With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Further...With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.展开更多
In the context of the coordinated pursuit of"carbon peak and neutrality"objectives,alongside the strategy to establish a robust agricultural nation,the economic and social development of rural areas is under...In the context of the coordinated pursuit of"carbon peak and neutrality"objectives,alongside the strategy to establish a robust agricultural nation,the economic and social development of rural areas is undergoing a profound paradigm shift.The traditional rural division of labor pattern,which depends on tangible factors such as land,labor,and capital,has increasingly encountered developmental challenges characterized by diminishing marginal returns and a detrimental cycle of internal competition.The new quality productive force,centered on data,algorithms,green technologies,bioengineering,and clean energy,offers a potential pathway for the rural division of labor system to overcome the"low-level equilibrium".This force is characterized by attributes such as non-exclusivity,replicability,network collaboration,and ecological compatibility.This paper develops a three-dimensional collaborative analytical framework encompassing"technology,institution,and culture".It systematically elucidates the internal logic by which new quality productive forces drive the transformation of the rural division of labor from"quantitative factor matching"to"qualitative structural reorganization"through three principal mechanisms:technology embedding,institutional reconstruction,and cultural coupling.Furthermore,the study proposes corresponding policy recommendations,thereby offering theoretical insights to support the modernization of China s agriculture and rural areas,as well as the development of a strong agricultural country.展开更多
Lactate,as a metabolite,plays a significant role in a number of fields,including medical diagnostics,exercise physiology and food science.Traditional methods for lactate measurement often involve expensive and cumbers...Lactate,as a metabolite,plays a significant role in a number of fields,including medical diagnostics,exercise physiology and food science.Traditional methods for lactate measurement often involve expensive and cumbersome instrumentation.This study developed a portable and low-cost lactate measurement system,including independently detectable hardware circuits and user-friendly embedded software,computer,and smartphone applications.The experiment verified that the relative error of the detection current in the device circuit was less than 1%.The electrochemical performance was measured by comparing the[Fe(CN)_(6)]^(3−)/[Fe(CN)_(6)]^(4−)solution with the desktop electrochemical workstation CHI660E,and a nearly consistent chronoamperometry(CA)curve was obtained.Two modified lactate sensors were used for CA testing of lactate.Within the concentration range of 0.1 mmol·L^(−1)to 20 mmol·L^(−1),there was a good linear relationship between lactate concentration and steady-state current,with a correlation coefficient(R2)greater than 0.99 and good repeatability,demonstrating the reliability of the developed device.The lactate measurement system developed in this study not only provides excellent detection performance and reliability,but also achieves portability and low cost,providing a new solution for lactate measurement.展开更多
Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrat...Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.展开更多
Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded...Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.展开更多
Objective To systematically evaluate the effect of acupoint catgut embedding therapy （ACET） for simple obesity. Methods Computer retrieval was used for randomized controlled trials on the treatment effect of simple ...Objective To systematically evaluate the effect of acupoint catgut embedding therapy （ACET） for simple obesity. Methods Computer retrieval was used for randomized controlled trials on the treatment effect of simple obesity with ACET in databases of the China National Knowledge Infrastructure （CNKI）, Wanfang Data system, and the China Biology Medicine disc （CBMDisc）. The Jadad Quality Scale was used in the evaluation of included studies. The outcome indicators were analyzed with the Review Manage 5.1 software. Results A total of 16 randomized controlled trials were included finally. The meta-analysis result showed that compared with the control group, there was statistically significance on the total efficiency of using ACET for simple obesity [OR=2.51, 95% confidence interval （1.74, 3.63）, Z=4.91, P〈0.000 01]. The analysis on the literature quality showed that there was only 2 article marked as 3 points. The other 25 articles marked ≤2 points. The quality of published articles was generally low. There were publication biases and the blinding method was seldom used, the losses of follow-up / drop oup / withdraw were reported with less. There were 27 acupoints used in the treatment, which mainly included Tianshu （天枢ST 25）, Zhongwan （中脘 CV 12）, Fenglong （丰隆 ST 40）, Shufen （水分 CV 9）, Qihai （气海 CV 6）, Sanyinjiao （三阴交 SP 6）, Zusnli （足三里 ST 36）, Ashi point, Daheng （大横 SP 25）. The five kinds of catgut embedding needle were injection needles ＋ acupuncture needle, specialized catgut embedding needle, spinal needle, triangular needle, and skin suture needle. Conclusion There is definite efficiency of using ACET in the treatment of simple obesity. However, the clinical efficiency still lacks of sufficient evidences. Therefore, further clinical research should be conducted in the providing of reliable evidences in the clinical decision-making in the future.展开更多
The embedded data acquistition technology in vehicle electronic system was discussed. This technology adopts the parallel working mode, gets vehicle electronic system data by communication. This technology can provide...The embedded data acquistition technology in vehicle electronic system was discussed. This technology adopts the parallel working mode, gets vehicle electronic system data by communication. This technology can provide us a lot of information on the electronic control unit, is very useful for the development of the vehicle electronic system, and can be used in diagnosis. The key points to this technology are the timer interrupt, A/D interrupt, communication interrupt and real time operation. This technology has been validated by the application in the electronic control mechanism transmission shifting system of the tank.展开更多
Unmanned aerial vehicles(UAVs),especially quadcopters,have become indispensable in numerous industrial and scientific applications due to their flexibility,lowcost,and capability to operate in dynamic environments.Thi...Unmanned aerial vehicles(UAVs),especially quadcopters,have become indispensable in numerous industrial and scientific applications due to their flexibility,lowcost,and capability to operate in dynamic environments.This paper presents a complete design and implementation of a compact autonomous quadcopter capable of trajectory tracking,object detection,precision landing,and real-time telemetry via long-range communication protocols.The system integrates an onboard flight controller running real-time sensor fusion algorithms,a vision-based detection system on a companion single-board computer,and a telemetry unit using Long Range(LoRa)communication.Extensive flight tests were conducted to validate the system’s stability,communication range,and autonomous capabilities.Potential applications in law enforcement,agriculture,search and rescue,and environmental monitoring are also discussed.展开更多
Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the ...Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the complex interactions between inks and support baths.Here,we present an artificial intelligence(AI)-driven framework that interprets and predicts embedded printability using rheological data.Using a standardized workflow,we extracted 21 rheological descriptors and established 12 indicators to evaluate structural continuity and geometric fidelity.Interpretable machine learning models revealed that direction-dependent defects are governed by the synergistic interplay among ink yield stress,support bath zero shear viscosity,flow behavior index,and time constant.To enable the prediction of printability in a generalizable manner,we further developed a cascaded neural network,which achieved mean relative prediction errors below 15%across all indicators.Experimental validation using three-dimensional(3 D)-printed constructs and micro-computed tomography(μCT)reconstructions confirmed a strong correlation between predicted and actual fidelity.This work establishes a physics-informed,data-driven paradigm for decoding and optimizing embedded printing,offering broad applicability and providing a robust tool for the rapid pairing of suitable printable ink-support bath combinations.展开更多
Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame...Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.展开更多
Community detection is a fundamental problem in network analysis for identifying densely connected node clusters,with successful applications in diverse fields like social networks,recommendation systems,biology,and c...Community detection is a fundamental problem in network analysis for identifying densely connected node clusters,with successful applications in diverse fields like social networks,recommendation systems,biology,and cyberattack detection.Overlapping community detection refers to the case of a node belonging to multiple communities simultaneously,which is a much more meaningful and challenging task.Graph representation learning with Evolutionary Computation has been studied well in overlapping community detection to deal with complex network structures and characteristics.However,most of them focus on searching the entire solution space,which can be inefficient and lead to inadequate results.To overcome the problem,a structural feature node extraction method is first proposed that can effectively map a network into a structural embedding space.Thus,nodes within the network are classified into hierarchical levels based on their structural feature strength,and only nodes with relatively high strength are considered in subsequent search steps to reduce the search space.Then,a maximal-clique representation method is employed on the given vertex set to identify overlapping nodes.A hybrid clique-based multi-objective evolutionary algorithmwith decomposition method is designed to address cliques and the remaining unexplored nodes separately.The number of communities generated with this allocation method is closer to the actual partition count with high division quality.Experimental results on nine usually used real-world networks,five synthetic networks,and two large-scale networks demonstrate the effectiveness of the proposed methodology in terms of community quality and algorithmic efficiency,compared to traditional,MOEA-based,and graph embedding-based community detection algorithms.展开更多
Objective This review aimed to systematically evaluate the evidence on the effects of acupoint catgut embedding （ACE） therapy for patients with polycystic ovary syndrome （PCOS）. Methods Five databases （CBM, CNKI,...Objective This review aimed to systematically evaluate the evidence on the effects of acupoint catgut embedding （ACE） therapy for patients with polycystic ovary syndrome （PCOS）. Methods Five databases （CBM, CNKI, Wanfang Database, VlP Database, and PubMed） were searched to identify relevant studies published before June 2027. The outcomes were resumption of menstruation and serum levels of testosterone （T）. The methodological quality of the included studies was judged using the Cochrane risk of bias tool. The overall level of evidence was judged by the Grading of Recommendations Assessment, Development, and Evaluation （GRADE） criteria. Results Twenty- five randomized controlled trials were included. ACE therapy significantly lowered serum T levels, and patients receiving ACE treatment reported resumption of menstruation. However, these results should be interpreted with caution due to a high risk of randomization and blinding bias, and likely publication bias. The level of evidence for resumption of menstruation and serum T levels was assessed as ＂low＂ and ＂low＂, respectively, using GRADE. Conclusion The current evidence on ACE therapy for PCOS is insufficient to draw firm conclusions due to the poor methodological quality. Future well- designed trials are needed to validate the therapeutic efficacy, safety, and mechanisms of ACE in patients with PCOS.展开更多
Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,inclu...Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.展开更多
In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the effor...In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the efforts of researchers and security experts to protect information systems from these attacks,the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks.The latest remarkable achievements of large language models(LLMs)in NLP tasks have caught the attention of cybersecurity researchers to integrate thesemodels into security threat detection.Thesemodels offer high embedding capabilities,able to extract rich semantic representations and paving theway formore accurate and adaptive solutions.In this context,we propose a new approach for ransomware detection based on an ensemblemethod that leverages three distinctLLMembeddingmodels.This ensemble strategy takes advantage of the variety of embedding methods and the strengths of each model.In the proposed solution,each embedding model is associated with an independently trainedMLP classifier.The predictions obtained are then merged using a weighted voting technique,assigning each model an influence proportional to its performance.This approach makes it possible to exploit the complementarity of representations,improve detection accuracy and robustness,and offer a more reliable solution in the face of the growing diversity and complexity of modern ransomware.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
A system of number recognition with a graphic user interface （GUI） is implemented on the embedded development platform by using the fuzzy pattern recognition method. An application interface （API） of uC/ OS-Ⅱ is ...A system of number recognition with a graphic user interface （GUI） is implemented on the embedded development platform by using the fuzzy pattern recognition method. An application interface （API） of uC/ OS-Ⅱ is used to implement the features of multi-task concurrency and the communications among tasks. Handwriting function is implemented by the improvement of the interface provided by the platform. Fuzzy pattern recognition technology based on fuzzy theory is used to analyze the input of handwriting. A primary system for testing is implemented. It can receive and analyze user inputs from both keyboard and touch-screen. The experimental results show that the embedded fuzzy recognition system which uses the technology which integrates two ways of fuzzy recognition can retain a high recognition rate and reduce hardware requirements.展开更多
OBJECTIVE: To evaluate the clinical effectiveness and safety of acupoint catgut embedding and acupuncture on simple obesity by Meta-analysis. METHODS: Studies on clinical randomized controlled trials of acupoint catgu...OBJECTIVE: To evaluate the clinical effectiveness and safety of acupoint catgut embedding and acupuncture on simple obesity by Meta-analysis. METHODS: Studies on clinical randomized controlled trials of acupoint catgut embedding for simple obesity which were published from January 2015 to November 2020 were searched in Cochrane Central Register of Control Trials(Central), Pub Med, China Science and Technology Journal Database, China National Knowledge Infrastructure Database and Wanfang databases. And those that met the inclusion criteria were screened. Rev Man5.3 was used for Meta-analysis. The “Risk of Bias” tool was used to evaluate the quality of included studies. R studio software was used for the measurement of publication bias. RESULTS: A total of 33 studies were included for Metaanalysis, including 2685 patients with simple obesity. Meta-analysis results showed the comparison of effectiveness rate was relative risk(RR) = 1.12, 95%CI(1.08, 1.16), body mass index(BMI) was mean difference(MD) =-1.12, 95%CI(-2.09,-0.14), waist circumference was MD =-2.14, 95%CI(-4.22,-0.06), and body mass was MD =-2.36, 95%CI(-3.99,-0.73). On the basis of diet and exercise intervention, the effectiveness rate [RR = 1.12, 95%CI(1.05, 1.19)], BMI [MD =-0.88, 95%CI(-1.35,-0.40)], waist circumference [MD =-1.10, 95%CI(-4.27, 2.07)], and body mass [MD =-0.68, 95%CI(-2.90, 1.54)]. The risk of bias of included literatures was low. CONCLUSIONS: Acupoint catgut embedding therapy was slightly better than acupuncture therapy in most of the outcomes. Moreover, the treatment frequency of acupoint catgut embedding is less with larger stimulation intensity, which is more conducive to clinical promotion.展开更多
基金supported by the National Natural Science Foundation of China under Grant 62471493supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066。
文摘With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.
基金Supported by Key Project of Jiangsu Education Science Planning"Research on the Structural Adjustment of Higher Education in Jiangsu in the Context of High-Quality Economic Development"(B/2021/01/67).
文摘In the context of the coordinated pursuit of"carbon peak and neutrality"objectives,alongside the strategy to establish a robust agricultural nation,the economic and social development of rural areas is undergoing a profound paradigm shift.The traditional rural division of labor pattern,which depends on tangible factors such as land,labor,and capital,has increasingly encountered developmental challenges characterized by diminishing marginal returns and a detrimental cycle of internal competition.The new quality productive force,centered on data,algorithms,green technologies,bioengineering,and clean energy,offers a potential pathway for the rural division of labor system to overcome the"low-level equilibrium".This force is characterized by attributes such as non-exclusivity,replicability,network collaboration,and ecological compatibility.This paper develops a three-dimensional collaborative analytical framework encompassing"technology,institution,and culture".It systematically elucidates the internal logic by which new quality productive forces drive the transformation of the rural division of labor from"quantitative factor matching"to"qualitative structural reorganization"through three principal mechanisms:technology embedding,institutional reconstruction,and cultural coupling.Furthermore,the study proposes corresponding policy recommendations,thereby offering theoretical insights to support the modernization of China s agriculture and rural areas,as well as the development of a strong agricultural country.
基金supported by National Natural Science Foundation of China(No.62006092)Natural Science Research Project of Anhui Educational Committee(No.2023AH030081)+1 种基金2023 New Era Education Provincial Quality Engineering Project(Graduate Education)(No.2023cxcysj103)2024 New Era Education Provincial Quality Engineering Project(Graduate Education)。
文摘Lactate,as a metabolite,plays a significant role in a number of fields,including medical diagnostics,exercise physiology and food science.Traditional methods for lactate measurement often involve expensive and cumbersome instrumentation.This study developed a portable and low-cost lactate measurement system,including independently detectable hardware circuits and user-friendly embedded software,computer,and smartphone applications.The experiment verified that the relative error of the detection current in the device circuit was less than 1%.The electrochemical performance was measured by comparing the[Fe(CN)_(6)]^(3−)/[Fe(CN)_(6)]^(4−)solution with the desktop electrochemical workstation CHI660E,and a nearly consistent chronoamperometry(CA)curve was obtained.Two modified lactate sensors were used for CA testing of lactate.Within the concentration range of 0.1 mmol·L^(−1)to 20 mmol·L^(−1),there was a good linear relationship between lactate concentration and steady-state current,with a correlation coefficient(R2)greater than 0.99 and good repeatability,demonstrating the reliability of the developed device.The lactate measurement system developed in this study not only provides excellent detection performance and reliability,but also achieves portability and low cost,providing a new solution for lactate measurement.
基金supported by the National Key R&D Program of China(Grant No.2021YFA1001000)the National Natural Science Foundation of China(Grant Nos.82111530212,U23A20282,and 61971255)+2 种基金the Natural Science Founda-tion of Guangdong Province(Grant No.2021B1515020092)the Shenzhen Bay Laboratory Fund(Grant No.SZBL2020090501014)the Shenzhen Science,Technology and Innovation Commission(Grant Nos.KJZD20231023094659002,JCYJ20220530142809022,and WDZC20220811170401001).
文摘Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.
基金supported by the National Natural Science Foundation of China(62303273,62373226)the National Research Foundation,Singapore through the Medium Sized Center for Advanced Robotics Technology Innovation(WP2.7)
文摘Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.
基金Supported by the Naional Natural Science Fund Project of China:81072883,81173342
文摘Objective To systematically evaluate the effect of acupoint catgut embedding therapy （ACET） for simple obesity. Methods Computer retrieval was used for randomized controlled trials on the treatment effect of simple obesity with ACET in databases of the China National Knowledge Infrastructure （CNKI）, Wanfang Data system, and the China Biology Medicine disc （CBMDisc）. The Jadad Quality Scale was used in the evaluation of included studies. The outcome indicators were analyzed with the Review Manage 5.1 software. Results A total of 16 randomized controlled trials were included finally. The meta-analysis result showed that compared with the control group, there was statistically significance on the total efficiency of using ACET for simple obesity [OR=2.51, 95% confidence interval （1.74, 3.63）, Z=4.91, P〈0.000 01]. The analysis on the literature quality showed that there was only 2 article marked as 3 points. The other 25 articles marked ≤2 points. The quality of published articles was generally low. There were publication biases and the blinding method was seldom used, the losses of follow-up / drop oup / withdraw were reported with less. There were 27 acupoints used in the treatment, which mainly included Tianshu （天枢ST 25）, Zhongwan （中脘 CV 12）, Fenglong （丰隆 ST 40）, Shufen （水分 CV 9）, Qihai （气海 CV 6）, Sanyinjiao （三阴交 SP 6）, Zusnli （足三里 ST 36）, Ashi point, Daheng （大横 SP 25）. The five kinds of catgut embedding needle were injection needles ＋ acupuncture needle, specialized catgut embedding needle, spinal needle, triangular needle, and skin suture needle. Conclusion There is definite efficiency of using ACET in the treatment of simple obesity. However, the clinical efficiency still lacks of sufficient evidences. Therefore, further clinical research should be conducted in the providing of reliable evidences in the clinical decision-making in the future.
文摘The embedded data acquistition technology in vehicle electronic system was discussed. This technology adopts the parallel working mode, gets vehicle electronic system data by communication. This technology can provide us a lot of information on the electronic control unit, is very useful for the development of the vehicle electronic system, and can be used in diagnosis. The key points to this technology are the timer interrupt, A/D interrupt, communication interrupt and real time operation. This technology has been validated by the application in the electronic control mechanism transmission shifting system of the tank.
文摘Unmanned aerial vehicles(UAVs),especially quadcopters,have become indispensable in numerous industrial and scientific applications due to their flexibility,lowcost,and capability to operate in dynamic environments.This paper presents a complete design and implementation of a compact autonomous quadcopter capable of trajectory tracking,object detection,precision landing,and real-time telemetry via long-range communication protocols.The system integrates an onboard flight controller running real-time sensor fusion algorithms,a vision-based detection system on a companion single-board computer,and a telemetry unit using Long Range(LoRa)communication.Extensive flight tests were conducted to validate the system’s stability,communication range,and autonomous capabilities.Potential applications in law enforcement,agriculture,search and rescue,and environmental monitoring are also discussed.
基金supported by the National Natural Science Foundation of China(Nos.52305314 and U21A20394)the Beijing Natural Science Foundation(Nos.7252285 and L246001)the National Key Research and Development Program of China(No.2023YFB4605800)。
文摘Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the complex interactions between inks and support baths.Here,we present an artificial intelligence(AI)-driven framework that interprets and predicts embedded printability using rheological data.Using a standardized workflow,we extracted 21 rheological descriptors and established 12 indicators to evaluate structural continuity and geometric fidelity.Interpretable machine learning models revealed that direction-dependent defects are governed by the synergistic interplay among ink yield stress,support bath zero shear viscosity,flow behavior index,and time constant.To enable the prediction of printability in a generalizable manner,we further developed a cascaded neural network,which achieved mean relative prediction errors below 15%across all indicators.Experimental validation using three-dimensional(3 D)-printed constructs and micro-computed tomography(μCT)reconstructions confirmed a strong correlation between predicted and actual fidelity.This work establishes a physics-informed,data-driven paradigm for decoding and optimizing embedded printing,offering broad applicability and providing a robust tool for the rapid pairing of suitable printable ink-support bath combinations.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R234)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Spam emails remain one of the most persistent threats to digital communication,necessitating effective detection solutions that safeguard both individuals and organisations.We propose a spam email classification frame-work that uses Bidirectional Encoder Representations from Transformers(BERT)for contextual feature extraction and a multiple-window Convolutional Neural Network(CNN)for classification.To identify semantic nuances in email content,BERT embeddings are used,and CNN filters extract discriminative n-gram patterns at various levels of detail,enabling accurate spam identification.The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails,achieving an accuracy of 98.69%,AUC of 0.9981,F1 Score of 0.9724,and MCC of 0.9639.With a medium kernel size of(6,9)and compact multi-window CNN architectures,it improves performance.Cross-validation illustrates stability and generalization across folds.By balancing high recall with minimal false positives,our method provides a reliable and scalable solution for current spam detection in advanced deep learning.By combining contextual embedding and a neural architecture,this study develops a security analysis method.
基金supported in part by the National Natural Science Foundation of China under Grants 62473176,62073155,62002137,62106088,and 62206113National Key Laboratory of Ship Structural Safety underGrant 450324300the Postgraduate Research&Practice Innovation Programof Jiangsu Province under Grant KYCX24_2642.
文摘Community detection is a fundamental problem in network analysis for identifying densely connected node clusters,with successful applications in diverse fields like social networks,recommendation systems,biology,and cyberattack detection.Overlapping community detection refers to the case of a node belonging to multiple communities simultaneously,which is a much more meaningful and challenging task.Graph representation learning with Evolutionary Computation has been studied well in overlapping community detection to deal with complex network structures and characteristics.However,most of them focus on searching the entire solution space,which can be inefficient and lead to inadequate results.To overcome the problem,a structural feature node extraction method is first proposed that can effectively map a network into a structural embedding space.Thus,nodes within the network are classified into hierarchical levels based on their structural feature strength,and only nodes with relatively high strength are considered in subsequent search steps to reduce the search space.Then,a maximal-clique representation method is employed on the given vertex set to identify overlapping nodes.A hybrid clique-based multi-objective evolutionary algorithmwith decomposition method is designed to address cliques and the remaining unexplored nodes separately.The number of communities generated with this allocation method is closer to the actual partition count with high division quality.Experimental results on nine usually used real-world networks,five synthetic networks,and two large-scale networks demonstrate the effectiveness of the proposed methodology in terms of community quality and algorithmic efficiency,compared to traditional,MOEA-based,and graph embedding-based community detection algorithms.
文摘Objective This review aimed to systematically evaluate the evidence on the effects of acupoint catgut embedding （ACE） therapy for patients with polycystic ovary syndrome （PCOS）. Methods Five databases （CBM, CNKI, Wanfang Database, VlP Database, and PubMed） were searched to identify relevant studies published before June 2027. The outcomes were resumption of menstruation and serum levels of testosterone （T）. The methodological quality of the included studies was judged using the Cochrane risk of bias tool. The overall level of evidence was judged by the Grading of Recommendations Assessment, Development, and Evaluation （GRADE） criteria. Results Twenty- five randomized controlled trials were included. ACE therapy significantly lowered serum T levels, and patients receiving ACE treatment reported resumption of menstruation. However, these results should be interpreted with caution due to a high risk of randomization and blinding bias, and likely publication bias. The level of evidence for resumption of menstruation and serum T levels was assessed as ＂low＂ and ＂low＂, respectively, using GRADE. Conclusion The current evidence on ACE therapy for PCOS is insufficient to draw firm conclusions due to the poor methodological quality. Future well- designed trials are needed to validate the therapeutic efficacy, safety, and mechanisms of ACE in patients with PCOS.
文摘Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01176).
文摘In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the efforts of researchers and security experts to protect information systems from these attacks,the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks.The latest remarkable achievements of large language models(LLMs)in NLP tasks have caught the attention of cybersecurity researchers to integrate thesemodels into security threat detection.Thesemodels offer high embedding capabilities,able to extract rich semantic representations and paving theway formore accurate and adaptive solutions.In this context,we propose a new approach for ransomware detection based on an ensemblemethod that leverages three distinctLLMembeddingmodels.This ensemble strategy takes advantage of the variety of embedding methods and the strengths of each model.In the proposed solution,each embedding model is associated with an independently trainedMLP classifier.The predictions obtained are then merged using a weighted voting technique,assigning each model an influence proportional to its performance.This approach makes it possible to exploit the complementarity of representations,improve detection accuracy and robustness,and offer a more reliable solution in the face of the growing diversity and complexity of modern ransomware.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
基金Pre-Research Project of the National Natural Science Foundation of China supported by Southeast University ( NoXJ0605227)
文摘A system of number recognition with a graphic user interface （GUI） is implemented on the embedded development platform by using the fuzzy pattern recognition method. An application interface （API） of uC/ OS-Ⅱ is used to implement the features of multi-task concurrency and the communications among tasks. Handwriting function is implemented by the improvement of the interface provided by the platform. Fuzzy pattern recognition technology based on fuzzy theory is used to analyze the input of handwriting. A primary system for testing is implemented. It can receive and analyze user inputs from both keyboard and touch-screen. The experimental results show that the embedded fuzzy recognition system which uses the technology which integrates two ways of fuzzy recognition can retain a high recognition rate and reduce hardware requirements.
基金Supported by National Administration of Traditional Chinese Medicine Project:Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (No. ZYYCXTD-C-202006)。
文摘OBJECTIVE: To evaluate the clinical effectiveness and safety of acupoint catgut embedding and acupuncture on simple obesity by Meta-analysis. METHODS: Studies on clinical randomized controlled trials of acupoint catgut embedding for simple obesity which were published from January 2015 to November 2020 were searched in Cochrane Central Register of Control Trials(Central), Pub Med, China Science and Technology Journal Database, China National Knowledge Infrastructure Database and Wanfang databases. And those that met the inclusion criteria were screened. Rev Man5.3 was used for Meta-analysis. The “Risk of Bias” tool was used to evaluate the quality of included studies. R studio software was used for the measurement of publication bias. RESULTS: A total of 33 studies were included for Metaanalysis, including 2685 patients with simple obesity. Meta-analysis results showed the comparison of effectiveness rate was relative risk(RR) = 1.12, 95%CI(1.08, 1.16), body mass index(BMI) was mean difference(MD) =-1.12, 95%CI(-2.09,-0.14), waist circumference was MD =-2.14, 95%CI(-4.22,-0.06), and body mass was MD =-2.36, 95%CI(-3.99,-0.73). On the basis of diet and exercise intervention, the effectiveness rate [RR = 1.12, 95%CI(1.05, 1.19)], BMI [MD =-0.88, 95%CI(-1.35,-0.40)], waist circumference [MD =-1.10, 95%CI(-4.27, 2.07)], and body mass [MD =-0.68, 95%CI(-2.90, 1.54)]. The risk of bias of included literatures was low. CONCLUSIONS: Acupoint catgut embedding therapy was slightly better than acupuncture therapy in most of the outcomes. Moreover, the treatment frequency of acupoint catgut embedding is less with larger stimulation intensity, which is more conducive to clinical promotion.
