A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

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,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,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.

A New Prediction System Based on Self-Growth Belief Rule Base with Interpretability Constraints

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.

Evaluation of the effects of health education interventions for hypertensive patients based on the health belief model

BACKGROUND Hypertension is a major risk factor for cardiovascular disease and stroke,and its prevalence is increasing worldwide.Health education interventions based on the health belief model(HBM)can improve the knowledge,attitudes,and behaviors of patients with hypertension and help them control their blood pressure.AIM To evaluate the effects of health education interventions based on the HBM in patients with hypertension in China.METHODS Between 2021 and 2023,140 patients with hypertension were randomly assigned to either the intervention or control group.The intervention group received health education based on the HBM,including lectures,brochures,videos,and counseling sessions,whereas the control group received routine care.Outcomes were measured at baseline,three months,and six months after the intervention and included blood pressure,medication adherence,self-efficacy,and perceived benefits,barriers,susceptibility,and severity.RESULTS The intervention group had significantly lower systolic blood pressure[mean difference(MD):-8.2 mmHg,P<0.001]and diastolic blood pressure(MD:-5.1 mmHg,P=0.002)compared to the control group at six months.The intervention group also had higher medication adherence(MD:1.8,P<0.001),self-efficacy(MD:12.4,P<0.001),perceived benefits(MD:3.2,P<0.001),lower perceived barriers(MD:-2.6,P=0.001),higher perceived susceptibility(MD:2.8,P=0.002),and higher perceived severity(MD:3.1,P<0.001)than the control group at six months.CONCLUSION Health education interventions based on the HBM effectively improve blood pressure control and health beliefs in patients with hypertension and should be implemented in clinical practice and community settings.

DeepQ Based Automated Irrigation Systems Using Deep Belief WSN

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.

Assessment of public perspectives and barriers towards dengue preventive practices using the Health Belief Model in Puducherry,India:A cross-sectional study

Objective:To assess the perspectives and barriers towards dengue preventive practices among the residents of Puducherry,India.Methods:A cross-sectional survey was conducted in 300 households in Puducherry,using a population-proportionate(7:3)distribution from urban and rural areas by grid sampling.One adult interview per household was conducted and the participants were selected using a KISH grid.A semi-structured questionnaire based on the Health Belief Model(HBM)with additional questions on knowledge assessment was used.Knowledge was assessed based on the correctness of answers and the HBM scores were calculated on a 5-point Likert scale.Participants were categorized based on the median score under each domain.Logistic regression was used for adjusted analysis and models were built to predict the performances in each domain.Results:Four percent of the participants lacked basic knowledge regarding dengue transmission.While 208(69.3%)participants did not consider themselves at risk of contracting dengue within the next year,majority perceived dengue as a disease with low severity.Around 49.3%(148)were skeptical about the benefit of time and money spent on dengue prevention.Inadequate government efforts were stated as the major barrier(47.0%)and frequent reminders(142,47.3%)as the major cue to action.Age above 50 years(aOR 1.78,95%CI 1.04-3.06,P=0.037)and rural locality(aOR 2.68,95%CI 1.52-4.71,P=0.001)were found to be significantly associated with poor knowledge scores.Urban participants had a significantly higher chance to perceive low susceptibility as compared to the rural counterparts(aOR 1.74,95%CI 1.05-2.9,P=0.03).Participants with less than a high school education had low perceived benefits(aOR 2.46,95%CI 1.52-3.96,P<0.001)and low self-efficacy scores(aOR 2.66,95%CI 1.61-4.39,P<0.001).Conclusions:This study identifies key gaps in dengue prevention,including low perceived susceptibility,mild disease perception,limited knowledge of breeding sites,and overreliance on government efforts.Tailoring interventions to community needs,stratified to factors influencing the community perspectives can significantly improve dengue prevention efforts.

Improved Belief Propagation Decoder for LDPC-CRC-Polar Codes with Bit-Freezing

Though belief propagation bit-flip(BPBF)decoding improves the error correction performance of polar codes,it uses the exhaustive flips method to achieve the error correction performance of CA-SCL decoding,thus resulting in high decoding complexity and latency.To alleviate this issue,we incorporate the LDPC-CRC-Polar coding scheme with BPBF and propose an improved belief propagation decoder for LDPC-CRC-Polar codes with bit-freezing(LDPCCRC-Polar codes BPBFz).The proposed LDPCCRC-Polar codes BPBFz employs the LDPC code to ensure the reliability of the flipping set,i.e.,critical set(CS),and dynamically update it.The modified CS is further utilized for the identification of error-prone bits.The proposed LDPC-CRC-Polar codes BPBFz obtains remarkable error correction performance and is comparable to that of the CA-SCL(L=16)decoder under medium-to-high signal-to-noise ratio(SNR)regions.It gains up to 1.2dB and 0.9dB at a fixed BLER=10-4compared with BP and BPBF(CS-1),respectively.In addition,the proposed LDPC-CRC-Polar codes BPBFz has lower decoding latency compared with CA-SCL and BPBF,i.e.,it is 15 times faster than CA-SCL(L=16)at high SNR regions.

Belief reliability:a scientific exploration of reliability engineering

This paper systematically introduces and reviews a scientific exploration of reliability called the belief reliability.Beginning with the origin of reliability engineering,the problems of present theories for reliability engineering are summarized as a query,a dilemma,and a puzzle.Then,through philosophical reflection,we introduce the theoretical solutions given by belief reliability theory,including scientific principles,basic equations,reliability science experiments,and mathematical measures.The basic methods and technologies of belief reliability,namely,belief reliability analysis,function-oriented belief reliability design,belief reliability evaluation,and several newly developed methods and technologies are sequentially elaborated and overviewed.Based on the above investigations,we summarize the significance of belief reliability theory and make some prospects about future research,aiming to promote the development of reliability science and engineering.

Heterogeneous information fusion recognition method based on belief rule structure

To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.

Improved Segmented Belief Propagation List Decoding for Polar Codes with Bit-Flipping

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.

Integrating the health belief model into health education programs in a clinical setting

The article demonstrates that health belief model(HBM)-based health education in hypertensive patients effectively improves blood pressure control and medication adherence at 3 months and 6 months.The HBM addresses perceived barriers,benefits,susceptibility,severity,and self-efficacy,leading to better health behaviors.HBM-based education has been effective in various contexts,including managing chronic diseases,promoting cancer screenings,and preventing infectious diseases.However,the model has limitations,such as cultural applicability and addressing complex health behaviors influenced by environmental factors.Future research should integrate HBM with other theories and conduct longitudinal studies to assess long-term impacts.Despite these limitations,HBM-based education significantly improves patient outcomes,highlighting its potential in health education and promotion when appropriately adapted and implemented.This reinforces the model's value in designing effective health interventions and advancing public health.

Chinese Adaptation and Psychometric Properties of the Belief in a Just World Scale for College Students

This study aims to revise the Belief in a Just World Scale(BJWS)for Chinese college students and test its reliability and validity(construct validity,convergent and divergent validity).Two samples of 546 and 595 college students were selected,respectively,using stratified cluster random sampling.Item analysis,exploratory factor analysis(EFA),confirmatory factor analysis(CFA),reliability analysis and convergent and divergent validity tests were carried out.The results showed that the 13 items of the BJWS have good item discrimination.The corrected item–total correlation in the general belief in a just world subscale was found to range from 0.464 to 0.655,and that in the personal belief in a just world subscale was 0.553 to 0.715.The internal consistency coefficients of the revised version of the BJWS and its subscales are good.The EFA and CFA results show that the structure and items of the revised scale are the same as those of the original scale.Belief in a just world was found to have significant positive correlations with gratitude and empathy,and has a significant negative correlation with anxiety,thereby exhibiting good convergent and divergent validity.Therefore,the Chinese revised version of the BJWS has good reliability and validity.

Exploring the Intersection of Spirituality and Religion:Transcendence,Secularism,and Personal Beliefs in Contemporary Society

This paper explores the intersection of spirituality and religion,focusing on how transcendence,secularism,and personal beliefs shape contemporary spiritual practices.It examines the philosophical foundations of transcendence,the rise of existentialism,and the distinction between spirituality and religion.Secularism's role in fostering personal spirituality and reducing religious authority is discussed,alongside the psychological and societal impacts of spiritual transcendence.The paper also critiques the limitations of spirituality,emphasizing the need for a balanced approach that integrates cognitive development and mental health perspectives.

A study on the cognitive status of human papillomavirus among adult males in Tianjin based on the health belief model

Background:To understand the health beliefs and knowledge of human papillomavirus among adult males in Tianjin.Methods:An online questionnaire survey was conducted from 18 January 2023 to 6 March 2023 using snowball sampling method.Analyze the health belief scores and human papillomavirus(HPV)and HPV vaccine knowledge scores of adult males in Tianjin,and analyze their influencing factors.Results:A total of 388 adult males in Tianjin were surveyed,with an average total score of 3.23±0.04 for their health beliefs.Among them,the average scores for perceived severity,perceived susceptibility,perceived impairment,perceived benefit,and self-efficacy were 3.41±1.05,2.37±1.20,2.96±1.00,3.51±0.90,and 3.36±1.08,respectively.Multiple linear regression analyses showed education was a factor influencing health beliefs.The average total score of knowledge is 64.09±15.62,with 277 people scoring above 60,and a pass rate of 71.4%.Through multiple linear regression analysis,education level,emotional status,whether disease testing has been done,and whether family and friends have been diagnosed with HPV positive are the main influencing factors.Conclusion:The awareness rate of HPV among adult males in Tianjin is still acceptable,but there are still misconceptions.The overall level of health beliefs is moderate,and the perceived susceptibility level is low.It is necessary to strengthen health education on HPV related knowledge for males and improve their cognitive level.

Faith and Belief in Autism in Cote d’Ivoire: About Judith, God’s Strange Gift

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.

Optimal Insurance with Background Risk under the Ambiguity and Belief Heterogeneity Structure

In this paper, we discuss the optimal insurance in the presence of background risk while the insured is ambiguity averse and there exists belief heterogeneity between the insured and the insurer. We give the optimal insurance contract when maxing the insured’s expected utility of his/her remaining wealth under the smooth ambiguity model and the heterogeneous belief form satisfying the MHR condition. We calculate the insurance premium by using generalized Wang’s premium and also introduce a series of stochastic orders proposed by [1] to describe the relationships among the insurable risk, background risk and ambiguity parameter. We obtain the deductible insurance is the optimal insurance while they meet specific dependence structures.

Evaluation of the Application Effect of Enteral and Parenteral Nutrition Therapy Combined with a Health Belief Education Model in Patients with Inflammatory Bowel Disease

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.

An Empirical Evaluation of the Impact of Moral and Law Course Teaching on the Effectiveness of University Students’Ideals and Beliefs Education

The Moral and Law course serves as a critical platform for educating university students about ideals and beliefs,and it has a significant impact on the effectiveness of ideals and beliefs education.This paper establishes a mechanism through which Moral and Law course teaching influences the effectiveness of ideals and beliefs education and conducts an empirical evaluation.The results reveal that factors such as the relevance and applicability of the teaching content,the integration of theory and practice,the innovation,interactivity,and participation of teaching methods,as well as classroom atmosphere,teaching facilities,and campus culture,all have a significant positive impact on the effectiveness of ideals and beliefs education for university students.

Bayesian belief-based model for reliability improvement of the digital reactor protection system

The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.

HUID:DBN-Based Fingerprint Localization and Tracking System with Hybrid UWB and IMU

High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.

Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

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.