期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到7,166篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Generating golden Syrian hamsters with conditional alleles via zygote microinjection of CRISPR/Cas9 认领 引用
1
作者 Wei Chen Xu Zhang +14 位作者 Rui Fan Xia Li Feifei Guan Gefan Wan Weining Kong Xiaolong Qi Shuo Pan Sijing Shi Yuanlong Su Shan Gao Wei Huang Xunde Xian Jiangning Liu Yuhui Wang Yuanwu Ma 《Animal Models and Experimental Medicine》 CAS CSCD 2026年第2期308-318,共11页
Background:The golden Syrian hamster is a valuable animal model for studying carcinogenesis,metabolic disorders,cardiovascular diseases,and viral infections due to its biological and pathological similarities to human... Background:The golden Syrian hamster is a valuable animal model for studying carcinogenesis,metabolic disorders,cardiovascular diseases,and viral infections due to its biological and pathological similarities to humans.However,the development of genetically engineered hamsters has lagged behind that of mice and rats,largely because of an embryonic development block at the two-cell stage in vitro.Although CRISPR/Cas9-mediated gene knockout has been achieved in hamsters,precise DNA fragment insertion or conditional knockout(cKO)models have not previously been reported,likely due to technical limitations in embryo manipulation and insufficient efficiency of homology-directed repair(HDR).Methods:In this study,we generated conditional alleles of the ApoF gene in golden Syrian hamsters.A two-cut strategy was applied using Cas9 protein,two sgRNAs,and a single donor plasmid containing exon 2 flanked by loxP sites and two~0.8 kb homology arms.A mixture of Cas9 protein,sgRNAs,and the donor plasmid was microinjected into the pronuclei of one-cell stage hamster embryos.Results:The efficiency of CRISPR/Cas9-mediated loxP knock-in reached up to 27%,and the genetically modified floxed alleles were successfully transmitted through the germline.The functionality of the inserted loxP sites was validated by in vivo Cremediated recombination following local administration of AAV vectors,including AAV-cTnT-Cre in the heart and AAV-CMV-Cre in the brain.Conclusions:To our knowledge,this work represents the first successful establishment of a conditional knockout model in the golden Syrian hamster,providing a valuable tool for mechanistic studies of gene function and disease modeling. 展开更多
关键词 conditional knockout genome editing Golden Syrian hamster
暂未订购 下载PDF
Evolve and Revoke:A Secure and Efficient Conditional Proxy Re-Encryption Scheme with Ciphertext Evolution 认领 引用
2
作者 Han-Yu Lin Tung-Tso Tsai Yi-Jia Ye 《Computers, Materials & Continua》 SCIE EI 2026年第4期1565-1583,共19页
Cloud data sharing is an important issue in modern times.To maintain the privacy and confidentiality of data stored in the cloud,encryption is an inevitable process before uploading the data.However,the centralized ma... Cloud data sharing is an important issue in modern times.To maintain the privacy and confidentiality of data stored in the cloud,encryption is an inevitable process before uploading the data.However,the centralized management and transmission latency of the cloud makes it difficult to support real-time processing and distributed access structures.As a result,fog computing and the Internet of Things(IoT)have emerged as crucial applications.Fog-assisted proxy re-encryption is a commonly adopted technique for sharing cloud ciphertexts.It allows a semitrusted proxy to transforma data owner’s ciphertext into another re-encrypted ciphertext intended for a data requester,without compromising any information about the original ciphertext.Yet,the user revocation and cloud ciphertext renewal problems still lack effective and secure mechanisms.Motivated by it,we propose a revocable conditional proxy re-encryption scheme offering ciphertext evolution(R-CPRE-CE).In particular,a periodically updated time key is used to revoke the user’s access privileges while an access condition prevents a malicious proxy from reencrypting unauthorized ciphertext.We also demonstrate that our scheme is provably secure under the notion of indistinguishability against adaptively chosen identity and chosen ciphertext attacks in the random oracle model.Performance analysis shows that our scheme reduces the computation time for a complete data access cycle from an initial query to the final decryption by approximately 47.05%compared to related schemes. 展开更多
关键词 Revocable proxy re-encryption conditional access control ciphertext evolution fog computing
暂未订购 下载PDF
Effects and mechanism of conditional osteoblast PHB1 knockout on bone metabolism in mice 认领 引用
3
作者 Jia-Hui Li Zheng Shan +5 位作者 Wen-Wei Xie Yan-Ling Deng Bing-Qing Wang Shi-Long Fan Qing-Yu Feng Zhi-Kun Zhou 《Biomedical Engineering Communications》 CAS 2026年第5期64-75,共12页
Background:Postmenopausal osteoporosis(PMOP)is a highly prevalent metabolic bone disorder with limited effective clinical therapies.To verify prohibitin 1(PHB1)as a potential therapeutic target for PMOP,we generated a... Background:Postmenopausal osteoporosis(PMOP)is a highly prevalent metabolic bone disorder with limited effective clinical therapies.To verify prohibitin 1(PHB1)as a potential therapeutic target for PMOP,we generated an osteoblast-specific PHB1 conditional knockout(CKO)mouse model.This strategy is based on our preliminary results that Huangqi Sanxian Decoction enhances osteogenesis by inhibiting PHB1 expression,indicating that PHB1 may serve as a critical negative regulator of bone formation.Methods:An osteoblast-specific PHB1 conditional knockout(CKO)mouse model was established for in vivo experiments.In vitro assays were conducted to evaluate the biological functions of PHB1 knockout on osteoblasts,including assessments of cell proliferation,differentiation,and survival.Molecular analyses were performed to detect the expression of osteogenic markers and the activity of the PI3K/Akt signaling pathway.Results:Compared to controls,CKO mice exhibited significantly increased bone mineral density(BMD),elevated bone formation marker(PINP),and reduced bone resorption marker(CTX-1)and osteoclast number.In vitro,PHB1 knockout enhanced osteoblast proliferation,differentiation(increased ALP activity and mineralization),and survival(elevated Bcl-2/Bax ratio,decreased caspase-3),while upregulating osteogenic markers(OPN,Runx2).Mechanistically,the PI3K/Akt pathway was activated,as indicated by an increased p-Akt/Akt ratio.Conclusion:These findings demonstrate that osteoblast-specific PHB1 deletion promotes bone formation and inhibits resorption via the PI3K/Akt pathway,offering a mechanistic foundation for PHB1-targeted therapies in PMOP. 展开更多
关键词 PHB1 gene Conditional knockout PI3K/Akt Osteoblast Bone metabolism
暂未订购 下载PDF
An improved conditional denoising diffusion GAN for Mach number field reconstruction in a multi-tunnel combined inlet based on sparse parameter information 认领 引用
4
作者 Ke MIN Fan LEI +2 位作者 Jiale ZHANG Chengxiang ZHU Yancheng YOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期169-190,共22页
The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To... The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields. 展开更多
关键词 Flow field reconstruction Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN) Mode transition Sparse parameter information Three-dimensional inward-tunning combined inlet
暂未订购 下载PDF
Development of Patient-Derived Conditionally Reprogrammed 3D Breast Cancer Culture Models for Drug Sensitivity Evaluation 认领 引用
5
作者 Jing Cai Haoyun Zhu +4 位作者 Weiling Guo Ting Huang Pangzhou Chen Wen Zhou Ziyun Guan 《Oncology Research》 SCIE 2026年第1期500-520,共21页
Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual pat... Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual patient responses towards different drugs.This study aimed to investigate the patient-derived breast cancer culture models for drug sensitivity evaluations.Methods:Tumor and adjacent tissues from female breast cancer patients were collected during surgery.Patient-derived breast cancer cells were cultured using the conditional reprogramming technique to establish 2D models.The obtained patient-derived conditional reprogramming breast cancer(CRBC)cells were subsequently embedded in alginate-gelatin methacryloyl hydrogel microspheres to form 3D culture models.Comparisons between 2D and 3D models were made using immunohistochemistry(tumor markers),MTS assays(cell viability),flow cytometry(apoptosis),transwell assays(migration),and Western blotting(protein expression).Drug sensitivity tests were conducted to evaluate patient-specific responses to anti-cancer agents.Results:2D and 3D culture models were successfully established using samples from eight patients.The 3D models retained histological and marker characteristics of the original tumors.Compared to 2D cultures,3D models exhibited increased apoptosis,enhanced drug resistance,elevated stem cell marker expression,and greater migration ability—features more reflective of in vivo tumor behavior.Conclusion:Patient-derived 3D CRBC models effectively mimic the in vivo tumor microenvironment and demonstrate stronger resistance to anti-cancer drugs than 2D models.These hydrogel-based models offer a cost-effective and clinically relevant platform for drug screening and personalized breast cancer treatment. 展开更多
关键词 Patient-derived breast cancer cells conditional reprogramming hydrogel microsphere 3D culture model drug screening
暂未订购 下载PDF
Conditional Generative Adversarial Network-Based Travel Route Recommendation 认领 引用
6
作者 Sunbin Shin Luong Vuong Nguyen +3 位作者 Grzegorz J.Nalepa Paulo Novais Xuan Hau Pham Jason J.Jung 《Computers, Materials & Continua》 SCIE EI 2026年第1期1178-1217,共40页
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of... Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence. 展开更多
关键词 Travel route recommendation conditional generative adversarial network heterogeneous information network anchor-and-expand algorithm
暂未订购 下载PDF
A data augmentation method for lacustrine shale lithofacies classification based on a conditional diffusion probabilistic model:A case study from the Dongying Depression,Bohai Bay Basin,China 认领 引用
7
作者 Gui-Ang Li Cheng-Yan Lin +6 位作者 Chun-Mei Dong Li-Hua Ren Peng-Jie Ma Yu-Qi Wu Guo-Yin Zhang Xin-Yu Du Zi-Ru Zhao 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3017-3036,共20页
Accurate identification of lithofacies is critical for shale hydrocarbon exploration and development.Although machine learning(ML)is one of the most effective approaches for predicting shale lithofacies,the inherent g... Accurate identification of lithofacies is critical for shale hydrocarbon exploration and development.Although machine learning(ML)is one of the most effective approaches for predicting shale lithofacies,the inherent geological heterogeneity results in scarce training samples and severely imbalanced class distributions,leading conventional ML methods to experience overfitting and reduced accuracy.To address this issue,we introduced a conditional diffusion probabilistic model(CDPM)to address these challenges and developed a comprehensive data augmentation framework for shale lithofacies prediction.Applying this framework to the Upper Fourth Member of the Shahejie Formation(Es4s)in the Dongying Depression,we successfully generated 3,600 class-balanced augmented samples from 926core-calibrated samples and eight conventional well log curves from well NY1,achieving an 878.3%increase in rare organic-rich fissile calcareous mudstone(L1)lithofacies.To ensure the reliability of the augmented data,a comprehensive quality assessment was conducted,demonstrating that augmented data effectively retained logging characteristics and petrophysical relationships,with a Fréchet Inception Distance(FID)of 22.9 and a maximum mean discrepancy(MMD)of 0.078.Building upon this highquality augmented dataset,we evaluated the impact of data augmentation on lithofacies classification performance across random forest(RF),support vector machine(SVM),and gradient boosted decision tree(GBDT)algorithms.The results showed substantial improvements,with average accuracy and F1 score increases of 13.6%and 16.5%,respectively,and a 33.1%improvement in L1 recall.To further validate the practical applicability of our approach,blind-well validation on independent wells from different structural positions demonstrated robust generalization capability,achieving significant improvement over traditional ML methods.This study pioneers a conditional diffusion model for predicting shale lithofacies,providing a novel framework for characterising lacustrine shale oil reservoirs and predicting sweet spots. 展开更多
关键词 Lacustrine shale Lithofacies prediction Conditional diffusion probabilistic model Data augmentation Class imbalance
暂未订购 下载PDF
Cognitive signatures of conditional reasoning dysfunction in major depression 认领 引用
8
作者 Elif Yöyen 《World Journal of Psychiatry》 SCIE 2026年第2期414-418,共5页
Major depressive disorder(MDD)represents one of the most urgent global mental health challenges,affecting hundreds of millions of individuals across cultures and socioeconomic contexts.While the affective and motivati... Major depressive disorder(MDD)represents one of the most urgent global mental health challenges,affecting hundreds of millions of individuals across cultures and socioeconomic contexts.While the affective and motivational dimensions of depression have long been emphasized,the cognitive dimension of the disorder has increasingly attracted attention.Within this cognitive framework,the study by Li et al represents an important milestone.It is the first investigation to combine the Wason selection task(WST),a classical paradigm for examining conditional reasoning,with event-related potentials(ERP),a method uniquely suited for revealing the temporal dynamics of cognitive processing.By integrating behavioral performance with electrophysiological measures,the authors provide valuable new insights into the neural mechanisms underlying reasoning dysfunction in MDD.However,while this study makes an important contribution,caution is warranted in interpreting its clinical and diagnostic implications.The methodological limitations,such as small sample size,limited ecological validity of the WST,and absence of control for confounding variables,should be carefully considered when evaluating the generalizability of ERP findings.Beyond summarizing the findings of Li et al,this letter emphasizes both the strengths and weaknesses of their approach.While the integration of cognitive reasoning and neurophysiological evidence is commendable,the lack of replication and comparative data leaves important open questions about how these results align with prior ERP or functional magnetic resonance imaging studies of depressive cognition.A more critical synthesis of these contextual gaps enhances the interpretative depth of the article.Overall,the study offers valuable preliminary evidence of conditional reasoning dysfunction in MDD,but its conclusions should be viewed as exploratory rather than definitive.Future research must address methodological limitations before clinical translation is possible. 展开更多
关键词 Major depression Neurological basis Wason selection task Conditional reasoning Cognitive neuroscience
暂未订购 下载PDF
FedCLCC:A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing 认领 引用 被引量：1
9
作者 Kangning Yin Xinhui Ji +1 位作者 Yan Wang Zhiguo Wang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2025年第1期80-93,共14页
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ... Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms. 展开更多
关键词 Federated learning Statistical heterogeneity Personalized model Conditional computing Contrastive learning
暂未订购 下载PDF
An Extension of Conditional Nonlinear Optimal Perturbation in the Time Dimension and Its Applications in Targeted Observations 认领 引用 被引量：1
10
作者 Ziqing ZU Mu MU +1 位作者 Jiangjiang XIA Qiang WANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第9期1783-1797,共15页
The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typic... The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typically input multiple time slices without deterministic dependencies.In this study,the CNOP for DLMs(CNOP-DL)is proposed as an extension of the CNOP in the time dimension.This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations.The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM.The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time.Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself,but also for random perturbations.Therefore,this approach holds potential for guiding practical field campaigns.Notably,forecast errors are more sensitive to time than to location in the sensitive area.It highlights the crucial role of identifying the time of the sensitive area in targeted observations,corroborating the usefulness of extending the CNOP in the time dimension. 展开更多
关键词 deep-learning forecasting model conditional nonlinear optimal perturbation targeted observation sensitive area
暂未订购 下载PDF
Cross‑sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market 认领 引用
11
作者 Zhong‑Qiang Zhou Jiajia Wu +1 位作者 Ping Huang Xiong Xiong 《Financial Innovation》 2025年第1期2984-3007,共24页
This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power ... This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models.Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression,we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market.Our results demonstrate that conditional models significantly outperform their unconditional counterparts.Notably,investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period.Additionally,it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs.We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events. 展开更多
关键词 Cross-sectional anomalies Conditional asset pricing Investor sentiment
暂未订购 下载PDF
Weighted Attribute Based Conditional Proxy Re-Encryption in the Cloud 认领 引用
12
作者 Xixi Yan Jing Zhang Pengyu Cheng 《Computers, Materials & Continua》 SCIE EI 2025年第4期1399-1414,共16页
Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribu... Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights delegation.By introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced greatly.At the same time,a weighted tree structure is constructed to simplify the expression of access structure effectively.With conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal costs.The scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources. 展开更多
关键词 Cloud service conditional proxy re-encryption user revocation weighted attribute
暂未订购 下载PDF
A FORMULA OF CONDITIONAL ENTROPY FOR METRICS INDUCED BY PROBABILITY BI-SEQUENCES 认领 引用
13
作者 M.RAHIMI N.BIDABADI 《Acta Mathematica Scientia》 SCIE CSCD 2025年第4期1619-1639,共21页
We study the conditional entropy of topological dynamical systems using a family of metrics induced by probability bi-sequences.We present a Brin-Katok formula by replacing the mean metric by a family of metrics induc... We study the conditional entropy of topological dynamical systems using a family of metrics induced by probability bi-sequences.We present a Brin-Katok formula by replacing the mean metric by a family of metrics induced by a probability bi-sequence.We also establish the Katok’s entropy formula for conditional entropy for ergodic measures in the case of the new family of metrics. 展开更多
关键词 entropy conditional entropy probability bi-sequence
暂未订购 下载PDF
Wavelet Transform-Based Bayesian Inference Learning with Conditional Variational Autoencoder for Mitigating Injection Attack in 6G Edge Network 认领 引用 被引量：1
14
作者 Binu Sudhakaran Pillai Raghavendra Kulkarni +1 位作者 Venkata Satya Suresh kumar Kondeti Surendran Rajendran 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期1141-1166,共26页
Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies... Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies. 展开更多
关键词 Bayesian inference learning automaton convolutional wavelet transform conditional variational autoencoder malicious data injection attack edge environment 6G communication
暂未订购 下载PDF
Neural correlates of conditional reasoning dysfunction in major depression:An event-related potential study with the Wason selection task 认领 引用 被引量：1
15
作者 Jia-Xv Li Mei-Chen Lu +7 位作者 Luo-An Wu Wei Li Yu Li Xin-Ping Li Xiao-Hong Liu Xue-Zheng Gao Zhen-He Zhou Hong-Liang Zhou 《World Journal of Psychiatry》 SCIE 2025年第12期107-119,共13页
BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To inv... BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To investigate the ERP characteristics of conditional reasoning in MD patients and explore the neural mechanism of cognitive processing.METHODS Thirty-four patients with MD and 34 healthy controls(HCs)completed ERP measurements while performing the Wason selection task(WST).The clusterbased permutation test in FieldTrip was used to compare the differences in the mean amplitudes between the patients with MD and HCs on the ERP components under different experimental conditions.Behavioral data[accuracy(ACC)and reaction times(RTs)],the ERP P100 and late positive potentials(LPPs)were analyzed.RESULTS Although the mean ACC was greater and the mean of RTs was shorter in HCs than in MD patients,the differences were not statistically significant.However,across both groups,the ACC in the precautionary WST was greater than that in the other tasks,and the RTs in the abstract task were greater than those in the other tasks.Importantly,compared with that of HCs,the P100 of the left centroparietal sites was significantly increased,and the early LPP was attenuated at parietal sites and increased at left frontocentral sites;the medium LPP and late LPP were increased at the left frontocentral sites.CONCLUSION Patients with MD have conditional reasoning dysfunction and exhibit abnormal ERP characteristics evoked by the WST,which suggests neural correlates of abnormalities in conditional reasoning function in MD patients. 展开更多
关键词 Major depression Event-related potential Wason selection task Conditional reasoning Neural mechanism
暂未订购 下载PDF
The evolving distribution of humidity conditional on temperature and implications for compound heat extremes across China in a warming world 认领 引用
16
作者 Caixia Liang Jiacan Yuan 《Atmospheric and Oceanic Science Letters》 CSCD 2025年第6期9-14,共6页
The likelihood of extreme heat occurrence is continuously increasing with global warming.Under high temperatures,humidity may exacerbate the heat impact on humanity.As atmospheric humidity depends on moisture availabi... The likelihood of extreme heat occurrence is continuously increasing with global warming.Under high temperatures,humidity may exacerbate the heat impact on humanity.As atmospheric humidity depends on moisture availability and is constrained by air temperature,it is important to project the changes in the distribution of atmospheric humidity conditional on air temperature as the climate continuously warms.Here,a non-crossing quantile smoothing spline is employed to build quantile regression models emulating conditional distributions of dew point(a measure of humidity)on local temperature evolving with escalating global mean surface temperature.By applying these models to 297 weather stations in seven regions in China,the study analyzes historical trends of humid-heat and dry-hot days,and projects their changes under global warming of 2.0℃ and 4.5℃.In response to global warming,rising trends of humid-heat extremes,while weakening trends of dry-hot extremes,are observed at most stations in Northeast China.Additionally,results indicate an increasing trend in dry-hot extremes at numerous stations across central China,but a rise in humid-heat extremes over Northwest China and coastal regions.These trends found in the current climate state are projected to intensify under 2.0℃ and 4.5℃ warming,possibly influenced by the heterogeneous variations in precipitation,soil moisture,and water vapor fluxes.Requiring much lower computational resources than coupled climate models,these quantile regression models can further project compound humidity and temperature extremes in response to different levels of global warming,potentially informing the risk management of compound humid-heat extremes on a local scale. 展开更多
关键词 Global warming Conditional distribution of dew point on temperature Non-crossing quantile smoothing spline model Compound heat extremes
暂未订购 下载PDF
A novel conditional diagnosability algorithm under the PMC model 认领 引用
17
作者 郭晨 Liang Jiarong +1 位作者 Leng Ming Peng Shuo 《High Technology Letters》 EI CAS 2017年第4期384-389,共6页
Conditionally t-diagnosable and t-diagnosable are important in system level diagnosis. Therefore,it is valuable to identify whether the system is conditionally t-diagnosable or t-diagnosable and derive the correspondi... Conditionally t-diagnosable and t-diagnosable are important in system level diagnosis. Therefore,it is valuable to identify whether the system is conditionally t-diagnosable or t-diagnosable and derive the corresponding conditional diagnosability and diagnosability. In the paper,distinguishable measures of pairs of distinct faulty sets with a new perspective on establishing functions are focused.Applying distinguishable function and decision function,it is determined whether a system is conditionally t-diagnosable( or t-diagnosable) or not under the PMC( Preparata,Metze,and Chien)model directly. Based on the decision function,a novel conditional diagnosability algorithm under the PMC model is introduced which can calculate conditional diagnosability rapidly. 展开更多
关键词 the PMC(Preparata,Metze,and Chien) model conditionally t-diagnosable conditional diagnosability conditional diagnosability algorithm
暂未订购 下载PDF
Optimal Receiver Operating Characteristic Curve of Classical Conditional Power under Normal Models 认领 引用
18
作者 ZHANG Ying-Ying 《应用概率统计》 CSCD 北大核心 2025年第2期277-304,共28页
A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment ... A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give goo go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the goo go decisions of the optimal CCP. 展开更多
关键词 area under the curve(AUC) classical conditional power(CCP) goo go decisions historical and interim data receiver operating characteristic(ROC)curve
暂未订购 下载PDF
Two Causal-Modeling Approaches to Indicative Conditionals 认领 引用
19
作者 ChingHui Su 《逻辑学研究》 CSSCI 2025年第6期43-61,共19页
Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subju... Recently there have been two causal modelling approaches to indicative conditionals,i.e.extrapolationist(Deng&Lee,2021)and filterist(Liang&Wang,2022),although they all take an interventionist position on subjunctive conditionals.Motivated by the so-called OK pairs,they try to provide a convincing explanation of the intuition underlying the OK pairs.As far as we know,what they have done is to provide not only an explanation of the OK pairs,but also a way of distinguishing between indicative and subjunctive conditionals.Although we agree with their success in explaining the OK pairs within a causal modelling framework,we argue that their ways of distinguishing between indicative and subjunctive conditionals fail.Instead,we argue that their approaches can be used to distinguish between two readings of conditionals,the epistemic reading and the ontic reading.which can be applied to both indicative and subjunctive conditionals.We conclude by arguing that these two readings are related to two approaches to asking and answering causal questions:the“auses-of-effects"approach and the"effects-of-causes"approach. 展开更多
关键词 subjunctive conditionals extrapolationist causal modelling approaches epistemic reading causal modeling filterist indicative subjunctive con ok pairs
暂未订购 下载PDF
Identification of unconditional and conditional QTL for oil, protein and starch content in maize 认领 引用 被引量：12
20
作者 Yuqiu Guo Xiaohong Yang +4 位作者 Subhash Chander Jianbing Yan Jun Zhang Tongming Song Jiansheng Li 《The Crop Journal》 2013年第1期34-42,共9页
Oil, protein and starch are key chemical components of maize kernels. A population of 245 recombinant inbred lines(RILs) derived from a cross between a high-oil inbred line, By804, and a regular inbred line, B73, was ... Oil, protein and starch are key chemical components of maize kernels. A population of 245 recombinant inbred lines(RILs) derived from a cross between a high-oil inbred line, By804, and a regular inbred line, B73, was used to dissect the genetic interrelationships among oil, starch and protein content at the individual QTL level by unconditional and conditional QTL mapping. Combined phenotypic data over two years with a genetic linkage map constructed using 236 markers, nine, five and eight unconditional QTL were detected for oil, protein and starch content, respectively. Some QTL for oil, protein and starch content were clustered in the same genomic regions and the direction of their effects was consistent with the sign of their correlation. In conditional QTL mapping, 37(29/8) unconditional QTL were not detected or showed reduced effects, four QTL demonstrated similar effects under unconditional and conditional QTL mapping, and 17 additional QTL were identified by conditional QTL mapping. These results imply that there is a strong genetic relationship among oil, protein and starch content in maize kernels. The information generated in the present investigation could be helpful in marker-assisted breeding for maize varieties with desirable kernel quality traits. 展开更多
关键词 Zea mays Oil Protein Starch Unconditional QTL Conditional QTL
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈