Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved s...Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved segmented belief propagation list decoding based on bit flipping(SBPL-BF) is proposed. On the one hand, the proposed algorithm makes use of the cooperative characteristic in BPL decoding such that the codeword is decoded in different BP decoders. Based on this characteristic, the unreliable bits for flipping could be split into multiple subblocks and could be flipped in different decoders simultaneously. On the other hand, a more flexible and effective processing strategy for the priori information of the unfrozen bits that do not need to be flipped is designed to improve the decoding convergence. In addition, this is the first proposal in BPL decoding which jointly optimizes the bit flipping of the information bits and the code bits. In particular, for bit flipping of the code bits, a H-matrix aided bit-flipping algorithm is designed to enhance the accuracy in identifying erroneous code bits. The simulation results show that the proposed algorithm significantly improves the errorcorrection performance of BPL decoding for medium and long codes. It is more than 0.25 d B better than the state-of-the-art BPL decoding at a block error rate(BLER) of 10^(-5), and outperforms CA-SCL decoding in the low signal-to-noise(SNR) region for(1024, 0.5)polar codes.展开更多
In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling struct...In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,theDensityPeakClustering(DPC)algorithmis used todetermine referential values of indicators forBRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.展开更多
Faced with autism, motherhood and parenthood are turned upside down in many ways. Coping with stress and mental health problems, continuing to assume a rewarding parental role and finding suitable care are the trials ...Faced with autism, motherhood and parenthood are turned upside down in many ways. Coping with stress and mental health problems, continuing to assume a rewarding parental role and finding suitable care are the trials and tribulations that mark out the journey of African parents. Faith and belief have been described as providing meaning and coping mechanisms in the frightening ordeal of disability. An encounter with a young girl and her parents provided an opportunity to analyse the mother’s experience and the impact of beliefs and discourses on her commitment to care. Based on this clinical story, we discuss the place of other-actors (parents and carers) and the Other-God in relation to the psychopathological dynamics of the mother.展开更多
Objective:To evaluate the application effect of enteral and parenteral nutrition therapy combined with a health belief education model in patients with inflammatory bowel disease.Methods:80 patients with inflammatory ...Objective:To evaluate the application effect of enteral and parenteral nutrition therapy combined with a health belief education model in patients with inflammatory bowel disease.Methods:80 patients with inflammatory bowel disease admitted to the Shanghai Zhangjiang Institute of Medical Innovation were chosen.This study was carried out from August 2022 to October 2023.The patients were randomly divided into a study group(40 cases)and a control group(40 cases).The treatment plan for the control group was the conventional treatment model,while the treatment plan for the study group was to provide enteral and parenteral nutrition therapy combined with a health belief education model based on the control group.The efficacy of both groups was compared.Results:In the study group,the therapeutic effect for 31 patients(77.50%)was markedly effective and 7 was effective(17.50%),accounting for 95.0%of the total,which was higher than the control group at 80.0%(P<0.05).The relief time of relevant symptoms in the study group was shorter than that of the control group(P<0.05).Before treatment,there were no differences in the high-sensitivity C-reactive protein(hs-CRP),interleukin 10(IL-10),and tumor necrosis factor-α(TNF-α)between both groups(P>0.05).After treatment,the levels of inflammatory factors in the study group(hs-CRP(8.02±1.13)mg/L,IL-10(9.24±1.25)pg/mL,and TNF-α(7.19±1.04)ng/L)were lower than those in the control group(P<0.05).Conclusion:Enteral and parenteral nutritional therapy combined with a health belief education model showed significant efficacy in inflammatory bowel disease patients.Patient symptoms were relieved and inflammatory reactions were reduced.This method is worthy of popularization.展开更多
A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure t...A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process.Therefore,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and knowledge.Moreover,an innovative whale optimization algorithm with interpretable constraints is proposed.The experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform.The MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,respectively.It can be seen that the proposed model also shows a better ability to handle small sample data.Meanwhile,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i model.Experimental results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.展开更多
Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model.Due to the understan...Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model.Due to the understandable knowledge expression and transparent reasoning process,the belief rule base(BRB)has extensive applications as an interpretable expert system in fault diagnosis.Optimization is an effective means to weaken the subjectivity of experts in BRB,where the interpretability of BRB may be weakened.Hence,to obtain a credible result,the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed,which are manifested in deviation from the initial judgement of experts and over-optimization of parameters.For these two factors,three indexes are proposed,namely the consistency index of rules,consistency index of the rule base and over-optimization index,tomeasure the interpretability of the optimizedmodel.Considering both the accuracy and interpretability of amodel,an improved coordinate ascent(I-CA)algorithmis proposed to fine-tune the parameters of the fault diagnosis model based on BRB.In I-CA,the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search algorithm.Furthermore,the random optimization sequence and adaptive step size are proposed to improve the accuracy of the model.Finally,a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.展开更多
Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in...Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.展开更多
Belief propagation(BP)decoding outputs soft information and can be naturally used in iterative receivers.BP list(BPL)decoding provides comparable error-correction performance to the successive cancellation list(SCL)de...Belief propagation(BP)decoding outputs soft information and can be naturally used in iterative receivers.BP list(BPL)decoding provides comparable error-correction performance to the successive cancellation list(SCL)decoding.In this paper,we firstly introduce an enhanced code construction scheme for BPL decoding to improve its errorcorrection capability.Then,a GPU-based BPL decoder with adoption of the new code construction is presented.Finally,the proposed BPL decoder is tested on NVIDIA RTX3070 and GTX1060.Experimental results show that the presented BPL decoder with early termination criterion achieves above 1 Gbps throughput on RTX3070 for the code(1024,512)with 32 lists under good channel conditions.展开更多
Orthogonal Time Frequency Space(OTFS)signaling with index modulation(IM)is a promising transmission scheme characterized by high transmission efficiency for high mobility scenarios.In this paper,we study the receiver ...Orthogonal Time Frequency Space(OTFS)signaling with index modulation(IM)is a promising transmission scheme characterized by high transmission efficiency for high mobility scenarios.In this paper,we study the receiver for coded OTFS-IM system.First,we construct the corresponding factor graph,on which the structured prior incorporating activation pattern constraint and channel coding is devised.Then we develop a iterative receiver via structured prior-based hybrid belief propagation(BP)and expectation propagation(EP)algorithm,named as StrBP-EP,for the coded OTFS-IM system.To reduce the computational complexity of discrete distribution introduced by structured prior,Gaussian approximation conducted by EP is adopted.To further reduce the complexity,we derive two variations of the proposed algorithm by using some approximations.Simulation results validate the superior performance of the proposed algorithm.展开更多
The error correction performance of Belief Propagation(BP)decoding for polar codes is satisfactory compared with the Successive Cancellation(SC)decoding.Nevertheless,it has to complete a fixed number of iterations,whi...The error correction performance of Belief Propagation(BP)decoding for polar codes is satisfactory compared with the Successive Cancellation(SC)decoding.Nevertheless,it has to complete a fixed number of iterations,which results in high computational complexity.This necessitates an intelligent identification of successful BP decoding for early termination of the decoding process to avoid unnecessary iterations and minimize the computational complexity of BP decoding.This paper proposes a hybrid technique that combines the“paritycheck”with the“G-matrix”to reduce the computational complexity of BP decoder for polar codes.The proposed hybrid technique takes advantage of the parity-check to intelligently identify the valid codeword at an early stage and terminate the BP decoding process,which minimizes the overhead of the G-matrix and reduces the computational complexity of BP decoding.We explore a detailed mechanism incorporating the parity bits as outer code and prove that the proposed hybrid technique minimizes the computational complexity while preserving the BP error correction performance.Moreover,mathematical formulation for the proposed hybrid technique that minimizes the computation cost of the G-matrix is elaborated.The performance of the proposed hybrid technique is validated by comparing it with the state-of-the-art early stopping criteria for BP decoding.Simulation results show that the proposed hybrid technique reduces the iterations by about 90%of BP decoding in a high Signal-to-Noise Ratio(SNR)(i.e.,3.5~4 dB),and approaches the error correction performance of G-matrix and conventional BP decoder for polar codes.展开更多
Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the model...Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.展开更多
Religion is one of the social entities that has had a significant impact on the pandemic.The study’s goals are to investigate the relationship between well-being and fear of COVID-19,as well as to test whether religi...Religion is one of the social entities that has had a significant impact on the pandemic.The study’s goals are to investigate the relationship between well-being and fear of COVID-19,as well as to test whether religious beliefs mediate the effect of wellbeing on fear of COVID-19.The sample comprised of 433 participants in Vietnam.Independent Sample t-Test,One-way ANOVA,mediation analysis were used to analyze the data.In the levels of well-being,individuals who engage in religious services daily have higher levels than those hardly and never attend,and people from the age of 18 to 30 have higher levels than individuals from 31 to above 60 years.In addition,people aged from 51 to above 60 have higher levels of religious beliefs than people aged from 18 to 50.Females experience more fear of COVID-19 compared to males.The latter illustrates that religious beliefs mediate the effect of well-being on fear of COVID-19.Social workers and clinicians must prioritize older adults and people with chronic diseases for early mental interventions,and they should be aware of the role of religion in psychological treatment integration.展开更多
BACKGROUND Medication misuse or overuse is significantly associated with poor health outcomes.Information regarding the knowledge,cultural beliefs,and behavior about medication safety in the general population is impo...BACKGROUND Medication misuse or overuse is significantly associated with poor health outcomes.Information regarding the knowledge,cultural beliefs,and behavior about medication safety in the general population is important.AIM To conduct a survey on medication habits and explored the potential factors impacting medication safety.METHODS The current survey included adults from 18 districts and counties in Harbin,China.A questionnaire on medication safety was designed based on knowledge,cultural beliefs,and behavior.Both univariate and multivariate analyses were used to explore the factors that impacted medication safety.RESULTS A total of 394 respondents completed the questionnaires on medication safety.The mean scores for knowledge,cultural beliefs,and behavior about medication safety were 59.41±19.33,40.66±9.24,and 60.97±13.69,respectively.The medication knowledge score was affected by age(P=0.044),education(P<0.001),and working status(P=0.015).Moreover,the cultural beliefs score was significantly affected by education(P<0.001).Finally,education(P=0.003)and working status(P=0.011)significantly affected the behavior score.CONCLUSION The knowledge,cultural beliefs,and behavior about medication safety among the general population was moderate.Health education should be provisioned for the elderly,individuals with a low education level,and the unemployed to improve medication safety in Harbin,China.展开更多
Deep learning is the subset of artificial intelligence and it is used for effective decision making.Wireless Sensor based automated irrigation system is proposed to monitor and cultivate crop.Our system consists of Dis...Deep learning is the subset of artificial intelligence and it is used for effective decision making.Wireless Sensor based automated irrigation system is proposed to monitor and cultivate crop.Our system consists of Distributed wire-less sensor environment to handle the moisture of the soil and temperature levels.It is automated process and useful for minimizing the usage of resources such as water level,quality of the soil,fertilizer values and controlling the whole system.The mobile app based smart control system is designed using deep belief network.This system has multiple sensors placed in agriculturalfield and collect the data.The collected transmitted to cloud server and deep learning process is applied for making decisions.DeepQ residue analysis method is proposed for analyzing auto-mated and sensor captured data.Here,we used 512×512×3 layers deep belief network and 10000 trained data and 2500 test data are taken for evaluations.It is automated process once data is collected deep belief network is generated.The performance is compared with existing results and our process method has 94%of accuracy factor.Also,our system has low cost and energy consumption also suitable for all kind of agriculturalfields.展开更多
Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the mil...Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.展开更多
Cancer disease is a deadliest disease cause more dangerous one.By identifying the disease through Artificial intelligence to getting the mage features directly from patients.This paper presents the lung knob division ...Cancer disease is a deadliest disease cause more dangerous one.By identifying the disease through Artificial intelligence to getting the mage features directly from patients.This paper presents the lung knob division and disease characterization by proposing an enhancement calculation.Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification.This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy.To resolve this problem,to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor.The general technique of the created approach includes four stages,such as pre-processing,segmentation,highlight extraction,and the order.From the outset,the Computerized Tomography(CT)image of the lung is taken care of to the division.When the division is done,the highlights are extricated through morphological factors for feature observation.By getting the features are analysed and the characterization is done dependent on the Deep Belief Network(DBN)which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm(CSCA)which distinguish the lung tumour,giving two classes in particular,knob or non-knob.The proposed system produce high performance as well compared to the other system.The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity,precision,affectability,and the explicitness.展开更多
The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can i...The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.展开更多
Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at...Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.展开更多
Objective:The objective of the study is to validate the short version of evidence-based practice(EBP)instruments among nurses in clinical practice.Methods:An institutional-based cross-sectional research design was use...Objective:The objective of the study is to validate the short version of evidence-based practice(EBP)instruments among nurses in clinical practice.Methods:An institutional-based cross-sectional research design was used and a stratified sampling technique to select 285 nurse clinicians.The study utilized a structured questionnaire comprising of demographic data from the participants and three validated scales:the shortened versions of the EBP Beliefs Scale,the EBP Implementation Scale,and the Organizational Culture and Readiness for System-Wide Integration of Evidence-Based Practice(OCRSIEP)survey.With the use of descriptive statistics,the data were analyzed and presented in frequencies and percentages,while inter-item correlation coefficient(ICC)and the Kaiser-Meyer-Olkin measure of sampling adequacy were used to confirm the validity of using factor analysis.Results:Findings revealed the mean scores of the EBP Beliefs Scale ranged from 1.50 to 1.61,EBP Implementation Scale ranged from 1.84 to 1.94,and the OCRSIEP Scale ranged from 1.93 to 2.19.All the three shortened scales accordingly had good internal reliability,29.30±9.93 out of 80 for the EBP Beliefs Scale,19.56±7.37 out of 72 for the EBP Implementation Scale,and 66.32±20.35 out of 125 for the OCRSIEP Scale.Conclusion:This study has generated a valid Short Version of EBP reliable instrument that is psychometrically robust that can be used by nurses and clinicians to evaluate EBP in clinical settings since the results presented as a whole confirmed the high reliability and factorial validity.展开更多
Background: Female Genital Mutilation is still practiced in Ebonyi State in Southeast Nigeria, despite the complications that follows it and government legislation against the practice. Aim: To determine the impact of...Background: Female Genital Mutilation is still practiced in Ebonyi State in Southeast Nigeria, despite the complications that follows it and government legislation against the practice. Aim: To determine the impact of Culture, Beliefs, attitude and Peer Group Influence on the persistence Female Genital Mutilation practice in the State especially in the rural areas. Materials and Methods: Qualitative study that used Focused Group Discussion and In-depth interviews for data collection. Those willing and given consent were recruited into the group discussion according to age, marital status, educational level and their location in the state. In-depth interviews were used with the Stake Holders, Opinion Leaders, Traditional Rulers and the Clergy. Result: A total of 454 participants were recruited from the 13 local government areas of Ebonyi State but only 420 (92.5%) participated. The age ranges of participants were 25 to 35 years for single males and females while the married participants male and female were aged 35 to 70 years. One hundred single females (23.8%) and 94 single males (21.4%) participated while 97 (23.1%) married women and 95 (22.6%) married men participated. Out of the 26 health workers recruited only 22 (5.2%) participated. Four traditional rulers, 4 women leaders, 4 youth leaders and 2 clergy 12 (2.9%) in number had in-depth interviews. All the participants had knowledge of FGM and its practice. Rural health workers are getting more involved. ‘Female Genital Crushing’ is replacing actual cutting. The participants all agreed there is no benefit and the practice should stop. Conclusion: Female Genital Mutilation is secretly practiced and is getting replaced by “Female Genital Crushing” perpetrated by rural health workers as well as mothers, fathers, traditional birth attendants and the peer group playing a major role with low knowledge of the Law against Female genital mutilation.展开更多
基金funded by the Key Project of NSFC-Guangdong Province Joint Program(Grant No.U2001204)the National Natural Science Foundation of China(Grant Nos.61873290 and 61972431)+1 种基金the Science and Technology Program of Guangzhou,China(Grant No.202002030470)the Funding Project of Featured Major of Guangzhou Xinhua University(2021TZ002).
文摘Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved segmented belief propagation list decoding based on bit flipping(SBPL-BF) is proposed. On the one hand, the proposed algorithm makes use of the cooperative characteristic in BPL decoding such that the codeword is decoded in different BP decoders. Based on this characteristic, the unreliable bits for flipping could be split into multiple subblocks and could be flipped in different decoders simultaneously. On the other hand, a more flexible and effective processing strategy for the priori information of the unfrozen bits that do not need to be flipped is designed to improve the decoding convergence. In addition, this is the first proposal in BPL decoding which jointly optimizes the bit flipping of the information bits and the code bits. In particular, for bit flipping of the code bits, a H-matrix aided bit-flipping algorithm is designed to enhance the accuracy in identifying erroneous code bits. The simulation results show that the proposed algorithm significantly improves the errorcorrection performance of BPL decoding for medium and long codes. It is more than 0.25 d B better than the state-of-the-art BPL decoding at a block error rate(BLER) of 10^(-5), and outperforms CA-SCL decoding in the low signal-to-noise(SNR) region for(1024, 0.5)polar codes.
基金supported by the Natural Science Foundation of China underGrant 61833016 and 61873293the Shaanxi OutstandingYouth Science Foundation underGrant 2020JC-34the Shaanxi Science and Technology Innovation Team under Grant 2022TD-24.
文摘In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,theDensityPeakClustering(DPC)algorithmis used todetermine referential values of indicators forBRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.
文摘Faced with autism, motherhood and parenthood are turned upside down in many ways. Coping with stress and mental health problems, continuing to assume a rewarding parental role and finding suitable care are the trials and tribulations that mark out the journey of African parents. Faith and belief have been described as providing meaning and coping mechanisms in the frightening ordeal of disability. An encounter with a young girl and her parents provided an opportunity to analyse the mother’s experience and the impact of beliefs and discourses on her commitment to care. Based on this clinical story, we discuss the place of other-actors (parents and carers) and the Other-God in relation to the psychopathological dynamics of the mother.
文摘Objective:To evaluate the application effect of enteral and parenteral nutrition therapy combined with a health belief education model in patients with inflammatory bowel disease.Methods:80 patients with inflammatory bowel disease admitted to the Shanghai Zhangjiang Institute of Medical Innovation were chosen.This study was carried out from August 2022 to October 2023.The patients were randomly divided into a study group(40 cases)and a control group(40 cases).The treatment plan for the control group was the conventional treatment model,while the treatment plan for the study group was to provide enteral and parenteral nutrition therapy combined with a health belief education model based on the control group.The efficacy of both groups was compared.Results:In the study group,the therapeutic effect for 31 patients(77.50%)was markedly effective and 7 was effective(17.50%),accounting for 95.0%of the total,which was higher than the control group at 80.0%(P<0.05).The relief time of relevant symptoms in the study group was shorter than that of the control group(P<0.05).Before treatment,there were no differences in the high-sensitivity C-reactive protein(hs-CRP),interleukin 10(IL-10),and tumor necrosis factor-α(TNF-α)between both groups(P>0.05).After treatment,the levels of inflammatory factors in the study group(hs-CRP(8.02±1.13)mg/L,IL-10(9.24±1.25)pg/mL,and TNF-α(7.19±1.04)ng/L)were lower than those in the control group(P<0.05).Conclusion:Enteral and parenteral nutritional therapy combined with a health belief education model showed significant efficacy in inflammatory bowel disease patients.Patient symptoms were relieved and inflammatory reactions were reduced.This method is worthy of popularization.
基金This work was supported in part by the Natural Science Foundation of China under Grant 62203461 and Grant 62203365in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736,in part by the Teaching Reform Project of Higher Education in Heilongjiang Province under Grant Nos.SJGY20210456 and SJGY20210457in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038,and in part by the Graduate Academic Innovation Project of Harbin Normal University under Grant Nos.HSDSSCX2022-17,HSDSSCX2022-18 and HSDSSCX2022-19。
文摘A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process.Therefore,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and knowledge.Moreover,an innovative whale optimization algorithm with interpretable constraints is proposed.The experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform.The MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,respectively.It can be seen that the proposed model also shows a better ability to handle small sample data.Meanwhile,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i model.Experimental results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.
基金supported by the Natural Science Foundation of China (No.61833016)the Shaanxi Outstanding Youth Science Foundation (No.2020JC-34)the Shaanxi Science and Technology Innovation Team (No.2022TD-24).
文摘Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model.Due to the understandable knowledge expression and transparent reasoning process,the belief rule base(BRB)has extensive applications as an interpretable expert system in fault diagnosis.Optimization is an effective means to weaken the subjectivity of experts in BRB,where the interpretability of BRB may be weakened.Hence,to obtain a credible result,the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed,which are manifested in deviation from the initial judgement of experts and over-optimization of parameters.For these two factors,three indexes are proposed,namely the consistency index of rules,consistency index of the rule base and over-optimization index,tomeasure the interpretability of the optimizedmodel.Considering both the accuracy and interpretability of amodel,an improved coordinate ascent(I-CA)algorithmis proposed to fine-tune the parameters of the fault diagnosis model based on BRB.In I-CA,the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search algorithm.Furthermore,the random optimization sequence and adaptive step size are proposed to improve the accuracy of the model.Finally,a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.
基金National Natural Science Foundation of China (Grant No.52178393)the Science and Technology Innovation Team of Shaanxi Innovation Capability Support Plan (Grant No.2020TD005)Science and Technology Innovation Project of China Railway Construction Bridge Engineering Bureau Group Co.,Ltd.(Grant No.DQJ-2020-B07)。
文摘Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.
基金supported by the Fundamental Research Funds for the Central Universities (FRF-TP20-062A1)Guangdong Basic and Applied Basic Research Foundation (2021A1515110070)
文摘Belief propagation(BP)decoding outputs soft information and can be naturally used in iterative receivers.BP list(BPL)decoding provides comparable error-correction performance to the successive cancellation list(SCL)decoding.In this paper,we firstly introduce an enhanced code construction scheme for BPL decoding to improve its errorcorrection capability.Then,a GPU-based BPL decoder with adoption of the new code construction is presented.Finally,the proposed BPL decoder is tested on NVIDIA RTX3070 and GTX1060.Experimental results show that the presented BPL decoder with early termination criterion achieves above 1 Gbps throughput on RTX3070 for the code(1024,512)with 32 lists under good channel conditions.
基金supported in part by the National Key Research and Development Program of China(No.2021YFB2900600)in part by the National Natural Science Foundation of China under Grant 61971041 and Grant 62001027。
文摘Orthogonal Time Frequency Space(OTFS)signaling with index modulation(IM)is a promising transmission scheme characterized by high transmission efficiency for high mobility scenarios.In this paper,we study the receiver for coded OTFS-IM system.First,we construct the corresponding factor graph,on which the structured prior incorporating activation pattern constraint and channel coding is devised.Then we develop a iterative receiver via structured prior-based hybrid belief propagation(BP)and expectation propagation(EP)algorithm,named as StrBP-EP,for the coded OTFS-IM system.To reduce the computational complexity of discrete distribution introduced by structured prior,Gaussian approximation conducted by EP is adopted.To further reduce the complexity,we derive two variations of the proposed algorithm by using some approximations.Simulation results validate the superior performance of the proposed algorithm.
基金This work is partially supported by the National Key Research and Development Project under Grant 2018YFB1802402.
文摘The error correction performance of Belief Propagation(BP)decoding for polar codes is satisfactory compared with the Successive Cancellation(SC)decoding.Nevertheless,it has to complete a fixed number of iterations,which results in high computational complexity.This necessitates an intelligent identification of successful BP decoding for early termination of the decoding process to avoid unnecessary iterations and minimize the computational complexity of BP decoding.This paper proposes a hybrid technique that combines the“paritycheck”with the“G-matrix”to reduce the computational complexity of BP decoder for polar codes.The proposed hybrid technique takes advantage of the parity-check to intelligently identify the valid codeword at an early stage and terminate the BP decoding process,which minimizes the overhead of the G-matrix and reduces the computational complexity of BP decoding.We explore a detailed mechanism incorporating the parity bits as outer code and prove that the proposed hybrid technique minimizes the computational complexity while preserving the BP error correction performance.Moreover,mathematical formulation for the proposed hybrid technique that minimizes the computation cost of the G-matrix is elaborated.The performance of the proposed hybrid technique is validated by comparing it with the state-of-the-art early stopping criteria for BP decoding.Simulation results show that the proposed hybrid technique reduces the iterations by about 90%of BP decoding in a high Signal-to-Noise Ratio(SNR)(i.e.,3.5~4 dB),and approaches the error correction performance of G-matrix and conventional BP decoder for polar codes.
基金This work was supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038+2 种基金in part by the innovation practice project of college students in Heilongjiang Province under Grant Nos.202010231009,202110231024,and 202110231155in part by the basic scientific research business expenses scientific research projects of provincial universities in Heilongjiang Province Grant Nos.XJGZ2021001in part by the Education and teaching reform program of 2021 in Heilongjiang Province under Grant No.SJGY20210457.
文摘Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.
文摘Religion is one of the social entities that has had a significant impact on the pandemic.The study’s goals are to investigate the relationship between well-being and fear of COVID-19,as well as to test whether religious beliefs mediate the effect of wellbeing on fear of COVID-19.The sample comprised of 433 participants in Vietnam.Independent Sample t-Test,One-way ANOVA,mediation analysis were used to analyze the data.In the levels of well-being,individuals who engage in religious services daily have higher levels than those hardly and never attend,and people from the age of 18 to 30 have higher levels than individuals from 31 to above 60 years.In addition,people aged from 51 to above 60 have higher levels of religious beliefs than people aged from 18 to 50.Females experience more fear of COVID-19 compared to males.The latter illustrates that religious beliefs mediate the effect of well-being on fear of COVID-19.Social workers and clinicians must prioritize older adults and people with chronic diseases for early mental interventions,and they should be aware of the role of religion in psychological treatment integration.
基金Supported by 2021 Science Popularization Research Project of National Medical Information Network,Chinese Pharmaceutical Association,No.CMEI2021KPYJ00101。
文摘BACKGROUND Medication misuse or overuse is significantly associated with poor health outcomes.Information regarding the knowledge,cultural beliefs,and behavior about medication safety in the general population is important.AIM To conduct a survey on medication habits and explored the potential factors impacting medication safety.METHODS The current survey included adults from 18 districts and counties in Harbin,China.A questionnaire on medication safety was designed based on knowledge,cultural beliefs,and behavior.Both univariate and multivariate analyses were used to explore the factors that impacted medication safety.RESULTS A total of 394 respondents completed the questionnaires on medication safety.The mean scores for knowledge,cultural beliefs,and behavior about medication safety were 59.41±19.33,40.66±9.24,and 60.97±13.69,respectively.The medication knowledge score was affected by age(P=0.044),education(P<0.001),and working status(P=0.015).Moreover,the cultural beliefs score was significantly affected by education(P<0.001).Finally,education(P=0.003)and working status(P=0.011)significantly affected the behavior score.CONCLUSION The knowledge,cultural beliefs,and behavior about medication safety among the general population was moderate.Health education should be provisioned for the elderly,individuals with a low education level,and the unemployed to improve medication safety in Harbin,China.
文摘Deep learning is the subset of artificial intelligence and it is used for effective decision making.Wireless Sensor based automated irrigation system is proposed to monitor and cultivate crop.Our system consists of Distributed wire-less sensor environment to handle the moisture of the soil and temperature levels.It is automated process and useful for minimizing the usage of resources such as water level,quality of the soil,fertilizer values and controlling the whole system.The mobile app based smart control system is designed using deep belief network.This system has multiple sensors placed in agriculturalfield and collect the data.The collected transmitted to cloud server and deep learning process is applied for making decisions.DeepQ residue analysis method is proposed for analyzing auto-mated and sensor captured data.Here,we used 512×512×3 layers deep belief network and 10000 trained data and 2500 test data are taken for evaluations.It is automated process once data is collected deep belief network is generated.The performance is compared with existing results and our process method has 94%of accuracy factor.Also,our system has low cost and energy consumption also suitable for all kind of agriculturalfields.
基金This work was supported in part by the Natural Science Foundation of China under Grant 62203461 and Grant 62203365in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736+3 种基金in part by the Teaching reform project of higher education in Heilongjiang Province under Grant Nos.SJGY20210456 and SJGY20210457in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038in part by the graduate academic innovation project of Harbin Normal University under Grant Nos.HSDSSCX2022-17,HSDSSCX2022-18 andHSDSSCX2022-19in part by the Foreign Expert Project of Heilongjiang Province under Grant No.GZ20220131.
文摘Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.
文摘Cancer disease is a deadliest disease cause more dangerous one.By identifying the disease through Artificial intelligence to getting the mage features directly from patients.This paper presents the lung knob division and disease characterization by proposing an enhancement calculation.Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification.This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy.To resolve this problem,to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor.The general technique of the created approach includes four stages,such as pre-processing,segmentation,highlight extraction,and the order.From the outset,the Computerized Tomography(CT)image of the lung is taken care of to the division.When the division is done,the highlights are extricated through morphological factors for feature observation.By getting the features are analysed and the characterization is done dependent on the Deep Belief Network(DBN)which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm(CSCA)which distinguish the lung tumour,giving two classes in particular,knob or non-knob.The proposed system produce high performance as well compared to the other system.The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity,precision,affectability,and the explicitness.
基金This work is supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Teaching reform project of higher education in Heilongjiang Province under Grant No.SJGY20210456in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038.
文摘The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.
文摘Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.
文摘Objective:The objective of the study is to validate the short version of evidence-based practice(EBP)instruments among nurses in clinical practice.Methods:An institutional-based cross-sectional research design was used and a stratified sampling technique to select 285 nurse clinicians.The study utilized a structured questionnaire comprising of demographic data from the participants and three validated scales:the shortened versions of the EBP Beliefs Scale,the EBP Implementation Scale,and the Organizational Culture and Readiness for System-Wide Integration of Evidence-Based Practice(OCRSIEP)survey.With the use of descriptive statistics,the data were analyzed and presented in frequencies and percentages,while inter-item correlation coefficient(ICC)and the Kaiser-Meyer-Olkin measure of sampling adequacy were used to confirm the validity of using factor analysis.Results:Findings revealed the mean scores of the EBP Beliefs Scale ranged from 1.50 to 1.61,EBP Implementation Scale ranged from 1.84 to 1.94,and the OCRSIEP Scale ranged from 1.93 to 2.19.All the three shortened scales accordingly had good internal reliability,29.30±9.93 out of 80 for the EBP Beliefs Scale,19.56±7.37 out of 72 for the EBP Implementation Scale,and 66.32±20.35 out of 125 for the OCRSIEP Scale.Conclusion:This study has generated a valid Short Version of EBP reliable instrument that is psychometrically robust that can be used by nurses and clinicians to evaluate EBP in clinical settings since the results presented as a whole confirmed the high reliability and factorial validity.
文摘Background: Female Genital Mutilation is still practiced in Ebonyi State in Southeast Nigeria, despite the complications that follows it and government legislation against the practice. Aim: To determine the impact of Culture, Beliefs, attitude and Peer Group Influence on the persistence Female Genital Mutilation practice in the State especially in the rural areas. Materials and Methods: Qualitative study that used Focused Group Discussion and In-depth interviews for data collection. Those willing and given consent were recruited into the group discussion according to age, marital status, educational level and their location in the state. In-depth interviews were used with the Stake Holders, Opinion Leaders, Traditional Rulers and the Clergy. Result: A total of 454 participants were recruited from the 13 local government areas of Ebonyi State but only 420 (92.5%) participated. The age ranges of participants were 25 to 35 years for single males and females while the married participants male and female were aged 35 to 70 years. One hundred single females (23.8%) and 94 single males (21.4%) participated while 97 (23.1%) married women and 95 (22.6%) married men participated. Out of the 26 health workers recruited only 22 (5.2%) participated. Four traditional rulers, 4 women leaders, 4 youth leaders and 2 clergy 12 (2.9%) in number had in-depth interviews. All the participants had knowledge of FGM and its practice. Rural health workers are getting more involved. ‘Female Genital Crushing’ is replacing actual cutting. The participants all agreed there is no benefit and the practice should stop. Conclusion: Female Genital Mutilation is secretly practiced and is getting replaced by “Female Genital Crushing” perpetrated by rural health workers as well as mothers, fathers, traditional birth attendants and the peer group playing a major role with low knowledge of the Law against Female genital mutilation.
