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 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
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
Reversible data embedding is becoming a very important issue in securing images transmitted over the Internet, especially in dealing with sensitive images such as those created for military data and medical data. Base...Reversible data embedding is becoming a very important issue in securing images transmitted over the Internet, especially in dealing with sensitive images such as those created for military data and medical data. Based on the relationships between pixels and their neighbors, we propose a reversible data embedding scheme to embed hidden messages into an original image. In our proposed scheme, a two-layer data embedding approach is used for our proposed data embedding phase. Layer-1 embedding is used to hide secret data. Layer-2 embedding, which is an embedding variant of the proposed layer-1 embedding, is used to hide side information, such as the parameters required to restore the marked image. In our layer-1 embedding, the value of an embedded pixel is determined according to a predetermined threshold and the relationship between the pixel and its neighbors. In our layer-2 embedding, the similar data embedding concept is expanded to the block-based. Experimental results provide supportive data to show that the proposed scheme can provide greater hiding capacity while preserving the image quality of a marked image in comparison with previous work.展开更多
In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity mea...In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the ...In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the potential to positively influence mental health by providing monitoring,insights,and inter-ventions.However,they also come with challenges that need to be addressed.Understanding the primary purpose for which individuals use these smart tech-nologies is essential to tailoring them to specific mental health needs and prefe-rences.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
This study investigated the role of intentional self-regulation and the moderating role of peer relationship in the relationship between teacher-student relationship and learning engagement.The study sample comprised ...This study investigated the role of intentional self-regulation and the moderating role of peer relationship in the relationship between teacher-student relationship and learning engagement.The study sample comprised 540 Chinese senior secondary school students between the ages of 15–18(51.67%boys;Mage=16.56 years;SDage=0.90).They completed surveys on the Teacher-Student Relationship Scale,the Selection,Optimization,and Compensation(SOC)Scale,the Peer Relationship Scale for Children and Adolescents,and the Learning Engagement Scale.The results following regression analysis showed that teacher-student relationship predicted higher learning engagement among senior secondary school students.Intentional self-regulation partially mediated the link between teacher-student relationship and learning engagement for higher learning engagement.Peer relationship moderated the relationships between teacher-student relationship and learning engagement and moderated the relationship between teacher-student relationship and intentional self-regulation for higher learning engagement.Thesefindings imply learning engagement can be enhanced by optimizing teacher-student relationship and strengthening intentional self-regulation interventions.展开更多
The relationship between ecosystem services(ES)and human well-being(HWB)is fundamental to the science and practice of sustainability.However,studies have shown conflicting results,which has been attributed to the infl...The relationship between ecosystem services(ES)and human well-being(HWB)is fundamental to the science and practice of sustainability.However,studies have shown conflicting results,which has been attributed to the influences of indicators,contexts,and scales.Yet,another potential factor,which has been overlooked,may be the mixed use of spatial and temporal approaches.Using twelve ES and seven well-being indicators and multiple statistical methods,we quantified and compared the spatial and temporal ES–HWB relationships for Inner Mongolia,China.The spatial and temporal relationships differed in both correlation direction and strength.Most relationships of economic and employment-related indicators with food provisioning and supporting services were temporally positive but spatially nonsignificant or negative.Some relationships of economic and employmentrelated indicators with water retention,sandstorm prevention,and wind erosion were temporally negative but spatially complex.However,the spatial and temporal ES–HWB relationships could also be similar in some cases.We conclude that although both the spatial and temporal approaches have merits,space generally cannot substitute for time in the study of ES–HWB relationship.Our study helps reconcile the seemingly conflicting findings in the literature,and suggests that future studies should explicitly distinguish between the spatial and temporal ES–HWB relationships.展开更多
This study examined the impact of teacher-student relationship quality on students’risk of bullying victimiza-tion and the mediating roles of student-student relationships and student engagement in this relationship....This study examined the impact of teacher-student relationship quality on students’risk of bullying victimiza-tion and the mediating roles of student-student relationships and student engagement in this relationship.A total of 656 Chinese junior high school students(females=361,mean age=13.75,SD=0.98)completed validated measures of teacher-student relationship quality,student-student relationship quality,student engagement,and bullying victimization.Regression analysis results indicated that higher teacher-student relationship quality predicted a lower risk of student bullying victimization.Serial mediating effect testing of the student-student relationship quality and student engagement revealed that these factors fully mediated the relationship between teacher-student relationship quality and bullying victimization,resulting in a lower risk of bullying victimization.The results showed that student-student relationship quality had a more substantial mediating effect than student engagement.Thefindings support the Socio-Ecological Framework,suggesting that within the Microsystem,interactions between individuals and their immediate environments significantly impact their behavior.Specifically,thesefindings suggest that good teacher-student relationships can enhance the quality of student-student relationships and student engagement,thereby preventing and reducing the occurrence of bullying victimization.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
Since China’s reform and development,under the influence of the global religious and cultural boom,it has also focused on studying Islamic culture and related fields,resulting in many important representative works.T...Since China’s reform and development,under the influence of the global religious and cultural boom,it has also focused on studying Islamic culture and related fields,resulting in many important representative works.The main characteristic of writing books in this area is to use popular knowledge materials that are suitable for various sectors of society.There are also works exploring the historical evolution of Islamic culture in China,which involve the relationship between Islamic culture and traditional Chinese culture.Before the 1980s,there were no significant achievements in publishing papers.However,since the 1990s,there has been a noticeable increase in papers related to Islamic culture and related fields,with new advances in research areas and methods.There are both articles that explore the attributes and characteristics of Islamic culture from the perspectives of world history and culture,as well as many articles that explore various specific aspects of Islamic culture from the perspective of specific topics.In this article,the author collects academic achievements in the study of Islamic culture in China and introduces them to the academic community,letting them know about China's research on world Islamic culture.展开更多
Soybean is widely used in diets,and numerous reports have highlighted its antioxidant properties.However,constructing a methodology for rapid identifying and predicting a series of antioxidant active ingredients in So...Soybean is widely used in diets,and numerous reports have highlighted its antioxidant properties.However,constructing a methodology for rapid identifying and predicting a series of antioxidant active ingredients in Soybean presents certain challenges.Therefore,we introduced the spectrum-effect relationship-ingredient knockout identification technique to identify a series of antioxidant active ingredients in soybean.By combining untargeted metabolomics with network pharmacology,we predicted the antioxidant active ingredients and their target sites.We successfully identified 4 antioxidant active compounds(daidzein,genistein,daidzein,and glycitin)and 10 corresponding antioxidant targets(epidermal growth factor receptor(EGFR),estrogen receptor 1(ESR1),steroid receptor coactivator(SRC),tumor necrosis factor(TNF),kinase insert domain receptor(KDR),AKT serine/threonine kinase 1(AKT1),growth factor receptor bound protein 2(GRB2),signal transducer and activator of transcription1(STAT1),mitogen-activated protein kinase 8(MAPK8),B-cell lymphoma-2(BCL2))by our analysis.The validation results from cell experiments revealed that glycitin exhibited the best antioxidant activity and significantly influenced the expression of EGFR and the proteins associated with nuclear factor erythroid 2-related factor 2/NAD(P)H quinone dehydrogenase 1(NRF2/NQO1)signaling pathways.These findings were consistent with the predicted outcomes and were further confirmed in a zebrafish model.It suggests that glycitin may exert antioxidant effects by regulating the expression of EGFR,NRF2,and NQO1 proteins.The results demonstrate that a rapid analytical method for determining antioxidant activity was established.展开更多
BACKGROUND Hyperglycemia secondary to diabetes can lead to organ failure unless properly controlled,and it is associated with numerous health issues,particularly metabolic problems.Breast cancer is one of the most com...BACKGROUND Hyperglycemia secondary to diabetes can lead to organ failure unless properly controlled,and it is associated with numerous health issues,particularly metabolic problems.Breast cancer is one of the most common malignancies in females worldwide and represents a major health concern due to its morbidity and mortality.The progression of the disease and the response to treatment depend on numerous variables,including both tumor biology and patient-related factors.Key prognostic indicators include age,tumor size,lymph node involvement,histological type,hormone receptor status,and expression of biomarkers such as HER2.AIM To investigate the relationship between preoperative blood glucose levels and cancer prognostic factors in patients diagnosed with breast cancer.METHODS In this study patients diagnosed with breast cancer were classified into two groups:Those with blood glucose levels<125 mg/dL and those with levels>125 mg/dL.Estrogen and progesterone receptor positivity,Ki-67 percentage,Cerb2 positivity,number of positive lymph nodes,and presence of lymphovascular invasion were examined histopathologically.RESULTS The study included 246 patients with breast cancer:196 were in the normal glucose group and 50 were in the elevated glucose group.In the normal glucose group,68%of the tumors were left-sided and 32%were right-sided,while all tumors in the elevated glucose group were left-sided.This difference was statistically significant(P<0.001).Tumor size,Ki-67,and the number of positive lymph nodes were not associated with blood glucose in patients with breast cancer(P>0.05 for all).However,blood glucose was correlated with age in this population.Correlation analyses revealed that blood glucose was positively correlated with age(r=0.23,P<0.001).Additionally,Ki-67 was correlated with tumor size(r=0.14,P=0.03)and age(r=-0.18,P=0.005).CONCLUSION Our study highlighted the complex interplay between metabolic factors,such as fasting glucose levels,and breast cancer characteristics.Elevated glucose levels were associated with older age.The significant association between tumor laterality and glucose levels underscores the need for further investigation into the collective influence of metabolic and anatomical factors on breast cancer development and progression.展开更多
Background With the rapid growth of internet usage,adolescent cyberbullying has become a pressing issue.This study examines the longitudinal impact of leisure-time physical activity and peer relationships on cyberbull...Background With the rapid growth of internet usage,adolescent cyberbullying has become a pressing issue.This study examines the longitudinal impact of leisure-time physical activity and peer relationships on cyberbullying over a one-year period,drawing on the Stage-Environment Fit Theory and the Interpersonal Relationship Theory.Methods A three-wave longitudinal study was conducted over one year,involving 896 middle school students from five schools in Sichuan,Jiangsu,and Guangdong,China,selected to ensure regional diversity.Participants were recruited using stratified random sampling,and data were collected at four-month intervals.Leisure-time physical activity,peer relationships,and cyberbullying behaviors were assessed using validated scales.Data were analyzed using latent growth modeling(LGM)and structural equation modeling(SEM)to examine longitudinal effects and mediation relationships.Results(1)Leisure-time physical activity,peer relationships,and cyberbullying behaviors remained relatively stable over time.(2)Both leisure-time physical activity and peer relationships significantly reduced future cyberbullying incidents,with physical activity also enhancing subsequent peer relationships.(3)Peer relationships partially mediated the effect of leisure-time physical activity on cyberbullying,indicating that improved peer interactions contributed to a reduction in cyberbullying behaviors.Conclusion This study found that leisure-time sports activities and peer relationships are important factors affecting cyberbullying,and peer relationships play a partial mediating role in it.The results provide empirical support for understanding the formation mechanism and influencing factors of cyberbullying.展开更多
Quantitative structure-retention relationship(QSRR)is an important tool in chromatography.QSRR examines the correlation between molecular structures and their retention behaviors during chromatographic separation.This...Quantitative structure-retention relationship(QSRR)is an important tool in chromatography.QSRR examines the correlation between molecular structures and their retention behaviors during chromatographic separation.This approach involves developing models for predicting the retention time(RT)of analytes,thereby accelerating method development and facilitating compound identification.In addition,QSRR can be used to study compound retention mechanisms and support drug screening efforts.This review provides a comprehensive analysis of QSRR workflows and applications,with a special focus on the role of artificial intelligence-an area not thoroughly explored in previous reviews.Moreover,we discuss current limitations in RT prediction and propose promising solutions.Overall,this review offers a fresh perspective on future QSRR research,encouraging the development of innovative strategies that enable the diverse applications of QSRR models in chromatographic analysis.展开更多
This paper explored how the work hardening process enhances the yield strength(YS)of magnesium alloy and established a novel quantitative relationship between microstructure and YS.The YS of an extruded Mg–Nd–Zn–Zr...This paper explored how the work hardening process enhances the yield strength(YS)of magnesium alloy and established a novel quantitative relationship between microstructure and YS.The YS of an extruded Mg–Nd–Zn–Zr alloy was nearly tripled by rotary swaging at 200℃,attributing to dislocation accumulation,grain fragmentation,and basal texture formation.A modified Hall–Petch relationship(GM–HP)was established by introducing the equivalent orientation factor and dislocation density to accurately quantify the YS and the contribution of each strengthening mechanism.The contributions of dislocation hardening and grain boundary strengthening were multiplied by the strong basal texture.This paper offered valuable insights and guidance for designing and optimizing high-strength wrought magnesium alloys and their plastic processing methods.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
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 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supported by the National Science Council Foundation under Grant No.NSC 98-2410-H-126-007-MY3
文摘Reversible data embedding is becoming a very important issue in securing images transmitted over the Internet, especially in dealing with sensitive images such as those created for military data and medical data. Based on the relationships between pixels and their neighbors, we propose a reversible data embedding scheme to embed hidden messages into an original image. In our proposed scheme, a two-layer data embedding approach is used for our proposed data embedding phase. Layer-1 embedding is used to hide secret data. Layer-2 embedding, which is an embedding variant of the proposed layer-1 embedding, is used to hide side information, such as the parameters required to restore the marked image. In our layer-1 embedding, the value of an embedded pixel is determined according to a predetermined threshold and the relationship between the pixel and its neighbors. In our layer-2 embedding, the similar data embedding concept is expanded to the block-based. Experimental results provide supportive data to show that the proposed scheme can provide greater hiding capacity while preserving the image quality of a marked image in comparison with previous work.
文摘In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
文摘In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the potential to positively influence mental health by providing monitoring,insights,and inter-ventions.However,they also come with challenges that need to be addressed.Understanding the primary purpose for which individuals use these smart tech-nologies is essential to tailoring them to specific mental health needs and prefe-rences.
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.
文摘This study investigated the role of intentional self-regulation and the moderating role of peer relationship in the relationship between teacher-student relationship and learning engagement.The study sample comprised 540 Chinese senior secondary school students between the ages of 15–18(51.67%boys;Mage=16.56 years;SDage=0.90).They completed surveys on the Teacher-Student Relationship Scale,the Selection,Optimization,and Compensation(SOC)Scale,the Peer Relationship Scale for Children and Adolescents,and the Learning Engagement Scale.The results following regression analysis showed that teacher-student relationship predicted higher learning engagement among senior secondary school students.Intentional self-regulation partially mediated the link between teacher-student relationship and learning engagement for higher learning engagement.Peer relationship moderated the relationships between teacher-student relationship and learning engagement and moderated the relationship between teacher-student relationship and intentional self-regulation for higher learning engagement.Thesefindings imply learning engagement can be enhanced by optimizing teacher-student relationship and strengthening intentional self-regulation interventions.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.B240201068)the National Natural Science Foundation of China(Grant No.42361144861)the National Basic Research Program of China(Grant No.2014CB954303).
文摘The relationship between ecosystem services(ES)and human well-being(HWB)is fundamental to the science and practice of sustainability.However,studies have shown conflicting results,which has been attributed to the influences of indicators,contexts,and scales.Yet,another potential factor,which has been overlooked,may be the mixed use of spatial and temporal approaches.Using twelve ES and seven well-being indicators and multiple statistical methods,we quantified and compared the spatial and temporal ES–HWB relationships for Inner Mongolia,China.The spatial and temporal relationships differed in both correlation direction and strength.Most relationships of economic and employment-related indicators with food provisioning and supporting services were temporally positive but spatially nonsignificant or negative.Some relationships of economic and employmentrelated indicators with water retention,sandstorm prevention,and wind erosion were temporally negative but spatially complex.However,the spatial and temporal ES–HWB relationships could also be similar in some cases.We conclude that although both the spatial and temporal approaches have merits,space generally cannot substitute for time in the study of ES–HWB relationship.Our study helps reconcile the seemingly conflicting findings in the literature,and suggests that future studies should explicitly distinguish between the spatial and temporal ES–HWB relationships.
基金supported by the 2024 Henan Province Philosophy and Social Science Planning Project(Youth Project)entitled“Research on the Mechanism and Intervention of Self-Regulated Learning in Promoting Children’s Chinese Reading Comprehension”(2024CJY070).
文摘This study examined the impact of teacher-student relationship quality on students’risk of bullying victimiza-tion and the mediating roles of student-student relationships and student engagement in this relationship.A total of 656 Chinese junior high school students(females=361,mean age=13.75,SD=0.98)completed validated measures of teacher-student relationship quality,student-student relationship quality,student engagement,and bullying victimization.Regression analysis results indicated that higher teacher-student relationship quality predicted a lower risk of student bullying victimization.Serial mediating effect testing of the student-student relationship quality and student engagement revealed that these factors fully mediated the relationship between teacher-student relationship quality and bullying victimization,resulting in a lower risk of bullying victimization.The results showed that student-student relationship quality had a more substantial mediating effect than student engagement.Thefindings support the Socio-Ecological Framework,suggesting that within the Microsystem,interactions between individuals and their immediate environments significantly impact their behavior.Specifically,thesefindings suggest that good teacher-student relationships can enhance the quality of student-student relationships and student engagement,thereby preventing and reducing the occurrence of bullying victimization.
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Since China’s reform and development,under the influence of the global religious and cultural boom,it has also focused on studying Islamic culture and related fields,resulting in many important representative works.The main characteristic of writing books in this area is to use popular knowledge materials that are suitable for various sectors of society.There are also works exploring the historical evolution of Islamic culture in China,which involve the relationship between Islamic culture and traditional Chinese culture.Before the 1980s,there were no significant achievements in publishing papers.However,since the 1990s,there has been a noticeable increase in papers related to Islamic culture and related fields,with new advances in research areas and methods.There are both articles that explore the attributes and characteristics of Islamic culture from the perspectives of world history and culture,as well as many articles that explore various specific aspects of Islamic culture from the perspective of specific topics.In this article,the author collects academic achievements in the study of Islamic culture in China and introduces them to the academic community,letting them know about China's research on world Islamic culture.
基金supported by National Key R&D Program of China(2022YFF1100300)Joint Fund of Henan Province Science and Technology R&D Program(235200810051)+1 种基金Key Project in Science and Technology Agency of Henan Province(242102310561)key research projects of higher education institutions in Henan Province(24B350002).
文摘Soybean is widely used in diets,and numerous reports have highlighted its antioxidant properties.However,constructing a methodology for rapid identifying and predicting a series of antioxidant active ingredients in Soybean presents certain challenges.Therefore,we introduced the spectrum-effect relationship-ingredient knockout identification technique to identify a series of antioxidant active ingredients in soybean.By combining untargeted metabolomics with network pharmacology,we predicted the antioxidant active ingredients and their target sites.We successfully identified 4 antioxidant active compounds(daidzein,genistein,daidzein,and glycitin)and 10 corresponding antioxidant targets(epidermal growth factor receptor(EGFR),estrogen receptor 1(ESR1),steroid receptor coactivator(SRC),tumor necrosis factor(TNF),kinase insert domain receptor(KDR),AKT serine/threonine kinase 1(AKT1),growth factor receptor bound protein 2(GRB2),signal transducer and activator of transcription1(STAT1),mitogen-activated protein kinase 8(MAPK8),B-cell lymphoma-2(BCL2))by our analysis.The validation results from cell experiments revealed that glycitin exhibited the best antioxidant activity and significantly influenced the expression of EGFR and the proteins associated with nuclear factor erythroid 2-related factor 2/NAD(P)H quinone dehydrogenase 1(NRF2/NQO1)signaling pathways.These findings were consistent with the predicted outcomes and were further confirmed in a zebrafish model.It suggests that glycitin may exert antioxidant effects by regulating the expression of EGFR,NRF2,and NQO1 proteins.The results demonstrate that a rapid analytical method for determining antioxidant activity was established.
文摘BACKGROUND Hyperglycemia secondary to diabetes can lead to organ failure unless properly controlled,and it is associated with numerous health issues,particularly metabolic problems.Breast cancer is one of the most common malignancies in females worldwide and represents a major health concern due to its morbidity and mortality.The progression of the disease and the response to treatment depend on numerous variables,including both tumor biology and patient-related factors.Key prognostic indicators include age,tumor size,lymph node involvement,histological type,hormone receptor status,and expression of biomarkers such as HER2.AIM To investigate the relationship between preoperative blood glucose levels and cancer prognostic factors in patients diagnosed with breast cancer.METHODS In this study patients diagnosed with breast cancer were classified into two groups:Those with blood glucose levels<125 mg/dL and those with levels>125 mg/dL.Estrogen and progesterone receptor positivity,Ki-67 percentage,Cerb2 positivity,number of positive lymph nodes,and presence of lymphovascular invasion were examined histopathologically.RESULTS The study included 246 patients with breast cancer:196 were in the normal glucose group and 50 were in the elevated glucose group.In the normal glucose group,68%of the tumors were left-sided and 32%were right-sided,while all tumors in the elevated glucose group were left-sided.This difference was statistically significant(P<0.001).Tumor size,Ki-67,and the number of positive lymph nodes were not associated with blood glucose in patients with breast cancer(P>0.05 for all).However,blood glucose was correlated with age in this population.Correlation analyses revealed that blood glucose was positively correlated with age(r=0.23,P<0.001).Additionally,Ki-67 was correlated with tumor size(r=0.14,P=0.03)and age(r=-0.18,P=0.005).CONCLUSION Our study highlighted the complex interplay between metabolic factors,such as fasting glucose levels,and breast cancer characteristics.Elevated glucose levels were associated with older age.The significant association between tumor laterality and glucose levels underscores the need for further investigation into the collective influence of metabolic and anatomical factors on breast cancer development and progression.
基金funded by the following projects:National Social Science Fund Project“The Empowerment Mechanism of Physical Exercise on Emotional Regulation in Adolescents”(23BTY116)Leshan Normal University 2024 Research Cultivation Project:“Research on the Trajectory Effect of Family Cumulative Risk and Home-based Activity of Adolescents”(KYPY2025-0014)+1 种基金Key Humanities and Social Sciences Cultivation Project of Leshan Normal University:“Research on the Sequence Difference of Knowledge and Behavior of Physical Activity among Adolescents and the Compensation Mechanism”and Sichuan Province College Students’Sports Association Annual Project“The Trajectory Effect of Family Cumulative Risk and Adolescents’Home Physical Activity”(23CDTXQ004).
文摘Background With the rapid growth of internet usage,adolescent cyberbullying has become a pressing issue.This study examines the longitudinal impact of leisure-time physical activity and peer relationships on cyberbullying over a one-year period,drawing on the Stage-Environment Fit Theory and the Interpersonal Relationship Theory.Methods A three-wave longitudinal study was conducted over one year,involving 896 middle school students from five schools in Sichuan,Jiangsu,and Guangdong,China,selected to ensure regional diversity.Participants were recruited using stratified random sampling,and data were collected at four-month intervals.Leisure-time physical activity,peer relationships,and cyberbullying behaviors were assessed using validated scales.Data were analyzed using latent growth modeling(LGM)and structural equation modeling(SEM)to examine longitudinal effects and mediation relationships.Results(1)Leisure-time physical activity,peer relationships,and cyberbullying behaviors remained relatively stable over time.(2)Both leisure-time physical activity and peer relationships significantly reduced future cyberbullying incidents,with physical activity also enhancing subsequent peer relationships.(3)Peer relationships partially mediated the effect of leisure-time physical activity on cyberbullying,indicating that improved peer interactions contributed to a reduction in cyberbullying behaviors.Conclusion This study found that leisure-time sports activities and peer relationships are important factors affecting cyberbullying,and peer relationships play a partial mediating role in it.The results provide empirical support for understanding the formation mechanism and influencing factors of cyberbullying.
基金supported by the Shanghai Sailing Program,China(Grant No.:23YF1413300).
文摘Quantitative structure-retention relationship(QSRR)is an important tool in chromatography.QSRR examines the correlation between molecular structures and their retention behaviors during chromatographic separation.This approach involves developing models for predicting the retention time(RT)of analytes,thereby accelerating method development and facilitating compound identification.In addition,QSRR can be used to study compound retention mechanisms and support drug screening efforts.This review provides a comprehensive analysis of QSRR workflows and applications,with a special focus on the role of artificial intelligence-an area not thoroughly explored in previous reviews.Moreover,we discuss current limitations in RT prediction and propose promising solutions.Overall,this review offers a fresh perspective on future QSRR research,encouraging the development of innovative strategies that enable the diverse applications of QSRR models in chromatographic analysis.
基金supported by National Natural Science Foundation of China(Grant No.51975146)Natural Science Foundation of Shandong Province(Grant No.ZR2020QE171)。
文摘This paper explored how the work hardening process enhances the yield strength(YS)of magnesium alloy and established a novel quantitative relationship between microstructure and YS.The YS of an extruded Mg–Nd–Zn–Zr alloy was nearly tripled by rotary swaging at 200℃,attributing to dislocation accumulation,grain fragmentation,and basal texture formation.A modified Hall–Petch relationship(GM–HP)was established by introducing the equivalent orientation factor and dislocation density to accurately quantify the YS and the contribution of each strengthening mechanism.The contributions of dislocation hardening and grain boundary strengthening were multiplied by the strong basal texture.This paper offered valuable insights and guidance for designing and optimizing high-strength wrought magnesium alloys and their plastic processing methods.
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.
