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.

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.

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.

Assessment of knowledge,cultural beliefs,and behavior regarding medication safety among residents in Harbin,China

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.

Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant challenge.Blockchain technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing technologies.Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development.In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details.Additionally,the deep belief network(DBN)model is used to select suitable vehicles and goods for transportation.Additionally,the chaotic krill herd technique is used to tune the DBN model’s hyper-parameters.The performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of conditions.The simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

Content-Based Movie Recommendation System Using MBO with DBN

The content-basedfiltering technique has been used effectively in a variety of Recommender Systems(RS).The user explicitly or implicitly provides data in the Content-Based Recommender System.The system collects this data and creates a profile for all the users,and the recommendation is generated by the user profile.The recommendation generated via content-basedfiltering is provided by observing just a single user’s profile.The primary objective of this RS is to recommend a list of movies based on the user’s preferences.A con-tent-based movie recommendation model is proposed in this research,which recommends movies based on the user’s profile from the Facebook platform.The recommendation system is built with a hybrid model that combines the Mon-arch Butterfly Optimization(MBO)with the Deep Belief Network(DBN).For feature selection,the MBO is utilized,while DBN is used for classification.The datasets used in the experiment are collected from Facebook and MovieLens.The dataset features are evaluated for performance evaluation to validate if data with various attributes can solve the matching recommendations.Eachfile is com-pared with features that prove the features will support movie recommendations.The proposed model’s mean absolute error(MAE)and root-mean-square error(RMSE)values are 0.716 and 0.915,and its precision and recall are 97.35 and 96.60 percent,respectively.Extensive tests have demonstrated the advantages of the proposed method in terms of MAE,RMSE,Precision,and Recall compared to state-of-the-art algorithms such as Fuzzy C-means with Bat algorithm(FCM-BAT),Collaborativefiltering with k-NN and the normalized discounted cumulative gain method(CF-kNN+NDCG),User profile correlation-based similarity(UPCSim),and Deep Autoencoder.

The Mediating Role of Religious Beliefs in the Relationship between Well-Being and Fear of the Pandemic

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.

A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

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.

Validation of short version of evidence-based practice instruments among nurses in clinical practice:Evidence-based practice beliefs,implementation,and organizational culture

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.

Culture, Beliefs, Attitude and Peer Group Influence on Female Genital Mutilation in Southeast Nigeria

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.

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.

A Scholar of Dignity:Remembering Peter Luh

It was a shock and disbelief to learn of Peter's death last December.To me,he was so fit in his figure,so healthy in his lifestyle,so mild and mindful in his social behavior,and so devout in his religious belief,......,I had expected a long and happy life for him,and planned to join his 80th or even 100th birthday celebration.

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.

A Denoiser for Correlated Noise Channel Decoding: Gated-Neural Network

This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).

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.