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
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
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
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
Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,transl...Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,translation-based embedding models constitute a prominent approach in TKGC research.However,existing translation-based methods typically incorporate timestamps into entities or relations,rather than utilizing them independently.This practice fails to fully exploit the rich semantics inherent in temporal information,thereby weakening the expressive capability of models.To address this limitation,we propose embedding timestamps,like entities and relations,in one or more dedicated semantic spaces.After projecting all embeddings into a shared space,we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities.Our method elevates timestamps to the same representational significance as entities and relations.Based on this strategy,we introduce two novel translation-based embedding models:TE-TransR and TE-TransT.With the independent representation of timestamps,our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task,namely time prediction.To further bolster the precision and reliability of time prediction,we introduce a granular,time unit-based timestamp setting and a relation-specific evaluation protocol.Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks,with TE-TransR outperforming existing baselines in the time prediction task.展开更多
To address the challenges of complexity,power consumption,and cost constraints in traditional display driver integrated circuits(DDICs)caused by external NOR Flash and SRAM,this work proposes an embedded resistive ran...To address the challenges of complexity,power consumption,and cost constraints in traditional display driver integrated circuits(DDICs)caused by external NOR Flash and SRAM,this work proposes an embedded resistive random-access memory(RRAM)integration solution based on a 40 nm high-voltage CMOS logic platform.Targeting the yield fluctuations and stability challenges during RRAM mass production,systematic process optimizations are implemented to achieve synergistic improvements in RRAM performance and yield.Through modifications to the film sputtering and pre-deposition treatment,the withinwafer resistance uniformity(RSU)of the oxygen-deficient layer(ODL)thin film is improved from 11%to 8%,while inter-wafer process stability variation reduces from 23%to below 6%.Consequently,the yield of 8 Mb RRAM embedded mass production products increases from 87%to 98.5%.In terms of device performance,the RRAM demonstrates a fast 4.8 ns read speed,exceptional read disturb immunity of 3×10^(8) cycles at 95℃,10^(3) write/erase endurance cycles for the 1 Mb cells,and data retention of 12.5 years at 125℃.Post high-temperature operating life(HTOL)testing exhibits stable high/low resistance window.This study provides process optimization strategies and a reliability assurance framework for the mass production of highly integrated,low-power embedded RRAM display driver IC.展开更多
Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,ex...Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features,resulting in fragmented feature representations that compromise model stability and accuracy.To improve the extraction of valuable features,this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering(EDESC-IDS).Following the standard learning approach,continuous messages are encoded into two-dimensional data frames via a frame builder,which are then input into an extended convolutional autoencoder for extracting shallow features from high-dimensional data.On this basis,the dual constraints of these output features and the embedding clustering module facilitate end-to-end training of the EDESC-IDS in various attack scenarios.Extensive experimental results show that such a system exhibits significant detection performance on four types of attack datasets,including DoS,Gear,Fuzzy,and RPM,with precision,recall,and F1 scores consistently above 97.79%,while maintaining a false negative rate(FNR)and an error rate(ER)below 2.22%.展开更多
This paper presents an optimal operation method for embedded DC interconnections based on low-voltage AC/DC distribution areas(EDC-LVDA)under three-phase unbalanced compensation conditions.It can optimally determine t...This paper presents an optimal operation method for embedded DC interconnections based on low-voltage AC/DC distribution areas(EDC-LVDA)under three-phase unbalanced compensation conditions.It can optimally determine the transmission power of the DC and AC paths to simultaneously improve voltage quality and reduce losses.First,considering the embedded interconnected,unbalanced power structure of the distribution area,a power flow calculation method for EDC-LVDA that accounts for three-phase unbalanced compensation is introduced.This method accurately describes the power flow distribution characteristics under both AC and DC power allocation scenarios.Second,an optimization scheduling model for EDC-LVDA under three-phase unbalanced conditions is developed,incorporating network losses,voltage quality,DC link losses,and unbalance levels.The proposed model employs an improved particle swarm optimization(IPSO)two-layer algorithm to autonomously select different power allocation coefficients for the DC link and AC section under various operating conditions.This enables embedded economic optimization scheduling while maintaining compensation for unbalanced conditions.Finally,a case study based on the IEEE 13-node system for EDC-LVDA is conducted and tested.The results show that the proposed optimal operation method achieves a 100%voltage compliance rate and reduces network losses by 13.8%,while ensuring three-phase power balance compensation.This provides a practical solution for the modernization and upgrading of low-voltage power grids.展开更多
The metal‐reduction‐induced dechlorination coupling(MR-DC)strategy enables the first successful synthesis of an all‐inorganic crosslinked phosphazene network(aPN)from hexachlorocyclotriphosphazene(HCCP)under mild r...The metal‐reduction‐induced dechlorination coupling(MR-DC)strategy enables the first successful synthesis of an all‐inorganic crosslinked phosphazene network(aPN)from hexachlorocyclotriphosphazene(HCCP)under mild reaction conditions.Using Cu as a model,the resulting Cu-aPN(copper‐embedded all‐inorganic phosphazene network)retains the intrinsic N_(3)P_(3)backbone and exhibits an amorphous structure where Cu species are uniformly anchored at dense P/N coordination sites of the network.Time of flight secondary ion mass spectrometry(TOF‐SIMS)and X‐ray diffraction(XRD)reveal a gradual CuCl‐to‐CuO phase conversion during ammonia treatment,which effectively ensures the structural stability of the phosphazene framework.In 1 M KOH,Cu-aPN delivers an overpotential of 280 mV at 10 mA cm^(−2)and a Tafel slope of 48 mV dec^(−1),markedly outperforming Ga-aPN.In situ Raman and density functional theory(DFT)analyses indicate stronger Cu-P/N coordination coupling that lowers the*OH formation barrier(0.39 vs.0.88 eV for Ga).This MR-DC route furnishes a general and versatile pathway for constructing metal‐embedded all‐inorganic phosphazene frameworks with tunable coordination environments for advanced electrocatalytic applications.展开更多
Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems.Artificial intelligence enables real-time sensing,decision-making,and control on embedded platforms with impro...Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems.Artificial intelligence enables real-time sensing,decision-making,and control on embedded platforms with improved efficiency.This study presents the design and implementation of an autonomous radio-controlled(RC)vehicle prototype capable of lane line detection,obstacle avoidance,and navigation through dynamic path planning.The system integrates image processing and ultrasonic sensing,utilizing Raspberry Pi for vision-based tasks and ArduinoNano for real-time control.Lane line detection is achieved through conventional image processing techniques,providing the basis for local path generation,while traffic sign classification employs a You Only Look Once(YOLO)model optimized with TensorFlow Lite to support navigation decisions.Images captured by the onboard camera are processed on the Raspberry Pi to extract lane geometry and calculate steering angles,enabling the vehicle to follow the planned path.In addition,ultrasonic sensors placed in three directions at the front of the vehicle detect obstacles and allow real-time path adjustment for safe navigation.Experimental results demonstrate stable performance under controlled conditions,highlighting the system’s potential for scalable autonomous driving applications.This work confirms that deep learning methods can be efficiently deployed on low-power embedded systems,offering a practical framework for navigation,path planning,and intelligent transportation research.展开更多
Combining transparent embedding with sectioning is likely to be the future direction for tissue clearing and 3-dimensional(3D)imaging.A newly published transparent embedding system,TESOS(Transparent Embedding Solvent ...Combining transparent embedding with sectioning is likely to be the future direction for tissue clearing and 3-dimensional(3D)imaging.A newly published transparent embedding system,TESOS(Transparent Embedding Solvent System),ensures consistent submicron resolution imaging throughout the entire sample,and can be compatible with different microscopy systems.This method shows great potential in connectome mapping,and might be an optimal option for future 3D multiplex immunofluorescence and RNA in situ hybridization imaging.Additional efforts would be needed to innovate labeling,imaging,and data processing strategies to fully utilize the potential of transparent embedding systems in highresolution imaging of large-scale samples.展开更多
Adaptive optics(AO)has significantly advanced high-resolution solar observations by mitigating atmospheric turbulence.However,traditional post-focal AO systems suffer from external configurations that introduce excess...Adaptive optics(AO)has significantly advanced high-resolution solar observations by mitigating atmospheric turbulence.However,traditional post-focal AO systems suffer from external configurations that introduce excessive optical surfaces,reduced light throughput,and instrumental polarization.To address these limitations,we propose an embedded solar adaptive optics telescope(ESAOT)that intrinsically incorporates the solar AO(SAO)subsystem within the telescope's optical train,featuring a co-designed correction chain with a single Hartmann-Shack full-wavefront sensor(HS f-WFS)and a deformable secondary mirror(DSM).The HS f-WFS uses temporal-spatial hybrid sampling technique to simultane-ously resolve tip-tilt and high-order aberrations,while the DSM performs real-time compensation through adaptive modal optimization.This unified architecture achieves symmetrical polarization suppression and high system throughput by min-imizing optical surfaces.A 600 mm ESAOT prototype incorporating a 12×12 micro-lens array HS f-WFS and 61-actuator piezoelectric DSM has been developed and successfully conducted on-sky photospheric observations.Validations in-cluding turbulence simulations,optical bench testing,and practical observations at the Lijiang observatory collectively confirm the system's capability to maintain aboutλ/10 wavefront error during active region tracking.This architectural breakthrough of the ESAOT addresses long-standing SAO integration challenges in solar astronomy and provides scala-bility analyses confirming direct applicability to the existing and future large solar observation facilities.展开更多
Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algeb...Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algebraic subsets of G forms a family of closed sets for another T_(1)topology Z_(G) on G called the Zarski topology.A subgroup H of G is said to be Markov(resp.Zarski)embedded if the equality M_(G|H)=M_(H)(resp.Z_(G|H)=Z_(H))holds.I's proved that an abirary subgroup of a free group is both Zariski and Markov embedded in it.展开更多
Digital watermarking must balance imperceptibility,robustness,complexity,and security.To address the challenge of computational efficiency in trellis-based informed embedding,we propose a modified watermarking framewo...Digital watermarking must balance imperceptibility,robustness,complexity,and security.To address the challenge of computational efficiency in trellis-based informed embedding,we propose a modified watermarking framework that integrates fuzzy c-means(FCM)clustering into the generation off block codewords for labeling trellis arcs.The system incorporates a parallel trellis structure,controllable embedding parameters,and a novel informed embedding algorithm with reduced complexity.Two types of embedding schemes—memoryless and memory-based—are designed to flexibly trade-off between imperceptibility and robustness.Experimental results demonstrate that the proposed method outperforms existing approaches in bit error rate(BER)and computational complexity under various attacks,including additive noise,filtering,JPEG compression,cropping,and rotation.The integration of FCM enhances robustness by increasing the codeword distance,while preserving perceptual quality.Overall,the proposed framework is suitable for real-time and secure watermarking applications.展开更多
A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehens...A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.展开更多
In shale gas reservoir stimulation,proppants are essential for sustaining fracture conductivity.However,increasing closing stress causes proppants to embed into the rock matrix,leading to a progressive decline in frac...In shale gas reservoir stimulation,proppants are essential for sustaining fracture conductivity.However,increasing closing stress causes proppants to embed into the rock matrix,leading to a progressive decline in fracture permeability and conductivity.Furthermore,rock creep contributes to long-term reductions in fracture performance.To elucidate the combined effects of proppant embedding and rock creep on sustained conductivity,this study conducted controlled experiments examining conductivity decay in propped fractures under varying closing stresses,explicitly accounting for both mechanisms.An embedded discrete fracture model was developed to simulate reservoir production under different conductivity decay scenarios,while evaluating the influence of proppant parameters on fracture performance.The results demonstrate that fracture conductivity diminishes rapidly with increasing stress,yet at 50 MPa,the decline becomes less pronounced.Simulated production profiles show strong agreement with actual gas well data,confirming the model’s accuracy and predictive capability.These findings suggest that employing a high proppant concentration with smaller particle size(5 kg/m^(2),70/140 mesh)is effective for maintaining long-term fracture conductivity and enhancing shale gas recovery.This study provides a rigorous framework for optimizing proppant selection and designing stimulation strategies that maximize reservoir performance over time.展开更多
Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to ...Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.展开更多
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.
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 Natural Science Foundation of China under Grant No.72293575.
文摘Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,translation-based embedding models constitute a prominent approach in TKGC research.However,existing translation-based methods typically incorporate timestamps into entities or relations,rather than utilizing them independently.This practice fails to fully exploit the rich semantics inherent in temporal information,thereby weakening the expressive capability of models.To address this limitation,we propose embedding timestamps,like entities and relations,in one or more dedicated semantic spaces.After projecting all embeddings into a shared space,we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities.Our method elevates timestamps to the same representational significance as entities and relations.Based on this strategy,we introduce two novel translation-based embedding models:TE-TransR and TE-TransT.With the independent representation of timestamps,our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task,namely time prediction.To further bolster the precision and reliability of time prediction,we introduce a granular,time unit-based timestamp setting and a relation-specific evaluation protocol.Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks,with TE-TransR outperforming existing baselines in the time prediction task.
文摘To address the challenges of complexity,power consumption,and cost constraints in traditional display driver integrated circuits(DDICs)caused by external NOR Flash and SRAM,this work proposes an embedded resistive random-access memory(RRAM)integration solution based on a 40 nm high-voltage CMOS logic platform.Targeting the yield fluctuations and stability challenges during RRAM mass production,systematic process optimizations are implemented to achieve synergistic improvements in RRAM performance and yield.Through modifications to the film sputtering and pre-deposition treatment,the withinwafer resistance uniformity(RSU)of the oxygen-deficient layer(ODL)thin film is improved from 11%to 8%,while inter-wafer process stability variation reduces from 23%to below 6%.Consequently,the yield of 8 Mb RRAM embedded mass production products increases from 87%to 98.5%.In terms of device performance,the RRAM demonstrates a fast 4.8 ns read speed,exceptional read disturb immunity of 3×10^(8) cycles at 95℃,10^(3) write/erase endurance cycles for the 1 Mb cells,and data retention of 12.5 years at 125℃.Post high-temperature operating life(HTOL)testing exhibits stable high/low resistance window.This study provides process optimization strategies and a reliability assurance framework for the mass production of highly integrated,low-power embedded RRAM display driver IC.
基金supported by the National Natural Science Foundation of China(Grant No.62172292).
文摘Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features,resulting in fragmented feature representations that compromise model stability and accuracy.To improve the extraction of valuable features,this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering(EDESC-IDS).Following the standard learning approach,continuous messages are encoded into two-dimensional data frames via a frame builder,which are then input into an extended convolutional autoencoder for extracting shallow features from high-dimensional data.On this basis,the dual constraints of these output features and the embedding clustering module facilitate end-to-end training of the EDESC-IDS in various attack scenarios.Extensive experimental results show that such a system exhibits significant detection performance on four types of attack datasets,including DoS,Gear,Fuzzy,and RPM,with precision,recall,and F1 scores consistently above 97.79%,while maintaining a false negative rate(FNR)and an error rate(ER)below 2.22%.
基金supported by the key technology project of China Southern Power Grid Corporation(GZKJXM20220041)partly by the National Key Research and Development Plan(2022YFE0205300).
文摘This paper presents an optimal operation method for embedded DC interconnections based on low-voltage AC/DC distribution areas(EDC-LVDA)under three-phase unbalanced compensation conditions.It can optimally determine the transmission power of the DC and AC paths to simultaneously improve voltage quality and reduce losses.First,considering the embedded interconnected,unbalanced power structure of the distribution area,a power flow calculation method for EDC-LVDA that accounts for three-phase unbalanced compensation is introduced.This method accurately describes the power flow distribution characteristics under both AC and DC power allocation scenarios.Second,an optimization scheduling model for EDC-LVDA under three-phase unbalanced conditions is developed,incorporating network losses,voltage quality,DC link losses,and unbalance levels.The proposed model employs an improved particle swarm optimization(IPSO)two-layer algorithm to autonomously select different power allocation coefficients for the DC link and AC section under various operating conditions.This enables embedded economic optimization scheduling while maintaining compensation for unbalanced conditions.Finally,a case study based on the IEEE 13-node system for EDC-LVDA is conducted and tested.The results show that the proposed optimal operation method achieves a 100%voltage compliance rate and reduces network losses by 13.8%,while ensuring three-phase power balance compensation.This provides a practical solution for the modernization and upgrading of low-voltage power grids.
基金financially supported by the National Natural Science Foundation of China(Grant 22205173)the Innovation Capability Support Program of Shaanxi(Grant 2024CX‐GXPT‐12)。
文摘The metal‐reduction‐induced dechlorination coupling(MR-DC)strategy enables the first successful synthesis of an all‐inorganic crosslinked phosphazene network(aPN)from hexachlorocyclotriphosphazene(HCCP)under mild reaction conditions.Using Cu as a model,the resulting Cu-aPN(copper‐embedded all‐inorganic phosphazene network)retains the intrinsic N_(3)P_(3)backbone and exhibits an amorphous structure where Cu species are uniformly anchored at dense P/N coordination sites of the network.Time of flight secondary ion mass spectrometry(TOF‐SIMS)and X‐ray diffraction(XRD)reveal a gradual CuCl‐to‐CuO phase conversion during ammonia treatment,which effectively ensures the structural stability of the phosphazene framework.In 1 M KOH,Cu-aPN delivers an overpotential of 280 mV at 10 mA cm^(−2)and a Tafel slope of 48 mV dec^(−1),markedly outperforming Ga-aPN.In situ Raman and density functional theory(DFT)analyses indicate stronger Cu-P/N coordination coupling that lowers the*OH formation barrier(0.39 vs.0.88 eV for Ga).This MR-DC route furnishes a general and versatile pathway for constructing metal‐embedded all‐inorganic phosphazene frameworks with tunable coordination environments for advanced electrocatalytic applications.
文摘Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems.Artificial intelligence enables real-time sensing,decision-making,and control on embedded platforms with improved efficiency.This study presents the design and implementation of an autonomous radio-controlled(RC)vehicle prototype capable of lane line detection,obstacle avoidance,and navigation through dynamic path planning.The system integrates image processing and ultrasonic sensing,utilizing Raspberry Pi for vision-based tasks and ArduinoNano for real-time control.Lane line detection is achieved through conventional image processing techniques,providing the basis for local path generation,while traffic sign classification employs a You Only Look Once(YOLO)model optimized with TensorFlow Lite to support navigation decisions.Images captured by the onboard camera are processed on the Raspberry Pi to extract lane geometry and calculate steering angles,enabling the vehicle to follow the planned path.In addition,ultrasonic sensors placed in three directions at the front of the vehicle detect obstacles and allow real-time path adjustment for safe navigation.Experimental results demonstrate stable performance under controlled conditions,highlighting the system’s potential for scalable autonomous driving applications.This work confirms that deep learning methods can be efficiently deployed on low-power embedded systems,offering a practical framework for navigation,path planning,and intelligent transportation research.
基金supported by a startup fund from the Chinese Institute for Brain Research,National Natural Science Foundation of China(82101059)research funding from West China School/Hospital of Stomatology Sichuan University(RCDWJS2022-11).
文摘Combining transparent embedding with sectioning is likely to be the future direction for tissue clearing and 3-dimensional(3D)imaging.A newly published transparent embedding system,TESOS(Transparent Embedding Solvent System),ensures consistent submicron resolution imaging throughout the entire sample,and can be compatible with different microscopy systems.This method shows great potential in connectome mapping,and might be an optimal option for future 3D multiplex immunofluorescence and RNA in situ hybridization imaging.Additional efforts would be needed to innovate labeling,imaging,and data processing strategies to fully utilize the potential of transparent embedding systems in highresolution imaging of large-scale samples.
基金support from the National Science Foundation of China(NSFC)(Grants No.12293031 and No.61905252)the National Science Foundation for Distinguished Young Scholars(Grant No.12022308)the National Key R&D Program of China(Grants No.2021YFC2202200 and No.2021YFC2202204).
文摘Adaptive optics(AO)has significantly advanced high-resolution solar observations by mitigating atmospheric turbulence.However,traditional post-focal AO systems suffer from external configurations that introduce excessive optical surfaces,reduced light throughput,and instrumental polarization.To address these limitations,we propose an embedded solar adaptive optics telescope(ESAOT)that intrinsically incorporates the solar AO(SAO)subsystem within the telescope's optical train,featuring a co-designed correction chain with a single Hartmann-Shack full-wavefront sensor(HS f-WFS)and a deformable secondary mirror(DSM).The HS f-WFS uses temporal-spatial hybrid sampling technique to simultane-ously resolve tip-tilt and high-order aberrations,while the DSM performs real-time compensation through adaptive modal optimization.This unified architecture achieves symmetrical polarization suppression and high system throughput by min-imizing optical surfaces.A 600 mm ESAOT prototype incorporating a 12×12 micro-lens array HS f-WFS and 61-actuator piezoelectric DSM has been developed and successfully conducted on-sky photospheric observations.Validations in-cluding turbulence simulations,optical bench testing,and practical observations at the Lijiang observatory collectively confirm the system's capability to maintain aboutλ/10 wavefront error during active region tracking.This architectural breakthrough of the ESAOT addresses long-standing SAO integration challenges in solar astronomy and provides scala-bility analyses confirming direct applicability to the existing and future large solar observation facilities.
基金Supported by the Grant-in-Aid for Scientific Research(C)by the Japan Society for the Promotion of Science(20K03615)。
文摘Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algebraic subsets of G forms a family of closed sets for another T_(1)topology Z_(G) on G called the Zarski topology.A subgroup H of G is said to be Markov(resp.Zarski)embedded if the equality M_(G|H)=M_(H)(resp.Z_(G|H)=Z_(H))holds.I's proved that an abirary subgroup of a free group is both Zariski and Markov embedded in it.
基金funded by the National Science and Technology Council,Taiwan,under grant number NSTC 114-2221-E-167-005-MY3,and NSTC 113-2221-E-167-006-.
文摘Digital watermarking must balance imperceptibility,robustness,complexity,and security.To address the challenge of computational efficiency in trellis-based informed embedding,we propose a modified watermarking framework that integrates fuzzy c-means(FCM)clustering into the generation off block codewords for labeling trellis arcs.The system incorporates a parallel trellis structure,controllable embedding parameters,and a novel informed embedding algorithm with reduced complexity.Two types of embedding schemes—memoryless and memory-based—are designed to flexibly trade-off between imperceptibility and robustness.Experimental results demonstrate that the proposed method outperforms existing approaches in bit error rate(BER)and computational complexity under various attacks,including additive noise,filtering,JPEG compression,cropping,and rotation.The integration of FCM enhances robustness by increasing the codeword distance,while preserving perceptual quality.Overall,the proposed framework is suitable for real-time and secure watermarking applications.
文摘A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.
基金supported by the National Natural Science Foundation of China(Nos.52204051,52304046).
文摘In shale gas reservoir stimulation,proppants are essential for sustaining fracture conductivity.However,increasing closing stress causes proppants to embed into the rock matrix,leading to a progressive decline in fracture permeability and conductivity.Furthermore,rock creep contributes to long-term reductions in fracture performance.To elucidate the combined effects of proppant embedding and rock creep on sustained conductivity,this study conducted controlled experiments examining conductivity decay in propped fractures under varying closing stresses,explicitly accounting for both mechanisms.An embedded discrete fracture model was developed to simulate reservoir production under different conductivity decay scenarios,while evaluating the influence of proppant parameters on fracture performance.The results demonstrate that fracture conductivity diminishes rapidly with increasing stress,yet at 50 MPa,the decline becomes less pronounced.Simulated production profiles show strong agreement with actual gas well data,confirming the model’s accuracy and predictive capability.These findings suggest that employing a high proppant concentration with smaller particle size(5 kg/m^(2),70/140 mesh)is effective for maintaining long-term fracture conductivity and enhancing shale gas recovery.This study provides a rigorous framework for optimizing proppant selection and designing stimulation strategies that maximize reservoir performance over time.
基金supported in part by the National Science and Technology Major Project under(Grant 2022ZD0116100)in part by the National Natural Science Foundation Key Project under(Grant 62436006)+4 种基金in part by the National Natural Science Foundation Youth Fund under(Grant 62406257)in part by the Xizang Autonomous Region Natural Science Foundation General Project under(Grant XZ202401ZR0031)in part by the National Natural Science Foundation of China under(Grant 62276055)in part by the Sichuan Science and Technology Program under(Grant 23ZDYF0755)in part by the Xizang University‘High-Level Talent Training Program’Project under(Grant 2022-GSP-S098).
文摘Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.
