Improving Federated Learning through Abnormal Client Detection and Incentive

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.

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

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.

Relationship of Retinal Nerve Fiber Layer Thickness and Retinal Vessel Calibers with Cognitive Impairment in the Asymptomatic Polyvascular Abnormalities Population

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.

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

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%.

Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding

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.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

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.

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

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.

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

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®) 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® 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® 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.

Analysis of the Efficacy of Prenatal B-Ultrasound in Diagnosing Fetal Abnormalities

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.

Tooth number abnormality:from bench to bedside

Tooth number abnormality is one of the most common dental developmental diseases,which includes both tooth agenesis and supernumerary teeth.Tooth development is regulated by numerous developmental signals,such as the well-known Wnt,BMP,FGF,Shh and Eda pathways,which mediate the ongoing complex interactions between epithelium and mesenchyme.Abnormal expression of these crutial signalling during this process may eventually lead to the development of anomalies in tooth number;however,the underlying mechanisms remain elusive.In this review,we summarized the major process of tooth development,the latest progress of mechanism studies and newly reported clinical investigations of tooth number abnormality.In addition,potential treatment approaches for tooth number abnormality based on developmental biology are also discussed.This review not only provides a reference for the diagnosis and treatment of tooth number abnormality in clinical practice but also facilitates the translation of basic research to the clinical application.

Coagulation abnormalities and their relationship with bleeding manifestations in patients with dengue-A single center observational study

Objective:To evaluate coagulation abnormalities and their relationship with bleeding manifestations among patients with dengue.Methods:This observational study was conducted on 292 adult dengue patients who were admitted to a tertiary care hospital of Western India from July 2021 to June 2022.Coagulation tests including prothrombin time(PT),international normalized ratio(INR),activated partial thromboplastin time(aPTT),fibrinogen,and D-dimer were performed.Patients were monitored for bleeding manifestations.Results:Coagulation abnormalities were reported in 42.8%of the patients.Overall,prolonged aPTT was the most common coagulation abnormality(40.8%),followed by low fibrinogen(38.7%),raised D-dimer(31.2%),raised INR(26.0%)and prolonged PT(19.2%).Bleeding manifestations were present in 19.9%patients.PT,INR,aPTT and D-dimer levels were significantly higher(P<0.01)and fibrinogen level was significantly lower(P<0.001)in patients with bleeding compared to patients without bleeding.Patients with bleeding had a significantly higher rate of all coagulation abnormalities than patients without bleeding(P<0.01).Conclusions:Patients with bleeding showed a significantly higher frequency of coagulation abnormalities compared to patients without bleeding.Patients with dengue should be assessed for coagulation abnormalities.

Analysis of Etiology,Diagnosis,and Treatment Strategy and Efficacy of Pulsatile Tinnitus Caused by Abnormal Vascular Anatomy

Objective To investigate the etiology,clinical characteristics,diagnosis,and treatment strategies and efficacy of pulsatile tinnitus(PT)caused by vascular anatomy abnormality.Methods The clinical data of 45 patients with PT in our hospital from 2012 to 2019 were collected and retrospectively analyzed.Results All 45 patients had vascular anatomical abnormalities.The patients were divided into 10 categories according to the different locations of vascular abnormalities:sigmoid sinus diverticulum(SSD),sigmoid sinus wall dehiscence(SSWD),SSWD with high jugular bulb,pure dilated mastoid emissary vein,aberrant internal carotid artery(ICA)in the middle ear,transverse-sigmoid sinus(TSS)transition stenosis,TSS transition stenosis with SSD,persistent occipital sinus stenosis,petrous segment stenosis of ICA,and dural arteriovenous fistula.All patients complained of PT synchronous with heartbeat rhythm.Endovascular interventional therapy and extravascular open surgery were used according to the location of the vascular lesions.Tinnitus disappeared in 41 patients,was significantly relieved in 3 patients,and was unchanged in 1 patient postoperatively.Except for one patient with transient headache postoperatively,no obvious complications occurred.Conclusion PT caused by vascular anatomy abnormalities can be identified by detailed medical history and physical and imaging examination.PT can be relieved or even completely alleviated after appropriate surgical treatments.

Prevalence of Ventilatory Function Abnormalities in Residents of Attecoube Lagune, Abidjan (Côte d’Ivoire)

Introduction: Studies of abnormal ventilatory function in Côte d’Ivoire have been carried out in the workplace and in schools. The objective of this study was to determine the prevalence of respiratory symptoms and ventilatory function abnormalities in the population of the lagoon district of Attécoubé in Abidjan. Material and Methods: A cross-sectional study was carried out on 170 people in the municipality of Attécoubé Lagune. A questionnaire was used to collect information on sociodemographic, clinical, and environmental characteristics. A basic spirometry and a beta mimetic test were carried out on all the subjects surveyed. Data analysis was done with the stata 15.1 software. Results: The study population was composed of 103 women and 67 men with a sex ratio (M/F) of 0.65. The average age was 35.92 ± 15.28 years. The most frequent respiratory symptoms were chest tightness (29.41%), dyspnea (28.82%), sneezing (22.94%) and cough (22.35%). The prevalence of ventilatory function abnormalities was 43.24% among residents of Attécoubé Lagune and the most frequent abnormality was ventilatory restriction (35.15%) followed by obstruction (4.85%). The risk factor for ventilatory function abnormalities was heavy pollution [OR = 2.66;IC: 1.053 - 6.743;P = 0.038]. Conclusion: Residents of the Attécoubé Lagune district had many respiratory symptoms and a high prevalence of ventilatory function abnormalities. Improving air quality is urgently needed in this municipality.

Correlation between Reasons for Prescription and Karyotype Results in Patients Referred for Suspected Chromosomal Abnormalities

Karyotype prescription is based on clinical signs (or reasons for karyotype prescription) which are phenotypic manifestations associated with chromosomal abnormalities. The aim of this study was to establish a correspondence between karyotype indications and their results in patients. This was a retrospective study that was carried out in the Histology-Embryology-Cytogenetics laboratory of the University Hospital of Cocody-Abidjan from 2014 to 2019. 58 patient files were identified and included the indication or reason for prescribing a constitutional karyotype and the biological result obtained. An individual data sheet was used to collect the data. 17 reasons for prescription were identified and divided into 2 groups. Sexual ambiguity was the most frequent reason (29.3%). The first group (G1) represented the 10 reasons for which the karyotype results were normal. The second group (G2) corresponded of the 7 motives with normal or abnormal karyotype results. Several anomalies were listed according to these reasons: inversions, mosaics (anomalies of number and structure) and trisomy 21. The last was the most frequent chromosomal anomaly (69.24%). It was found in several reasons for karyotype prescription: malformations, neurological disorders, suspected trisomy and cardiac pathology. Several factors could explain these results, among which are the limits of the karyotype and the non-genetic causes that can induce these abnormal phenotypes. Complementary examinations to the karyotype are molecular cytogenetic techniques, notably fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (Array-CGH).

GRU-based Buzzer Ensemble for Abnormal Detection in Industrial Control Systems

Recently,Industrial Control Systems(ICSs)have been changing from a closed environment to an open environment because of the expansion of digital transformation,smart factories,and Industrial Internet of Things(IIoT).Since security accidents that occur in ICSs can cause national confusion and human casualties,research on detecting abnormalities by using normal operation data learning is being actively conducted.The single technique proposed by existing studies does not detect abnormalities well or provide satisfactory results.In this paper,we propose a GRU-based Buzzer Ensemble for AbnormalDetection(GBE-AD)model for detecting anomalies in industrial control systems to ensure rapid response and process availability.The newly proposed ensemble model of the buzzer method resolves False Negatives(FNs)by complementing the limited range that can be detected in a single model because of the internal models composing GBE-AD.Because the internal models remain suppressed for False Positives(FPs),GBE-AD provides better generalization.In addition,we generated mean prediction error data in GBE-AD and inferred abnormal processes using soft and hard clustering.We confirmed that the detection model’s Time-series Aware Precision(TaP)suppressed FPs at 97.67%.The final performance was 94.04%in an experiment using anHIL-basedAugmented ICS(HAI)Security Dataset(ver.21.03)among public datasets.

Abnormal Flight Passenger Recovery Algorithm Based on Itinerary Acceptance

Under the background of the rapid development of the air transport industry, the abnormal phenomenon of flights has become increasingly serious due to various factors such as the gradual reduction of resources, adverse climatic conditions, problems in air traffic control and mechanical failures. In order to reduce losses, it has become a major problem for airlines to use optimization algorithm to study the recovery of abnormal flights. By upgrading the passenger recovery engine, the purpose of this paper is to provide the optimal recovery scheme for passengers, so as to reduce the risk of transferring overseas flights, and thus reduce the economic loss of airlines. In this paper, the optimization model and algorithm based on network flow, combined with actual business requirements, comprehensively consider multiple optimization objectives to quickly generate passenger recovery solutions, and at the same time achieve the optimal income of airlines and the acceptance rate of passenger recovery, so as to balance the two. The practicability and effectiveness of the proposed model and algorithm are proved by some concrete examples.

Rationale of Cross-Sectional Descriptive Study on Clinical Effectiveness of Oral Combination Therapy Based on Secnidazole, Azithromycin and Fluconazole in Syndromic Treatment of Abnormal Vaginal Discharge in Kinshasa/DR Congo

Background: Vaginal discharge is one of the most common troubles faced by childbearing age women. About 20% - 25% of women who visit service of gynecology complain of vaginal discharge and leucorrhoea. Management of vaginal discharge in low-income countries generally depends on syndromic approach, which limits the understanding of specific responsible agents. Thus targeted management is based on the identification of causal organism and targeting of therapy against it, while syndromic management is based on presence of high risk factors. Thus the oral combination kit (Azimyn FS Kit®) offers convenience of a one-day treatment compared to other multidose treatments, which will also ensure high patient adherence to treatment, thus increasing chances of desired results. Due to its widespread use, it is proposed to evaluate the effectiveness of this oral association kit therapy in management of vaginal discharge in the population of our milieu in the Democratic Republic of Congo (DRC) particularly those received in outpatient consultation in some medical facilities in city of Kinshasa. Expensive laboratory tests and the associated waiting period for result mean that patient remains without treatment while waiting for test results. Therefore, by adopting a syndromic management approach, patient’s eligibility for treatment will be decided based on abnormal vaginal discharge, their characteristics, severity and other presentations symptomatic. This approach will also avoid losing sight of patients during follow-up and will help to reduce financial burden for patients. Objectives: To determine the efficacy and safety of oral combination kit therapy containing secnidazole, azithromycin and fluconazole (Azimyn FS Kit®) in syndromic treatment of abnormal vaginal discharge in patients received in outpatient consultation in some medical facilities in the city of Kinshasa;to measure rate of recurrence of abnormal vaginal discharge in these patients. And to identify the adverse effects observed in these patients who received treatment with the combined oral kit containing secnidazole, azithromycin and fluconazole (Azimyn FS Kit®) in outpatient consultation in some medical facilities in the city of Kinshasa. Methods: It will be a cross-sectional descriptive study. Sample size will be 319 women of childbearing age who consult the gynecology department with complaint of abnormal vaginal discharge and suspicion of vaginal infection, who agree to abstain from sex during treatment and who have given their written consent to use their personal and/or health data in the study. Conclusion: A study on clinical efficacy of oral combination therapy based on secnidazole, azithromycin and fluconazole is beneficial.

Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

Abnormal Uterine Bleeding due to Retention of Fetal Bones after Abortion: A Case Report

Background: Retention of fetal bones is a rare cause of abnormal uterine bleeding. Others may present as subfertility, chronic pelvic pain, abnormal vaginal discharge, menometrorrhagia, dysmenorrhea and spontaneous expulsion of bony fragments. Incidence is 0.26% among patients undergoing hysteroscopy. Aim: To document a pattern of presentation of retained fetal bone and its management. Case Presentation: Our patient is an 18-year old who presented with vaginal bleeding of four weeks duration and managed for abnormal uterine bleeding due to retained fetal bone following second trimester abortion. Conclusion: The use of transvaginal ultrasound in making diagnosis of retained fetal bone is effective. Treatment by removal of bones through evacuation by dilatation and curettage or hysteroscopy brings about resolution of symptoms. Use of pelvic ultrasonography to confirm complete evacuation of the uterus after abortion especially second trimester abortion could aid in early diagnosis and management of incomplete abortion.

A Qualitative Study on the Psychological Experience of Pregnant Women with Abnormal Induced Abortion of Fetuses under 20 Weeks of Pregnancy

Objective: This paper aims to understand the psychological experience and needs of pregnant women who need induction of labor due to fetal abnormalities at less than 20 weeks of gestation through in-depth interviews. Methods: Using the phenomenological research method in qualitative research, 12 pregnant women with fetal abnormalities less than 20 weeks of gestation in the obstetrics department of a hospital from January to April 2023 were selected as the research objects for semi-structured in-depth interviews. After information was collected to saturation, Colaizzi content analysis method was used to sort out, analyze and refine the data. Results: Through the interview results, three themes were refined from the transcript. Those are: 1) the support and assistance needed during the induction of labor;2) inner feelings after labor induction;3) The vision of future life. Conclusion: Abnormal fetal development at <20 weeks of gestation can cause anxiety, depression, guilt, loneliness and other negative emotions for pregnant women, and they need to get corresponding psychological support, especially during the period from fetal abnormality to induction of labor, pregnant women have to experience many physical and mental suffering, and need to get the care of family, relatives, friends, medical staff and society. Medical staff can help them seek multi-directional support, provide professional knowledge, and help them with self-management.