements,and the design considerations underlying their construction:geometric stability,payload loading,and tightly constrained safety of operation.We then consider sensing configurations,principles of calibration and synchronization,as well as acquisition strategies that regulate the completeness of data and measurement consistency.The foundations of core processing are examined in light of georeferencing,registration,Simultaneous Localization and Mapping(SLAM)-based localization,and uncertainty propagation,which are essential to achieve survey-grade outputs.The evidence of application is discussed in the framework of infrastructure monitoring,construction,industrial facilities,urban/corridor mapping,mining,and indoor/underground settings,showing areas of obvious robotics advantage in repeatability and risk mitigation,as well as conditions of limitation because of the Global Navigation Satellite System(GNSS)denial,drift,calibration sensitivity,and inconsistent evaluation practices.Lastly,we determine research priorities such as benchmark datasets and metrics,accuracy-motivated autonomy,strong multisensor fusion with uncertainty results,and a closer association with Building Information Modeling(BIM)/digital twin pipelines.
文摘In the anticorrosive coating line of a welded tube plant, the current status and existing problems of the medium-frequency induction heating equipment were discussed.Partial renovations of the power control cabinet have been conducted.Parameters such as the DC current, DC voltage, intermediate frequency power, heating temperature, and the positioning signal at the pipe end were collected.A data acquisition and processing system, which can process data according to user needs and provide convenient data processing functions, has been developed using LabVIEW software.This system has been successfully applied in the coating line for the automatic control of high-power induction heating equipment, production management, and digital steel tube and/or digital delivery.
基金supported in part by the Beijing Natural Science Foundation under Award Number L258055in part by the Major Program of the National Natural Science Foundation of China under Award Numbers 62394310 and 62394312in part by the National Institutes of Health(NIH)under Award Numbers R01 EB019483,R01 NS106030,R01 NS124212,R01 LM013608,R01 HD109395,R01 EB031849,R01 NS133228,R01 NS121657,R21 EB036105,and S10OD025111.
文摘Background:Magnetic resonance imaging(MRI)is of considerable importance due to its wide range of applications in both scientific research and clinical diagnostics.Acquiring high-quality MRI data is of paramount importance.Super-resolution reconstruction serves as a post-acquisition method capable of improving MRI data quality.Current methods predominantly utilize convolutional neural networks in super-resolution reconstruction.However,convolutional layers have inherent limitations in capturing extensive spatial dependencies due to their localized nature.Methods:We developed a new methodology that enables rapid and high-quality MRI data acquisition through a novel super-resolution approach.We proposed an innovative architecture using transformers to exploit long-range spatial dependencies present in images,allowing for an unsupervised learning framework specifically designed for super-resolution tasks tailored to individual subject.We validated our approach using both simulated data and clinical data comprising 40 scans acquired with a 3-T MRI system.Results:We obtained images with T2 contrast at an isotropic spatial resolution of 500μm in just 4 min of imaging time,and simultaneously,the signal-to-noise ratio and contrast-to-noise ratio were improved by 13.23% and 18.45%,respectively,in comparison to current leading super-resolution techniques.Conclusions:The results demonstrated that incorporating long-range spatial dependencies substantially improved super-resolution reconstruction,thereby allowing for the acquisition of high-quality MRI data with reduced imaging time.
基金supported by the National Key Research and Development Project,China(No.2023YFA1606404)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB34010300)+5 种基金the National Natural Science Foundation of China(Nos.12022501,12105329,12475127)the Guangdong Major Project of Basic and Applied Basic Research(No.2021B0301030006)the Research Program of Heavy Ion Science and Technology Key Laboratory,Institute of Modern Physics,Chinese Academy of Sciences(Nos.HIST2024KS04,HIST2024CO04)Longyuan Youth Innovation and Entrepreneurship Talent Project of Gansu Province(No.2024GZT04)State Key Laboratory of Nuclear Physics and Technology,Peking University(No.NPT2023KFY01)the Major Science and Technology Projects in Gansu Province(No.24GD13GA005)。
文摘A state-of-the-art detector array with a digital data acquisition system has been developed for charged-particle decay studies,includingβ-delayed protons,αdecay,and direct proton emissions from exotic proton-rich nuclei.The digital data acquisition system enables precise synchronization and processing of complex signals from various detectors,such as plastic scintillators,silicon detectors,and germaniumγdetectors.The system's performance was evaluated using theβdecay of^(32)Ar and its neighboring nuclei,produced via projectile fragmentation at the first Radioactive Ion Beam Line in Lanzhou(RIBLL1).Key measurements,including the half-life,charged-particle spectrum,andγ-ray spectrum,were obtained and compared with previous results for validation.Using the implantation–decay method,the isotopes of interest were implanted into two doublesided silicon strip detectors,where their subsequent decays were measured and correlated with preceding implantations using both position and time information.This detection system has potential for further applications,including the study ofβ-delayed charged-particle decay and direct proton emissions from even more exotic proton-rich nuclei.
基金supported by National Key R&D Program of China(2023YFA0915600)Guangdong Basic and Applied Basic Research Foundation(2025B1515020010)+3 种基金Shenzhen Clinical Research Center for Emerging Infectious Diseases(LCYSSQ20220823091203007)Sanming Project of Medicine in Shenzhen(SZSM202311033)Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP011)Shenzhen High-level Hospital Construction Fund(XKJSCRGRK-006).
文摘Dengue virus(DENV)remains a pervasive global health threat,further complicated by the occurrence of neutropenia-a distinct clinical feature indicative of an altered host immune response,closely correlated with progressive disease deterioration and increased severity.Nevertheless,the molecular mechanisms underlying dengue-associated neutropenia remain inadequately elucidated.In this study,the comprehensive plasma proteomic profiling of dengue fever(DF)patients,DF patients with neutropenia(DFN),and healthy controls(HC)was systematically analyzed using a deep dataindependent acquisition(DIA)workflow combined with LC-MS/MS analysis,to elucidate key cellular pathways and identify promising biomarkers.DFN patients exhibited significant dual hematological alterations,with notable changes in both platelet and neutrophil counts,reflecting a complex disturbance in hematological homeostasis during dengue progression.DIA analysis quantified 2475 proteins,revealing widespread proteomic alterations among the DF,DFN,and HC subjects.Differential analysis highlighted significant fluctuations in proteins related to cytoskeletal organization,metabolic regulation,and intracellular signaling.Enrichment analyses implicated pathways such as focal adhesion,platelet activation,and PI3K-Akt signaling.Machine learning methods further identified a panel of four biomarkers-CNST,DSTN,DUSP3,and PDIA5-with high predictive accuracy for dengue diagnosis and subgroup differentiation.In conclusion,this study advances our understanding of dengue’s plasma proteomic landscape and underscores the synergistic potential of DIA-based proteomics and machine learning in unveiling host-response mechanisms,thereby informing early diagnosis and targeted therapeutic strategies.
文摘While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as complex 3D survey planning,low signal-tonoise ratio raw data,inadequate near-surface velocity modeling,and imaging inaccuracy have long hindered the advancement of seismic exploration across this region.Through a problem-solving approach rooted in geological target analysis,this research systematically investigates the behavioral patterns of nodal seismometer-based high-density seismic acquisition in loess plateau.Tailored advancements in waveform enhancement and depth velocity modelling methodologies have been engineered.Field validations confirm that the optimized workflow demonstrates marked improvements in amplitude preservation and imaging resolution,offering novel insights for future reservoir characterization endeavors.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
文摘This study examines whether and how cues focusing enhances Chinese-speaking English learners’engagement in comparison,thereby facilitating their acquisition of English articles within xu-based comparative continuation writing tasks.Fifty English majors from a Chinese university were randomly assigned to three groups and each group was required to complete a comparative continuation task with one of three conditions:paired cues(cues presented in pairs),randomized cues(cues presented in random order),or implicit cues(no explicit cues provided).All participants undertook pretests,posttests,and delayed tests on English article knowledge,and ten of them volunteered to take follow-up interviews.The results indicate that:1)paired cues were more effective than randomized or implicit cues in promoting the acquisition of English articles;and 2)learners in the paired cues condition produced more target-like article usage in their continuation writings compared to those in the other two conditions.The effectiveness of paired cues is attributed to an enhanced contrast effect,which prompts learners to identify similarities and differences between cues within each pair,relates cue explanations and examples with actual article usage in the reading text,and reflects upon and compares their own article productions against those in the provided reading text.The study concludes that the process of learning through continuation is fundamentally supported by learners’capacity for comparison,reinforcing its role as a core element of xu-competence.
文摘This interview examines the theoretical foundations,pedagogical applications,developmental trajectory,and future directions of the xu-argument.Professor Wang Chuming offers a comprehensive account of the xu-argument,clarifying its theoretical framework,the learning mechanisms underlying xu,and its interface with international theories of second language acquisition(SLA).From the perspective of the xu-argument,he proposes novel interpretations of core issues in SLA.Drawing on the development of the xu-argument,Wang further discusses the essence,directions,and methodology of innovation in SLA theory.He emphasizes that theoretical advances must capture and illuminate underlying natural laws,arguing that innovative approaches are typically rooted in deep reflection on common sense.He also calls for theoretical innovation in SLA in the Chinese context,advocating a robust research paradigm that shifts from local observation to global theoretical generalization,thereby promoting bottom-up theoretical development.In closing,he highlights the promising prospects for SLA theory in the era of artificial intelligence.
基金supported by the National Natural Science Foundation of China[grant numbers 12171158,12371474 and 12571510]Fundamental Research Funds for the Central Universities[grant number 2025ECNU-WLJC006].
文摘This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)under the grant number IMSIU-DDRSP2601.
文摘Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potential to process complex datasets and support decision-making in OC diagnosis.Nevertheless,traditional ML models tend to be biased,overfitting,noisy,and less generalized.Moreover,their black-box nature reduces interpretability and limits their practical clinical applicability.In this study,we introduce an explainable ensemble learning(EL)model,TreeX-Stack,based on a stacking architecture that employs tree-based learners such as Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),and Extreme Gradient Boosting(XGBoost)as base learners,and Logistic Regression(LR)as the meta-learner to enhance ovarian cancer(OC)diagnosis.Local Interpretable ModelAgnostic Explanations(LIME)are used to explain individual predictions,making the model outputs more clinically interpretable and applicable.The model is trained on the dataset that includes demographic information,blood test,general chemistry,and tumor markers.Extensive preprocessing includes handling missing data using iterative imputation with Bayesian Ridge and addressing multicollinearity by removing features with correlation coefficients above 0.7.Relevant features are then selected using the Boruta feature selection method.To obtain robust and unbiased performance estimates during hyperparameter tuning,nested cross-validation(CV)with grid search is employed,and all experiments are repeated five times to ensure statistical reliability.TreeX-Stack demonstrates excellent diagnostic performance,achieving an accuracy of 0.9027,a precision of 0.8673,a recall of 0.9391,and an F1-score of 0.9012.Feature-importance analyses using LIME and permutation importance highlight Human Epididymis Protein 4(HE4)as the most significant biomarker for OC.The combination of high predictive performance and interpretability makes TreeX-Stack a reliable tool for clinical decision support in OC diagnosis.
基金supported by the International Partnership program of the Chinese Academy of Sciences(170GJHZ2023074GC)National Natural Science Foundation of China(42425706 and 42488201)+1 种基金National Key Research and Development Program of China(2024YFF0807902)Beijing Natural Science Foundation(8242041),and China Postdoctoral Science Foundation(2025M770353).
文摘Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.
文摘To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.
基金supported by the National Natural Science Foundation of China(91959106)the Foundation of the Shanghai Municipal Education Commission(24RGZNC02)+4 种基金Shanghai Key Laboratory of Intelligent Information Processing,Fudan University(IIPL-2025-RD3-02)Key University Science Research Project of Anhui Province(2023AH030108)Climbing Peak Training Program for Innovative Technology team of Yijishan Hospital,Wannan Medical College(PF201904)Peak Training Program for Scientific Research of Yijishan Hospital,Wannan Medical College(GF2019G15)the talent project of the First Affiliated Hospital of Wannan Medical College(Yijishan Hospital of Wannan Medical College)(YR202422).
文摘tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.
基金supported by National Key R&D Program of China(No.2021YFF0501301)the National Natural Science Foundation of China(No.42172231)。
文摘0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.
基金financially supported by the National Natural Science Foundation of China(grant numbers 22174118,12411530077,and 22374124).
文摘Nuclear magnetic resonance(NMR)spectroscopy is a powerful tool for analyzing molecular structure and composition.However,traditional NMR experiments suffer from long acquisition times,especially in multidimensional NMR spectroscopy.This problem,to some extent,limits broader applications of NMR techniques.Various methods have been proposed to accelerate sampling,including non-uniform sampling(NUS),multi-FID acquisition(MFA),Hadamard encoding,Fourier encoding,spatial encoding Ultrafast 2D NMR(UF2DNMR),and so on.The review focuses on rapid sampling methods developed in contemporary China,introducing their fundamental principles and applications while discussing their respective advantages and disadvantages.
基金supported by the National Key R&D Program of China[Grant No.2023YFF0713600]the National Natural Science Foundation of China[Grant No.62275062]+3 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant No.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Provincethe Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.
文摘Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.
文摘With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.
基金supported by the School of Digital Science,Universiti Brunei Darussalam,Brunei.
文摘Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.
