A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environment...A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environments. At the mobile hosts, all transactions perform local pre-validation. The local pre-validation process is carried out against the committed transactions at the server in the last broadcast cycle. Transactions that survive in local pre-validation must be submitted to the server for local final validation. The new protocol eliminates conflicts between mobile read-only and mobile update transactions, and resolves data conflicts flexibly by using multiversion dynamic adjustment of serialization order to avoid unnecessary restarts of transactions. Mobile read-only transactions can be committed with no-blocking, and respond time of mobile read-only transactions is greatly shortened. The tolerance of mobile transactions of disconnections from the broadcast channel is increased. In global validation mobile distributed transactions have to do check to ensure distributed serializability in all participants. The simulation results show that the new concurrency control protocol proposed offers better performance than other protocols in terms of miss rate, restart rate, commit rate. Under high work load （think time is ls） the miss rate of DMVOCC-MVDA is only 14.6%, is significantly lower than that of other protocols. The restart rate of DMVOCC-MVDA is only 32.3%, showing that DMVOCC-MVDA can effectively reduce the restart rate of mobile transactions. And the commit rate of DMVOCC-MVDA is up to 61.2%, which is obviously higher than that of other protocols.展开更多
Recovery performance in the event of failures is very important for distributed real-time database systems. This paper presents a time-cognizant logging-based crash recovery scheme （TCLCRS） that aims at distributed ...Recovery performance in the event of failures is very important for distributed real-time database systems. This paper presents a time-cognizant logging-based crash recovery scheme （TCLCRS） that aims at distributed real-time databases, which adopts a main memory database as its ground support. In our scheme, each site maintains a real-time log for local transactions and the subtransactions, which execute at the site, and execte local checkpointing independently. Log records are stored in non-volatile high- speed store, which is divided into four different partitions based on transaction classes. During restart recovery after a site crash, partitioned crash recovery strategy is adopted to ensure that the site can be brought up before the entire local secondary database is reloaded in main memory. The partitioned crash recovery strategy not only guarantees the internal consistency to be recovered, but also guarantee the temporal consistency and recovery of the sates of physical world influenced by uncommitted transactions. Combined with two- phase commit protocol, TCLCRS can guarantee failure atomicity of distributed real-time transactions.展开更多
This paper formally defines and analyses the new notion of correctness called quasi serializability, and then outlines corresponding concurrency control protocol QDHP for distributed real-time databases. Finally, thro...This paper formally defines and analyses the new notion of correctness called quasi serializability, and then outlines corresponding concurrency control protocol QDHP for distributed real-time databases. Finally, through a series of simulation studies, it shows that using the new concurrency control protocol the performance of distributed real-time databases can be much improved.展开更多
In parallel real-time database systems, concurrency control protocols must satisfy time constraints as well as the integrity constraints. The authors present a validation concurrency control(VCC) protocol, which can e...In parallel real-time database systems, concurrency control protocols must satisfy time constraints as well as the integrity constraints. The authors present a validation concurrency control(VCC) protocol, which can enhance the performance of real-time concurrency control mechanism by reducing the number of transactions that might miss their deadlines, and compare the performance of validation concurrency control protocol with that of HP2PL(High priority two phase locking) protocol and OCC-TI-WAIT-50(Optimistic concurrency control-time interval-wait-50) protocol under shared-disk architecture by simulation. The simulation results reveal that the protocol the author presented can effectively reduce the number of transactions restarting which might miss their deadlines and performs better than HP2PL and OCC-TI-WAIT-50. It works well when arrival rate of transaction is lesser than threshold. However, due to resource contention the percentage of missing deadline increases sharply when arrival rate is greater than the threshold.展开更多
Most of the proposed concurrency control protocols for real time database systems are based on serializability theorem. Owing to the unique characteristics of real time database applications and the importance of sa...Most of the proposed concurrency control protocols for real time database systems are based on serializability theorem. Owing to the unique characteristics of real time database applications and the importance of satisfying the timing constraints of transactions, serializability is too strong as a correctness criterion and not suitable for real time databases in most cases. On the other hand, relaxed serializability including epsilon serializability and similarity serializability can allow more real time transactions to satisfy their timing constraints, but database consistency may be sacrificed to some extent. We thus propose the use of weak serializability(WSR) that is more relaxed than conflicting serializability while database consistency is maintained. In this paper, we first formally define the new notion of correctness called weak serializability. After the necessary and sufficient conditions for weak serializability are shown, corresponding concurrency control protocol WDHP(weak serializable distributed high priority protocol) is outlined for distributed real time databases, where a new lock mode called mask lock mode is proposed for simplifying the condition of global consistency. Finally, through a series of simulation studies, it is shown that using the new concurrency control protocol the performance of distributed real time databases can be greatly improved.展开更多
Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,viole...Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,violet LEDs are proposed as an alternative solution.Currently,phosphors that can be efficiently excited by violet light(with wavelengths from 400 to 420 nm)remain under development still.In this study,we utilize large language models to construct a comprehensive database of Eu^(2+)and Ce^(3+)doped phosphors for discovering novel violet-excited phosphors.A total of 822 phosphor data entries,including elemental compositions,crystal structures and excitation/emission wavelengths,have been extracted and validated from 9551 research papers.Compared with Ce^(3+)doped phosphors,the Eu^(2+)are in general more suited for violet-excited phosphors,as well as red-emitting phosphors.In particular,Eu^(2+)doped nitrides and sulfides are worth of exploration for violet-excited phosphors.This database is expected to be useful in the future development of phosphors for pc-WLEDs based on artificial intelligence methods.The datasets in this article are listed in Science Data Bank at http://doi.org/10.57760/sciencedb.34314.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
Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dua...Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
Sepsis poses a serious threat to patient survival,making timely risk assessment crucial.Predicting in-hospital mortality based on clinical indicators can aid in making better clinical decisions.Previous studies have f...Sepsis poses a serious threat to patient survival,making timely risk assessment crucial.Predicting in-hospital mortality based on clinical indicators can aid in making better clinical decisions.Previous studies have focused on classifier selection but lacked a comprehensive analysis of feature selection and data preprocessing.This study optimized machine learning models for sepsis mortality prediction by:(1)comprehensively comparing feature selection and classification methods to identify the best combination,(2)building a high-performing model with fewer features,and(3)identifying key clinically relevant indicators.Methods:Using the MIMIC-III sepsis cohort,we conducted a comprehensive analysis to determine the optimal model,including data preprocessing,data balance,classifier selection,and feature selection.Feature importance was further analyzed to identify the key predictors of in-hospital mortality.Results:The proposed Synthetic Minority Oversampling Technique-Random Forest Recursive Feature Elimination-Extreme Gradient Boosting(SMOTE-(RF-RFE)-XGB)model achieved high predictive performance with a mean Area Under the Curve(AUC)of 0.8507,while reducing the number of features from 78 to 39.Compared to other feature selection methods evaluated in this study and those reported in related literature,Random Forest Recursive Feature Elimination(RF-RFE)offers the best trade-off between accuracy,feature compactness,and stability.Additionally,feature importance rankings consistently identified Acute Physiology Score Ⅲ(APS Ⅲ),Ventilation on First Day,and Depression as the top three most influential predictors,besides the Length of Stay in ICU and Hospital.Conclusions:This study addresses key gaps by conducting a comprehensive evaluation of classifiers and feature selection methods for predicting in-hospital mortality in patients with sepsis.The proposed SMOTE-(RFRFE)-XGB model achieved a high predictive performance and stability with a compact feature set.APS III,Ventilation on First Day,and Depression were consistently identified as key predictors besides Length of Stay in ICU and Hospital.展开更多
Background:The purpose of this study was to analyze and classify adverse drug events(ADEs)related to ceftazidime/avibactam reported in the Food and Drug Administration Adverse Event Reporting System(FAERS)database and...Background:The purpose of this study was to analyze and classify adverse drug events(ADEs)related to ceftazidime/avibactam reported in the Food and Drug Administration Adverse Event Reporting System(FAERS)database and to evaluate their potential safety signals since the drug’s market introduction.Methods:This analysis systematically extracted and filtered FAERS data for ceftazidime/avibactam from its market launch in 2015 to the last quarter of 2024,utilizing the Medical Dictionary for Regulatory Activities(MedDRA)terminology for ADE recoding.The analysis employed the reporting odds ratio(ROR)method to assess the strength of ADE signals and to identify significant diseases associated with infections,the hepatobiliary system,the urinary system,and the nervous system.Results:A review of 540 adverse reaction reports revealed significant signals of adverse effects related to infections,hepatobiliary disorders,urinary system issues,and neurological impairments,including pathogen resistance,liver and kidney function impairment,encephalopathy,thrombocytopenia,and toxic epidermal necrolysis.However,these issues require further clinical attention.Conclusion:Ceftazidime/avibactam is associated with a range of adverse reactions,necessitating enhanced clinical monitoring,particularly in patients with underlying liver or kidney dysfunction.Continuous risk assessment and vigilant monitoring are critical for its clinical use.However,this study is limited by inherent reporting biases and confounders associated with the spontaneous reporting database(FAERS).Future research should validate these signals through prospective cohort and mechanistic studies and explore personalized risk management strategies for high-risk populations.展开更多
In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task exec...In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.展开更多
An integrated real-time control methodology is introduced to mitigate process-induced challenges,namely temperature overshoot,uneven cure,and interfacial shear stress,during the autoclave curing of Carbon Fiber-Reinfo...An integrated real-time control methodology is introduced to mitigate process-induced challenges,namely temperature overshoot,uneven cure,and interfacial shear stress,during the autoclave curing of Carbon Fiber-Reinforced Polymer(CFRP)composites.First,a high-fidelity Finite Element(FE)model incorporating tool-part interaction is developed to reveal the curing process of the composites,wherein the interaction is characterized by friction interface modeling with experimentally measured cure-dependent friction coefficients.The accuracy of FE model is confirmed through experimental tests on a doubly curved T-stiffened composite panel.This validated model then generates a dataset of curing temperature profile and associated defect information,which is used to train a customized Long Short-Term Memory(LSTM)neural network.We culminate in a real-time control framework that actively optimizes the curing process by integrating LSTM-based state prediction with Q-learning-driven decision logic.The optimized thermal profile demonstrates a clear performance enhancement over the traditional multi-dwell approach,achieving marked reductions in temperature difference,Degree of Cure(DoC)difference and tool-part interface shear stress,which provides more insights for intelligent composite manufacturing.展开更多
During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study invest...During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.展开更多
The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which ...The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which integration of Internet of Things(IoT)sensing,edge computing,and real-time analytics facilitates timely detection,interpretation,and prediction of the environmental conditions across the applications,such as urban air quality,watershed and coastal surveillance,industrial safety,agriculture,and disaster response.We define end-to-end architectural patterns to organize devices,edge nodes,and cloud services to satisfy latency,reliability,bandwidth,and governance constraints with emphasis on event-time processing,adaptive offloading,and hierarchical aggregation.Then we look at sensing and infrastructure foundations,emphasizing the effects of sensor modality and power autonomy,connectivity,and the practices of calibration on the practicable analytics and eventual plausibility.It is on this basis that we examine real-time analytics pipelines and Artificial Intelligence(AI)techniques to preprocess,sensor combine,anomaly detect,and short-horizon forecast,with a focus on edge-deployable models,quantification of uncertainties,and query resistance to drift and domain shift.Lastly,we address the realities of deployment that condition operational success,such as lifecycle engineering,provenance-aware data management,security and privacy risks,ethical governance,and evaluation methodologies,which place end-to-end latency and field generalization as a priority.This review offers cohesion to algorithmic capabilities and systems engineering and governance to define an overall framework,show open areas of research directions,and provide practical recommendations on how to design trustworthy,scalable,and sustainable environmental monitoring systems.展开更多
To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens...To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.展开更多
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a...An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Primary liver cancer (PLC) is a major global healthchallenge, ranking as the sixth most common andthird most fatal malignancy worldwide, according toGLOBOCAN 2022 estimates[1]. This high mortalityrate underscores the ...Primary liver cancer (PLC) is a major global healthchallenge, ranking as the sixth most common andthird most fatal malignancy worldwide, according toGLOBOCAN 2022 estimates[1]. This high mortalityrate underscores the aggressive nature of thedisease and the significant burden it places on globalhealthcare systems. Although primary preventionremains the cornerstone of liver cancer control,improving outcomes for patients already diagnosedis equally critical for mitigating the impact of thedisease.展开更多
基金Project(20030533011)supported by the National Research Foundation for the Doctoral Program of Higher Education of China
文摘A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environments. At the mobile hosts, all transactions perform local pre-validation. The local pre-validation process is carried out against the committed transactions at the server in the last broadcast cycle. Transactions that survive in local pre-validation must be submitted to the server for local final validation. The new protocol eliminates conflicts between mobile read-only and mobile update transactions, and resolves data conflicts flexibly by using multiversion dynamic adjustment of serialization order to avoid unnecessary restarts of transactions. Mobile read-only transactions can be committed with no-blocking, and respond time of mobile read-only transactions is greatly shortened. The tolerance of mobile transactions of disconnections from the broadcast channel is increased. In global validation mobile distributed transactions have to do check to ensure distributed serializability in all participants. The simulation results show that the new concurrency control protocol proposed offers better performance than other protocols in terms of miss rate, restart rate, commit rate. Under high work load （think time is ls） the miss rate of DMVOCC-MVDA is only 14.6%, is significantly lower than that of other protocols. The restart rate of DMVOCC-MVDA is only 32.3%, showing that DMVOCC-MVDA can effectively reduce the restart rate of mobile transactions. And the commit rate of DMVOCC-MVDA is up to 61.2%, which is obviously higher than that of other protocols.
基金Project supported by National Natural Science Foundation ofChina (Grant No .60203017) Defense Pre-research Projectof the"Tenth Five-Year-Plan"of China (Grant No .413150403)
文摘Recovery performance in the event of failures is very important for distributed real-time database systems. This paper presents a time-cognizant logging-based crash recovery scheme （TCLCRS） that aims at distributed real-time databases, which adopts a main memory database as its ground support. In our scheme, each site maintains a real-time log for local transactions and the subtransactions, which execute at the site, and execte local checkpointing independently. Log records are stored in non-volatile high- speed store, which is divided into four different partitions based on transaction classes. During restart recovery after a site crash, partitioned crash recovery strategy is adopted to ensure that the site can be brought up before the entire local secondary database is reloaded in main memory. The partitioned crash recovery strategy not only guarantees the internal consistency to be recovered, but also guarantee the temporal consistency and recovery of the sates of physical world influenced by uncommitted transactions. Combined with two- phase commit protocol, TCLCRS can guarantee failure atomicity of distributed real-time transactions.
基金the National Natural Science Foundation of China and the Commission of Science,Technokgy and Industry for National Defense
文摘This paper formally defines and analyses the new notion of correctness called quasi serializability, and then outlines corresponding concurrency control protocol QDHP for distributed real-time databases. Finally, through a series of simulation studies, it shows that using the new concurrency control protocol the performance of distributed real-time databases can be much improved.
文摘In parallel real-time database systems, concurrency control protocols must satisfy time constraints as well as the integrity constraints. The authors present a validation concurrency control(VCC) protocol, which can enhance the performance of real-time concurrency control mechanism by reducing the number of transactions that might miss their deadlines, and compare the performance of validation concurrency control protocol with that of HP2PL(High priority two phase locking) protocol and OCC-TI-WAIT-50(Optimistic concurrency control-time interval-wait-50) protocol under shared-disk architecture by simulation. The simulation results reveal that the protocol the author presented can effectively reduce the number of transactions restarting which might miss their deadlines and performs better than HP2PL and OCC-TI-WAIT-50. It works well when arrival rate of transaction is lesser than threshold. However, due to resource contention the percentage of missing deadline increases sharply when arrival rate is greater than the threshold.
文摘Most of the proposed concurrency control protocols for real time database systems are based on serializability theorem. Owing to the unique characteristics of real time database applications and the importance of satisfying the timing constraints of transactions, serializability is too strong as a correctness criterion and not suitable for real time databases in most cases. On the other hand, relaxed serializability including epsilon serializability and similarity serializability can allow more real time transactions to satisfy their timing constraints, but database consistency may be sacrificed to some extent. We thus propose the use of weak serializability(WSR) that is more relaxed than conflicting serializability while database consistency is maintained. In this paper, we first formally define the new notion of correctness called weak serializability. After the necessary and sufficient conditions for weak serializability are shown, corresponding concurrency control protocol WDHP(weak serializable distributed high priority protocol) is outlined for distributed real time databases, where a new lock mode called mask lock mode is proposed for simplifying the condition of global consistency. Finally, through a series of simulation studies, it is shown that using the new concurrency control protocol the performance of distributed real time databases can be greatly improved.
基金National Key Research and Development Program of China(2021YFB3500501)。
文摘Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,violet LEDs are proposed as an alternative solution.Currently,phosphors that can be efficiently excited by violet light(with wavelengths from 400 to 420 nm)remain under development still.In this study,we utilize large language models to construct a comprehensive database of Eu^(2+)and Ce^(3+)doped phosphors for discovering novel violet-excited phosphors.A total of 822 phosphor data entries,including elemental compositions,crystal structures and excitation/emission wavelengths,have been extracted and validated from 9551 research papers.Compared with Ce^(3+)doped phosphors,the Eu^(2+)are in general more suited for violet-excited phosphors,as well as red-emitting phosphors.In particular,Eu^(2+)doped nitrides and sulfides are worth of exploration for violet-excited phosphors.This database is expected to be useful in the future development of phosphors for pc-WLEDs based on artificial intelligence methods.The datasets in this article are listed in Science Data Bank at http://doi.org/10.57760/sciencedb.34314.
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 the National Institute of Health(R37CA240806,U01CA288351,and R50CA283816)support from UCI Chao Family Comprehensive Cancer Center(P30CA062203).
文摘Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
文摘Sepsis poses a serious threat to patient survival,making timely risk assessment crucial.Predicting in-hospital mortality based on clinical indicators can aid in making better clinical decisions.Previous studies have focused on classifier selection but lacked a comprehensive analysis of feature selection and data preprocessing.This study optimized machine learning models for sepsis mortality prediction by:(1)comprehensively comparing feature selection and classification methods to identify the best combination,(2)building a high-performing model with fewer features,and(3)identifying key clinically relevant indicators.Methods:Using the MIMIC-III sepsis cohort,we conducted a comprehensive analysis to determine the optimal model,including data preprocessing,data balance,classifier selection,and feature selection.Feature importance was further analyzed to identify the key predictors of in-hospital mortality.Results:The proposed Synthetic Minority Oversampling Technique-Random Forest Recursive Feature Elimination-Extreme Gradient Boosting(SMOTE-(RF-RFE)-XGB)model achieved high predictive performance with a mean Area Under the Curve(AUC)of 0.8507,while reducing the number of features from 78 to 39.Compared to other feature selection methods evaluated in this study and those reported in related literature,Random Forest Recursive Feature Elimination(RF-RFE)offers the best trade-off between accuracy,feature compactness,and stability.Additionally,feature importance rankings consistently identified Acute Physiology Score Ⅲ(APS Ⅲ),Ventilation on First Day,and Depression as the top three most influential predictors,besides the Length of Stay in ICU and Hospital.Conclusions:This study addresses key gaps by conducting a comprehensive evaluation of classifiers and feature selection methods for predicting in-hospital mortality in patients with sepsis.The proposed SMOTE-(RFRFE)-XGB model achieved a high predictive performance and stability with a compact feature set.APS III,Ventilation on First Day,and Depression were consistently identified as key predictors besides Length of Stay in ICU and Hospital.
基金Intramural Project of The First Affiliated Hospital of Guangxi University of Chinese Medicine(2018QN008).
文摘Background:The purpose of this study was to analyze and classify adverse drug events(ADEs)related to ceftazidime/avibactam reported in the Food and Drug Administration Adverse Event Reporting System(FAERS)database and to evaluate their potential safety signals since the drug’s market introduction.Methods:This analysis systematically extracted and filtered FAERS data for ceftazidime/avibactam from its market launch in 2015 to the last quarter of 2024,utilizing the Medical Dictionary for Regulatory Activities(MedDRA)terminology for ADE recoding.The analysis employed the reporting odds ratio(ROR)method to assess the strength of ADE signals and to identify significant diseases associated with infections,the hepatobiliary system,the urinary system,and the nervous system.Results:A review of 540 adverse reaction reports revealed significant signals of adverse effects related to infections,hepatobiliary disorders,urinary system issues,and neurological impairments,including pathogen resistance,liver and kidney function impairment,encephalopathy,thrombocytopenia,and toxic epidermal necrolysis.However,these issues require further clinical attention.Conclusion:Ceftazidime/avibactam is associated with a range of adverse reactions,necessitating enhanced clinical monitoring,particularly in patients with underlying liver or kidney dysfunction.Continuous risk assessment and vigilant monitoring are critical for its clinical use.However,this study is limited by inherent reporting biases and confounders associated with the spontaneous reporting database(FAERS).Future research should validate these signals through prospective cohort and mechanistic studies and explore personalized risk management strategies for high-risk populations.
基金Supported by the National Program on Key Basic Research Project(2020YFA0713600)the National Natural Science Foundation of China(62272214)。
文摘In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.
基金provided by the National Key Research and Development Program of China(No.2021YFB3401700)the National Natural Science Foundation of China(Nos.12302189 and 12220101002)the Shaanxi Provincial Key Science and Technology Innovation Team,China(No.2023-CX-TD-14)。
文摘An integrated real-time control methodology is introduced to mitigate process-induced challenges,namely temperature overshoot,uneven cure,and interfacial shear stress,during the autoclave curing of Carbon Fiber-Reinforced Polymer(CFRP)composites.First,a high-fidelity Finite Element(FE)model incorporating tool-part interaction is developed to reveal the curing process of the composites,wherein the interaction is characterized by friction interface modeling with experimentally measured cure-dependent friction coefficients.The accuracy of FE model is confirmed through experimental tests on a doubly curved T-stiffened composite panel.This validated model then generates a dataset of curing temperature profile and associated defect information,which is used to train a customized Long Short-Term Memory(LSTM)neural network.We culminate in a real-time control framework that actively optimizes the curing process by integrating LSTM-based state prediction with Q-learning-driven decision logic.The optimized thermal profile demonstrates a clear performance enhancement over the traditional multi-dwell approach,achieving marked reductions in temperature difference,Degree of Cure(DoC)difference and tool-part interface shear stress,which provides more insights for intelligent composite manufacturing.
基金National Natural Science Foundation of China,Grant/Award Number:52104120Hunan Provincial Key Laboratory of Key Technology on Hydropower Development,Grant/Award Number:PKLHD202303。
文摘During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures.
基金supported by Jiangxi Polytechnic Institute Key Research Topics in Educational Reform 2025-JGJG-07.
文摘The intelligent environmental sensing systems are quickly transforming the sparse and retrospective monitoring to dense and decision-oriented environmental intelligence.This review brings together the manner in which integration of Internet of Things(IoT)sensing,edge computing,and real-time analytics facilitates timely detection,interpretation,and prediction of the environmental conditions across the applications,such as urban air quality,watershed and coastal surveillance,industrial safety,agriculture,and disaster response.We define end-to-end architectural patterns to organize devices,edge nodes,and cloud services to satisfy latency,reliability,bandwidth,and governance constraints with emphasis on event-time processing,adaptive offloading,and hierarchical aggregation.Then we look at sensing and infrastructure foundations,emphasizing the effects of sensor modality and power autonomy,connectivity,and the practices of calibration on the practicable analytics and eventual plausibility.It is on this basis that we examine real-time analytics pipelines and Artificial Intelligence(AI)techniques to preprocess,sensor combine,anomaly detect,and short-horizon forecast,with a focus on edge-deployable models,quantification of uncertainties,and query resistance to drift and domain shift.Lastly,we address the realities of deployment that condition operational success,such as lifecycle engineering,provenance-aware data management,security and privacy risks,ethical governance,and evaluation methodologies,which place end-to-end latency and field generalization as a priority.This review offers cohesion to algorithmic capabilities and systems engineering and governance to define an overall framework,show open areas of research directions,and provide practical recommendations on how to design trustworthy,scalable,and sustainable environmental monitoring systems.
基金supported by the National Natural Science Foundation of China(Grant No.42077244)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0434).
文摘To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金National Key Project of Research and Development Program of China[2021YFC2500404].
文摘Primary liver cancer (PLC) is a major global healthchallenge, ranking as the sixth most common andthird most fatal malignancy worldwide, according toGLOBOCAN 2022 estimates[1]. This high mortalityrate underscores the aggressive nature of thedisease and the significant burden it places on globalhealthcare systems. Although primary preventionremains the cornerstone of liver cancer control,improving outcomes for patients already diagnosedis equally critical for mitigating the impact of thedisease.
