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Planning,monitoring and replanning techniques for handling abnormity in HTN-based planning and execution
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作者 KANG Kai CHENG Kai +2 位作者 SHAO Tianhao ZHANG Hongjun ZHANG Ke 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第5期1264-1275,共12页
A framework that integrates planning,monitoring and replanning techniques is proposed.It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of... A framework that integrates planning,monitoring and replanning techniques is proposed.It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution.The framework consists of three parts:the hierarchical task network(HTN)planner based on Monte Carlo tree search(MCTS),hybrid plan monitoring based on forward and backward and norm-based replanning method selection.The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration.Based on specific objectives,it can identify the best solution to the current problem.The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action,thus trigger the replanning.The norm-based replanning selection method can measure the difference between the expected state and the actual state,and then select the best replanning algorithm.The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way. 展开更多
关键词 hierarchical task network Monte carlo tree search(MCTS) PLANNING EXECUTION abnormity
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Manganese Abnormity in Holocene Sediments of the Bohai Sea 被引量:1
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作者 刘建国 李安春 +1 位作者 徐兆凯 徐方建 《Journal of China University of Geosciences》 SCIE CSCD 2007年第2期135-141,共7页
Manganese abnormity has been observed in the Holocene sediments of the mud area of Bohai Sea. On the basis of grain size, chemical composition, heavy mineral content and accelerator mass spectrometry (AMS) 14C datin... Manganese abnormity has been observed in the Holocene sediments of the mud area of Bohai Sea. On the basis of grain size, chemical composition, heavy mineral content and accelerator mass spectrometry (AMS) 14C dating of foraminifer, relationships between manganese abnormity and sedimentation rates, material source, hydrodynamic conditions are probed. Manganese abnormity occurred during the Middle Holocene when sea level and sedimentation rates were higher than those at present. Sedimentary hiatus was not observed when material sources and hydrodynamic conditions were quite similar. Compared with the former period, the latter period showed a decrease in reduction environment and an inclination toward oxidation environment with high manganese content, whereas provenance and hydrodynamic conditions showed only a slight change. From the above observations, it can be concluded that correlation among manganese abnormity, material source, and hydrodynamic conditions is not obvious. Redox environment seems to be the key factor for manganese enrichment, which is mainly related to marine authigenic process. 展开更多
关键词 manganese abnormity HOLOCENE SEDIMENT sedimentation rate Bohai Sea
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Electromagnetic Abnormity Recorded by Borehole TOA Installment during the Mojiang M_S5. 9 Earthquake on September 8,2018 被引量:2
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作者 LI Mei LI Wuxian +4 位作者 ZHAO Huaxing ZHANG Li ZHANG Ping ZHANG Xuhui YU Chen 《Earthquake Research in China》 CSCD 2019年第4期648-660,共13页
As an achievement of the cooperation with Japan,TOA electromagnetic observation station was established with an 800 m borehole antenna and put into service in 1992 in Dali,Yunnan province,China.Li Wuxian et al.(2003)s... As an achievement of the cooperation with Japan,TOA electromagnetic observation station was established with an 800 m borehole antenna and put into service in 1992 in Dali,Yunnan province,China.Li Wuxian et al.(2003)summarized main anomalous variation characters by analyzing 23 strong earthquakes with magnitudes more than 5.0 recorded in the first ten years.This work mainly presents the electromagnetic changes prior to the last Mojiang MS5.9 earthquake on September 8,2018.First of all,the initial weak signals appeared in two ULF channels out of three observing channels(CH10.01-0.10 Hz,CH20.1-1.0 Hz and CH31-9 kHz)on May 30,2018 at Dali TOA electromagnetic station.The information recorded was characterized by wave-like changes with magnitudes of ACH1≤0.26 mV in CH1 and pulse-like impulses of ACH2≤0.6 mV in CH2,respectively.Then,abnormal information gradually enhanced either in magnitudes or in occurrence frequency.Pulse-like signals were full of lattices of recording paper for CH2 during June24-25 and slopped over the recording paper during June 28-29,with the magnitudes being greater than or equal to 10 mV.At the same time,the clear wave-like signals also appeared in CH1 with a maximum magnitude of^0.6 mV on June 28 and reached its climax.From then on,the information started to decrease from the end of July and only weak signals occasionally occurred till the end of August 2018,when obvious anomaly was recorded again in two ULF channels with maximum magnitudes of ACH1~0.2 mV and ACH2~0.3 mV respectively.Generally,these signals did not appear continuously but group by group and accumulated intensively only in ULF band instead of VLF band during the total period.10 days later,the Mojiang MS5.9 earthquake occurred on September 8,2018,300 km away from Dali TOA station,and a coseismic response was also recorded at this time.Thus,these ULF electromagnetic abnormities could be probably attributed to the Mojiang event. 展开更多
关键词 Mojiang M_S5.9 earthquake Borehole electromagnetic abnormity Coseismic response
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Electromagnetic Abnormity Observed at Sujiatun Seismic Station and Its Relation toEarthquake
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作者 Li Mei Cheng Wenhai +1 位作者 Hou Wankai Yin Shipeng 《Earthquake Research in China》 2006年第3期295-304,共10页
The Sujiatun seismic station is a local professional seismic observation station in the city of Shenyang, Liaoning province. Electromagnetic radiations observation apparatus, of the type EMAOS-L, are used there. In th... The Sujiatun seismic station is a local professional seismic observation station in the city of Shenyang, Liaoning province. Electromagnetic radiations observation apparatus, of the type EMAOS-L, are used there. In this paper, four years observation data and observed earthquakes with M_S≥4.0 were collected in the Sujiatun observation station. The characteristics of electromagnetic radiation abnormities and earthquakes are analyzed from such aspects as capabilities of the apparatus used, tectonic structure, origins of electromagnetic radiations and so on. By analysis, precursor abnormities observed at this observation station are remarkable. The electric and magnetic abnormities are with good synchronism and the abnormities can respond better to the earthquakes with the epicenter distance being less than 20km and taking place in the upper crust. At the same time, for earthquakes with large magnitudes, the variation of precursor abnormities shows a periodic diurnal variation. 展开更多
关键词 Sujiatun observation station Electromagnetic radiations abnormity Seismicactivities Characteristics of abnormities and earthquakes
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Improving Federated Learning through Abnormal Client Detection and Incentive
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作者 Hongle Guo Yingchi Mao +3 位作者 Xiaoming He Benteng Zhang Tianfu Pang Ping Ping 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期383-403,共21页
Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients m... Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness. 展开更多
关键词 Federated learning abnormal clients INCENTIVE credit score abnormal score DETECTION
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Predictors of Abnormal Vaginal Discharge among Women of Reproductive Age in Southeast Nigeria
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作者 Jideuma Egwim Victor Dike +5 位作者 Hope Igbonagwam Nkechinyere Oke Uzoma Amajo Akuchi Okafor Angela Izegbune Ijedimma Okafor 《International Journal of Clinical Medicine》 CAS 2024年第7期240-256,共17页
Background: An abnormal vaginal discharge is a common complaint among women of reproductive age, and it can indicate serious conditions like pelvic inflammatory disease and cervical cancer. This study aimed to assess ... Background: An abnormal vaginal discharge is a common complaint among women of reproductive age, and it can indicate serious conditions like pelvic inflammatory disease and cervical cancer. This study aimed to assess the predictors of abnormal vaginal discharge in women of reproductive age group in Imo State, Southeast Nigeria. Methods: A cross-sectional study was conducted among 368 women of reproductive age group attending the clinic at Federal University Teaching Hospital Owerri, in Imo State, Nigeria. Respondents were recruited using a systematic sampling technique. Data were collected using a pre-tested interviewer-administered questionnaire. Multivariable analysis was performed to determine predictors of abnormal vaginal discharge. Statistical significance was set at p Results: The mean age of the respondents was 30 ±  4.5 years. Predictors of abnormal vaginal discharge were: age 36 - 45 years (OR: 4.5;95% C.I: 1.023 - 8.967, p = 0.041), being a student (OR: 2.4: 95% C.I: 1.496 - 7.336, p = 0.003), use of oral contraceptives (OR: 3.4;95% C.I: 1.068 - 6.932, p = 0.010), use of water cistern (OR: 4.7;C.I: 1.654 - 5.210, p = 0.028) anal hygiene practices (OR: 2.7;95% C.I: 1.142 - 4.809, p Conclusion: These findings suggest that targeted sexual and reproductive health interventions should be provided to reduce the risk of abnormal vaginal discharge in women of reproductive age group. 展开更多
关键词 PREDICTORS abnormAL VAGINAL DISCHARGE
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Combinatorial reasoning-based abnormal sensor recognition method for subsea production control system
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作者 Rui Zhang Bao-Ping Cai +3 位作者 Chao Yang Yu-Ming Zhou Yong-Hong Liu Xin-Yang Qi 《Petroleum Science》 SCIE EI CAS CSCD 2024年第4期2758-2768,共11页
The subsea production system is a vital equipment for offshore oil and gas production.The control system is one of the most important parts of it.Collecting and processing the signals of subsea sensors is the only way... The subsea production system is a vital equipment for offshore oil and gas production.The control system is one of the most important parts of it.Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal.However,subsea sensors degrade rapidly due to harsh working environments and long service time.This leads to frequent false alarm incidents.A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed.A combinatorial algorithm is proposed to group sensors.The long short-term memory network(LSTM)is used to establish a single inference model.A counting-based judging method is proposed to identify abnormal sensors.Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method.The results show that the proposed method can identify the abnormal sensors effectively. 展开更多
关键词 abnormal sensor Combinatorial algorithm Fault identification Subsea production control system
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Relationship of Retinal Nerve Fiber Layer Thickness and Retinal Vessel Calibers with Cognitive Impairment in the Asymptomatic Polyvascular Abnormalities Population
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作者 WANG Dan Dan WANG An Xin +3 位作者 ZHANG Xiao Li WEI Wen Bin WU Shou Ling ZHAO Xing Quan 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2024年第2期196-203,共8页
Objective Cognitive impairment(CI)in older individuals has a high morbidity rate worldwide,with poor diagnostic methods and susceptible population identification.This study aimed to investigate the relationship betwee... Objective Cognitive impairment(CI)in older individuals has a high morbidity rate worldwide,with poor diagnostic methods and susceptible population identification.This study aimed to investigate the relationship between different retinal metrics and CI in a particular population,emphasizing polyvascular status.Methods We collected information from the Asymptomatic Polyvascular Abnormalities Community Study on retinal vessel calibers,retinal nerve fiber layer(RNFL)thickness,and cognitive function of 3,785participants,aged 40 years or older.Logistic regression was used to analyze the relationship between retinal metrics and cognitive function.Subgroups stratified by different vascular statuses were also analyzed.Results RNFL thickness was significantly thinner in the CI group(odds ratio:0.973,95%confidence interval:0.953–0.994).In the subgroup analysis,the difference still existed in the non-intracranial arterial stenosis,non-extracranial carotid arterial stenosis,and peripheral arterial disease subgroups(P<0.05).Conclusion A thin RNFL is associated with CI,especially in people with non-large vessel stenosis.The underlying small vessel change in RNFL and CI should be investigated in the future. 展开更多
关键词 Retinal nerve fiber layer Cognitive impairment Polyvascular abnormality
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Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network
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作者 Tingting Su Jia Wang +2 位作者 Wei Hu Gaoqiang Dong Jeon Gwanggil 《Computers, Materials & Continua》 SCIE EI 2024年第6期4433-4448,共16页
Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportati... Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%. 展开更多
关键词 abnormal network traffic deep learning residual network multi-scale feature extraction max-feature-map
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Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder
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作者 Haoyi Zhong Yongjiang Zhao Chang Gyoon Lim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第8期1757-1781,共25页
This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(... This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data. 展开更多
关键词 Lithium-ion battery abnormal state detection autoencoder virtual power plants LSTM
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Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios
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作者 Changyu Liu Hao Huang +2 位作者 Guogang Huang Chunyin Wu Yingqi Liang 《Computers, Materials & Continua》 SCIE EI 2024年第9期4219-4242,共24页
Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method ca... Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety. 展开更多
关键词 Parameter-efficient transfer learning laboratory scenarios TubeRAPT abnormal action detection
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Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding
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作者 Yuanyao Lu Wei Chen +2 位作者 Zhanhe Yu Jingxuan Wang Chaochao Yang 《Computers, Materials & Continua》 SCIE EI 2024年第6期5051-5066,共16页
With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical chall... With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models. 展开更多
关键词 Vehicle abnormal behavior deep learning ResNet dense block soft thresholding
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Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene
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作者 Yunfeng Cai Ran Qin +3 位作者 Jin Tang Long Zhang Xiaotian Bi Qing Yang 《Computers, Materials & Continua》 SCIE EI 2024年第6期4979-4994,共16页
Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(... Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training. 展开更多
关键词 abnormal action recognition action recognition lightweight pose estimation electric power training
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First record of abnormal body coloration in a rockfish Sebastes koreanus(Scorpaenoidei:Sebastidae)from coastal water of China based on morphological characteristics and DNA barcoding
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作者 Ang LI Huan WANG +1 位作者 Changting AN Shufang LIU 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2024年第2期640-646,共7页
The first record of abnormal body coloration in Sebastes koreanus Kim and Lee,1994,from the Yellow Sea of China,was documented based on morphological characteristics and DNA barcoding.The two rockfish specimens were c... The first record of abnormal body coloration in Sebastes koreanus Kim and Lee,1994,from the Yellow Sea of China,was documented based on morphological characteristics and DNA barcoding.The two rockfish specimens were collected from the coastal waters of Qingdao,China,and the whole body and all fins of them were red.Of the two red-colored rockfish,there were tiny deep red spots on each fin,2 red radial stripes behind and below the eyes and 1 large deep red blotch on the opercula,while the similar stripe and spot patterns are also present in the S.koreanus specimens with normal body coloration.The countable characteristics of the two specimens are in the range of the morphometry of S.koreanus.To further clarify the species identity and taxonomic status of the two specimens,DNA barcode analysis was carried out.The genetic distance between the red-colored rockfish and S.koreanus was 0,and the minimum net genetic distances between the red-colored rockfish and other Sebastes species except for S.koreanus were 3.0%,which exceeds the threshold of species delimitation.The phylogenetic analysis showed that the DNA barcoding sequences of the two red-colored rockfish clustered with the S.koreanus sequences.The above results of DNA barcode analysis also support that the two red-colored rockfish could be identified as the species of S.koreanus.The mechanism of color variation in S.koreanus is desirable for further research and the species could be an ideal model to study the color-driven speciation of the rockfishes. 展开更多
关键词 abnormal body coloration Sebastes koreanus coastal water of China Yellow Sea morphological characteristics DNA barcoding
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A Clinical Study to Assess the Effectiveness of Oral Combination Kit Therapy in Syndromic Management of Abnormal Vaginal Discharge (FEMINE Study) in Kinshasa, Democratic Republic of Congo
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作者 Feruzi Michel Mangala Muela Andy Mbangama +10 位作者 Di-Mosi-Nkoy Roger Wumba Ambis Joëlle Lumaya Nkongolo Freddy Muamba Tshitadi Jean Mukendi Ndombasi Neilda Lemba Otem Christian Ndesanzim Nkashama Bienvenu Kazadi Banza Jesual Lotoy Umba Adrien Tandu Mushengezi Dieudonné Sengeyi Mwimba Roger Mbungu 《Open Journal of Obstetrics and Gynecology》 2024年第1期193-208,共16页
Background: Vaginal discharge is one of most common and nagging problems that women face. About 20% - 25% of women who visit gynecology department complain of vaginal discharge and leucorrhoea. An orally administered ... Background: Vaginal discharge is one of most common and nagging problems that women face. About 20% - 25% of women who visit gynecology department complain of vaginal discharge and leucorrhoea. An orally administered combination kit, containing 2 g secnidazole, 1 g azithromycin and 150 mg fluconazole (Azimyn FS Kit), has been successfully evaluated in clinical trials and used in several countries for management syndromic vaginal discharge due to infections. Methods: This is a longitudinal study which aimed to verify the clinical efficacy of the combined oral kit containing secnidazole, azithromycin and fluconazole (Azimyn FS Kit<sup><sup>®</sup></sup>) in the syndromic treatment of abnormal vaginal discharge in patients received in outpatient consultations in Kinshasa/DR Congo from March to September 2023. Results: Majority of patients had whitish vaginal discharge (51.6%) of average abundance (56.2%), accompanied by pruritus in 72.1% of cases, and dyspareunia in 23.5% of cases and hypogastralgia in 40.2% of cases. One week after treatment with the Azimyn FS<sup><sup>®</sup></sup> combined kit, at the greatest majority of patients (97.3%), abnormal vaginal discharge had decreased by more than 50% (84.1%). Two weeks after treatment with the Azimyn FS<sup><sup>®</sup></sup> combined kit, almost all patients (97.3%) no longer had abnormal vaginal discharge which had completely disappeared. Conclusion: A single dose of secnidazole, azithromycin and fluconazole in the form of an oral combi-kit (Azimyn FS Kit) has shown excellent therapeutic effectiveness in the syndromic treatment of abnormal vaginal discharge wherein patients were treated without diagnostic confirmation. 展开更多
关键词 Oral Combination Kit Therapy Syndromic Management abnormal Vaginal Discharge
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High-grade serous carcinoma of the fallopian tube in a young woman with chromosomal 4q abnormality:A case report
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作者 Kai-Cheng Zhang Shao-Yin Chu Dah-Ching Ding 《World Journal of Clinical Cases》 SCIE 2024年第18期3539-3547,共9页
BACKGROUND Few studies have reported an association between an increased risk of acquiring cancers and survival in patients with 4q deletion syndrome.This study presents a rare association between chromosome 4q abnorm... BACKGROUND Few studies have reported an association between an increased risk of acquiring cancers and survival in patients with 4q deletion syndrome.This study presents a rare association between chromosome 4q abnormalities and fallopian tube highgrade serous carcinoma(HGSC)in a young woman.CASE SUMMARY A 35-year-old woman presented with acute dull abdominal pain and a known chromosomal abnormality involving 4q13.3 duplication and 4q23q24 deletion.Upon arrival at the emergency room,her abdomen appeared ovoid and distended with palpable shifting dullness.Ascites were identified through abdominal ultrasound,and computed tomography revealed an omentum cake and an enlarged bilateral adnexa.Blood tests showed elevated CA-125 levels.Paracentesis was conducted,and immunohistochemistry indicated that the cancer cells favored an ovarian origin,making us suspect ovarian cancer.The patient underwent debulking surgery,which led to a diagnosis of stage IIIC HGSC of the fallopian tube.Subsequently,the patient received adjuvant chemotherapy with carboplatin and paclitaxel,resulting in stable current condition.CONCLUSION This study demonstrates a rare correlation between a chromosome 4q abnormality and HGSC.UBE2D3 may affect crucial cancer-related pathways,including P53,BRCA,cyclin D,and tyrosine kinase receptors,thereby possibly contributing to cancer development.In addition,ADH1 and DDIT4 may be potential influencers of both carcinogenic and therapeutic responses. 展开更多
关键词 High-grade serous carcinoma Fallopian tube Young age Chromosomal abnormality Mental retardation AGING Case report
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Analysis of the Efficacy of Prenatal B-Ultrasound in Diagnosing Fetal Abnormalities
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作者 Juan Chen Qiumin Li 《Journal of Clinical and Nursing Research》 2024年第5期47-51,共5页
Objective:To explore the positive significance of using prenatal B-ultrasound in diagnosing fetal abnormalities.Methods:A total of 200 pregnant women who visited Shaanxi Provincial People’s Hospital between January 2... Objective:To explore the positive significance of using prenatal B-ultrasound in diagnosing fetal abnormalities.Methods:A total of 200 pregnant women who visited Shaanxi Provincial People’s Hospital between January 2023 and January 2024 were recruited as the research subjects.All pregnant women received prenatal examinations.A retrospective analysis was carried out to analyze the positive significance of prenatal B-ultrasound examination in the diagnosis of fetal abnormalities.Results:Prenatal B-ultrasound examination detected 10 cases of fetal abnormalities,with a detection rate of 5.00%.When compared with the postnatal examination results of 5.50%,the difference was insignificant(P>0.05).Moreover,comparing the fetal limb abnormalities and cardiovascular abnormalities in prenatal B-ultrasound examination and postnatal examination,one case of congenital heart disease was missed in the prenatal B-ultrasound examination,and the others were consistent with the postnatal examination results,with a coincidence rate of 90.91%,indicating a high compliance rate.Conclusion:Fetal abnormalities have a great impact on mothers,babies,and families,and it is particularly important to strengthen diagnosis during this process.Prenatal B-ultrasound examination can improve the accuracy of diagnosis of fetal abnormalities and can be promoted in clinical practice as a basis for screening fetal abnormalities. 展开更多
关键词 Prenatal B-ultrasound Fetal abnormalities Diagnostic value
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云南地区GNSS应变率场异常识别方法及地震预测效能评估
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作者 王伶俐 洪敏 +3 位作者 牛甜 李芹 杨薪俊 喻世贤 《地震研究》 北大核心 2025年第1期49-60,共12页
利用1999-2020年云南及邻区近300个GNSS测站的观测数据解算获取的速度场为约束,采用克里金插值方法分时段估计了1999-2007年,2009-2014年,2015-2020年三期区域应变率场;通过回溯各个观测时段之后3年内M_(S)≥5.0地震事件,分析区域地壳... 利用1999-2020年云南及邻区近300个GNSS测站的观测数据解算获取的速度场为约束,采用克里金插值方法分时段估计了1999-2007年,2009-2014年,2015-2020年三期区域应变率场;通过回溯各个观测时段之后3年内M_(S)≥5.0地震事件,分析区域地壳形变特征与地震事件发生地点之间的相关性,结果表明,绝大部分地震都发生在面应变高梯度带的张压转换区和最大剪应变率沿断层方向的高值区或其边缘。基于上述应变率场异常特征,提出格网地震危险因子算法,建立地震危险区识别模型,通过估计格网最大剪应变率和面应变率风险区划因子,定量提取异常区地震危险指标,结果显示采用数值模型识别出的异常区与地震事件具有较好地对应关系;进一步采用R值评分的方式对应变率场异常区模型识别方法进行地震预测效能量化评估与分析,结果显示3期应变率场预测结果均通过R值评分检测。 展开更多
关键词 GNSS 应变率场 地震预测 R值评分 异常指标 云南地区
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免疫异常数据的金属回流双极直流配电线路状态估计保护方法
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作者 曾琦 曾维刚 +4 位作者 廖建权 王少雄 郑宗生 王渝红 周念成 《电力自动化设备》 北大核心 2025年第1期16-24,共9页
实际工程中的量测可能存在异常数据干扰,增加保护误动的风险。为此,基于模型匹配的思想,提出一种免疫异常数据的直流配电线路状态估计保护方法。考虑金属回流双极直流线路的极间耦合,建立线路的精细化等值模型。据此得到系统的量测方程... 实际工程中的量测可能存在异常数据干扰,增加保护误动的风险。为此,基于模型匹配的思想,提出一种免疫异常数据的直流配电线路状态估计保护方法。考虑金属回流双极直流线路的极间耦合,建立线路的精细化等值模型。据此得到系统的量测方程,并根据二次积分法将其离散化以便于求解。对于可能存在的异常数据问题,提出基于窗口图傅里叶变换对数据进行预处理,将数据视为图信号并赋予“频率”的概念,通过提取低频信号达到剔除随机脉冲等高频异常数据的目的。基于递推最小二乘算法对预处理后的状态估计模型进行求解,根据估计模型和实测模型的匹配度构建保护判据,实现区内和区外故障的识别。仿真结果表明,该方法可快速、准确识别区内故障,并有效避免异常数据干扰,同时具有较强的耐高阻、抗通信延时等性能。 展开更多
关键词 直流配电 线路保护 异常数据 图傅里叶变换 状态估计 递推最小二乘
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光伏发电机组异动信息主动增量式更新算法
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作者 王晖 赵咨钧 +1 位作者 管保晋 曲诺亚 《电子设计工程》 2025年第1期132-136,共5页
针对进行光伏发电机组密集型异动信息更新操作时,出现更新时延长、更新结果不完整的问题,提出了光伏发电机组异动信息主动增量式更新算法。通过特征提取、小波变换和模极大值计算来获取有效的增量式特征;构建增量特征成词袋模型并进行标... 针对进行光伏发电机组密集型异动信息更新操作时,出现更新时延长、更新结果不完整的问题,提出了光伏发电机组异动信息主动增量式更新算法。通过特征提取、小波变换和模极大值计算来获取有效的增量式特征;构建增量特征成词袋模型并进行标注;结合蚁群算法搜索异动信息,计算最小平均带宽和最佳更新路径,通过动态更新相容类和决策类异动信息完善数据;采用2阶段集合式更新方案进行信息增量收集和校验增量分发,以获取主动增量式更新异动信息。由实验结果可知,该算法更新时延在全部校验节点下未超过40 s,且能够保证更新结果具有完整性。 展开更多
关键词 光伏发电机组 异动信息 主动增量式 更新
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