Bayesian Belief Network-based approach for diagnostics and prognostics of semiconductor manufacturing systems

Water mains are the essential component of a water supply system (WSS) buried underground, and they account for 80% of total system expenditures. Deterioration of this asset, as a result of an aggressive soil environment, is inevitable over a pipe’s service life. Although an increasing number of studies have estimated the remaining service life (RSL) of metallic pipes, most of them either are data intensive or consider limited soil and pipe parameters. In this paper, a Bayesian belief network (BBN) model is proposed to handle the problem of varying data availability and the dependency between parameters. First the proposed approach uses a combination of empirical data, experimental data, expert opinion, and a mathematical model to predict soil corrosivity and pit depth. Then a simple programming logic is used to predict RSL. Finally, the performance of the model is evaluated using a BBN sensitivity analysis. Monte Carlo simulations (MCSs) are performed using randomly generated input from measured ...

Liquefaction is considered a damaging phenomenon of earthquakes and a major cause of concern in civil engineering. Therefore, its predictory assessment is an essential task for geotechnical experts. This paper investigates the performance of Bayesian belief network (BBN) and C4.5 decision tree (DT) models to evaluate seismic soil liquefaction potential based on the updated and relatively large cone penetration test (CPT) dataset (which includes 251 case histories), comparing them to a simplified procedure and an evolutionary-based approach. The BBN model was developed using the K2 machine learning algorithm and domain knowledge (DK) with data fusion methodology, while the DT model was created using a C4.5 algorithm. This study shows that the BBN model is preferred over the others for evaluation of seismic soil liquefaction potential. Owing to its overall performance, simplicity in practice, data-driven characteristics, and ability to map interactions between variables, the use of a BBN model in assessing seismic soil liquefaction is quite promising. The results of a sensitivity analysis show that ‘equivalent clean sand penetration resistance’ is the most significant factor affecting liquefaction potential. This study also interprets the probabilistic reasoning of the robust BBN model and most probable explanation (MPE) of seismic soil liquefied sites, based on an engineering point of view.

Human activities (e.g. agriculture and industry) have been increasing within the river basins. These agricultural and industrial impacts affect the water quality in the basin and the related ecosystem and ecosystem services. We aim to identify the link between water management options and ecosystem services, and how the ecosystem services change when certain management actions are applied. Thus, a case study in the Guayas River basin (Ecuador) is presented. A trade-off tool was developed to assess ecosystem services. A model was developed with both biophysical and management variables as input to calculate the effect on multiple ecosystem services (food production, fresh water availability and recreational value). With these three ecosystem services, a trade-off tool was constructed with the use of Bayesian Belief Networks (BBNs). Based on the model outcome, it can be stated that in general, the changes in management leading to an increase in food production, made the fresh water availability and recreational value go down. With further research on the influences of different variables on ecosystem functions and services, this BBN model can become a valuable trade-off tool to be implemented in both water and environmental management and decision making.

This paper proposes machine learning techniques to discover knowledge in a dataset in the form of if-then rules for the purpose of formulating queries for validation of a Bayesian belief network model of the same data. Although do-main expertise is often available, the query formulation task is tedious and laborious, and hence automation of query formulation is desirable. In an effort to automate the query formulation process, a machine learning algorithm is lev-eraged to discover knowledge in the form of if-then rules in the data from which the Bayesian belief network model under validation was also induced. The set of if-then rules are processed and filtered through domain expertise to identify a subset that consists of “interesting” and “significant” rules. The subset of interesting and significant rules is formulated into corresponding queries to be posed, for validation purposes, to the Bayesian belief network induced from the same dataset. The promise of the proposed methodology was assessed through an empirical study performed on a real-life dataset, the National Crime Victimization Survey, which has over 250 attributes and well over 200,000 data points. The study demonstrated that the proposed approach is feasible and provides automation, in part, of the query formulation process for validation of a complex probabilistic model, which culminates in substantial savings for the need for human expert involvement and investment.

Background: Short (<6hrs) and long (>8 hrs) sleep durations are linked to stroke and cardiovascular disease. However, results have relied primarily on regression analysis, which may not be optimal to model associations between sleep and medical outcomes. Big-data and machine learning analyses provide unique opportunities to quantify dynamic interactions between sleep and medical outcomes, adjusting for multiple risk factors. Method: The current study utilized two types of analyses: logistic regression and Bayesian Belief Network, a form of machine learning analysis, to assess sleep-related stroke risk. We used data from the 2004-2013 National Health Interview Survey, yielding 288,888 cases, to investigate how short (<7hrs.) and long (>8hrs.) sleep durations impact stroke risk. In both analyses, we assessed the contribution of 34 demographic, medical, behavioral, and psychosocial factors. We used SPSS 20 to conduct regression analyses and BayesiaLab’s Tree Augmented Naïve Bayes learning algorithm for complex system analysis. We compared results of both analytic models to determine their ecological and clinical utility. Results: Forty-eight percent of volunteers were ≤45 yrs.; 77.40% were White; 15.96%, Black/African American; and 45.1% made < $35K annually; 29.55% reported short sleep and 8.9%, long sleep; 61.55% were average sleepers (7-8hrs.). Adjusted regression models indicated that relationships between short sleep and stroke were not significant (OR=.97, 95% CI=0.92-1.02, p =.21); however, long sleep was associated with stroke (OR=1.43, 95% CI= 1.32-1.52, p <.001). Results from Bayesian analysis indicated both short and long sleep were associated with stroke, but that long sleep doubled stroke risk (7.48%) relative to short sleep (3.74%). Regression model had a R 2 of 0.24 for short sleep and long sleep, while the R 2 for Bayesia was 0.73. Conclusion: Bayesian Belief Network analysis is superior to regression modeling because it provides ecologically and clinically valid findings. Although both short and long sleep durations are associated with stroke risk, long sleep seems to be a stronger predictor.

Bayesian belief networks to analyse trade-offs among ecosystem services at the regional scale

A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland

Bayesian belief network (BBN) is a framework for representation/inference of some knowledge with uncertainty. Since the process of constructing a BBN manually by experts is time-consuming in general, some method supporting the task is needed. We proposed an algorithm for acquiring some BBN automatically from finite examples based on minimum description length (MDL) principle. This paper addresses an improvement which relaxes a constraint that the original scheme held on the representation. In BBNs, attributes and stochastic dependencies between them are expressed as nodes and directed links connecting them, respectively, where each attribute may be a predicate, a numerical data, etc., and each dependence is numerically expressed as the conditional probability of one attribute given other attributes if their dependence exists. Therefore, in general, BBNs are represented in terms of the network structure and the conditional probabilities.

Heat treatment equipment are usually harmful to people, they need to be monitored remotely. As the fact that different types of equipment are used in factories, which leads to the problem that monitoring equipment is difficult. Therefore, research on remote centralized temperature monitoring system is necessary. The concept of isomeric equipment is proposed. The framework of monitoring system is established, which is constituted of temperature control instruments, monitoring server and clients. The functions of monitoring system are described, such as setting temperature, receiving temperature, showing the chart of temperature, accumulating and reusing heat treatment experience etc. The key technologies of monitoring system including dynamic protocol adapter and knowledge accumulation and reuse are also explained. Finally, the system is applied in a factory. The engineer can monitor almost all of the heat treatment equipment only by use the system in the office.

Sustainability is increasingly critical for universities as institutions influence environmental stewardship, social equity, and the management of economic resources. The Environmental, Social, and Governance (ESG) framework is a widely used tool to assess university sustainability efforts, providing insight into institutional policies, practices, and external contributions, such as alumni research and societal engagement. This study analyzes data from 1,397 universities using the 2024 QS (Quacquarelli Symonds) sustainability rankings, which evaluate environmental impact, social impact, and governance indicators. A Bayesian Belief Network (BBN) model is applied to uncover the complex interdependencies among governance, social impact, and environmental impact, revealing how these factors jointly influence overall sustainability performance. The findings demonstrate that governance plays a pivotal role, with strong governance enhancing both social and environmental outcomes. By highlighting these interdependencies, the study provides valuable insights for university leaders and policymakers seeking to optimize ESG-based strategies to improve institutional sustainability. The main contribution of this research lies in applying a network-based probabilistic model to ESG indicators, offering a more nuanced understanding of how governance and other ESG factors interact to shape sustainability performance in higher education.

Pregnancy and childbirth are commonly seen as positive experiences, but they can also pose distinct challenges and risks, especially when care is insufficient. This study investigates the factors influencing maternity patient experience by exploring the complex interactions among these factors. Using data from the 2021 maternity patient survey by the National Health Services (NHS) in England, we implemented a Bayesian Belief Network (BBN) to model these interactions. Three structural learning models were created, namely Bayesian Search (BS), Peter-Clark (PC), and Greedy Thick Thinning (GTT). Further, sensitivity analysis was conducted to quantify interactions among the influencing factors and identify the most influential factor affecting the outcome. The results underscore the importance of recognizing the interdependencies among the eight key domains of the survey, which collectively shape maternity care experiences. These factors include the start of care in pregnancy, antenatal check-ups, care during pregnancy, labour and birth, staff caring, care in the hospital, feeding the baby, and care after birth. These findings can guide healthcare managers and decision-makers in developing proactive strategies to mitigate factors impacting maternity patient experiences. Ultimately, this study contributes to the ongoing efforts to enhance the quality of maternity care and improve outcomes for mothers and their infants.

Climate change is a pervasive global issue that threatens the livelihoods and wellbeing of billions living globally. Climate change is a risk multiplier impacting all ecosystems, society, and sectors of the economy. The agriculture sector is one such sector that is highly vulnerable to changes in climate. In a country like Nepal where rainfed agriculture is a dominant occupation and a key pillar of the country’s economy, climate change brings risks and negative consequences for on-farm production, farmers' livelihoods as well as on the country’s development. The impacts of climate change including rising temperatures, an increase in the frequency or intensity of extreme weather events such as drought, and shifts in the rainfall seasonality, can cause a decline in food production and threaten the quality of food supplies, leading to reduced food security. The rise in the global population will increase global demand for food which implies that agriculture needs to boost production and increase yields, among other things. The unprecedented risks posed by climate change potentially undermine the ability of farms and farm holders to grow adequate and quality food. The severity of these risks varies due to a range of underlying factors including low economic development, their location, existing biophysical and socioeconomic conditions, and institutional arrangements. While the impacts of climate change on food production as well as agricultural practices in Nepal have been documented, there is a dearth in scholarly literature that has assessed the impacts of climate change on household food security in Nepal incorporating farmers’ perspectives and in particular smallholder subsistence farmers. Furthermore, there is only modest literature that has examined geographical variations in those experiences and understandings. This PhD study aimed, therefore, to investigate the effects of climate change on agricultural practices and food security, with a focus on subsistence smallholder farmers in three main agro- ecological zones of Nepal known as The Mountains, Hills, and the Terai. The study aimed to respond to the primary research question: How are Nepalese farming communities being impacted by climate change and how are they responding to ensure their continued food security? To answer this main research, question the study posed the following secondary research questions: Q1. How is climate changing and how is it impacting subsistence agriculture? Q2. What are the farmers experiencing and what is their understanding? a. Are there gender differences in understanding and experiencing? Q3. What is the state of food insecurity among these farmers? a. How is it being impacted by climate change? Q4. What adaptation strategies have been adopted by smallholder farmers to address threats to agricultural practices and food security from climate change and other pressures? Both the qualitative and qualitative data were collected using multiple methods to address the identified research questions. Methods included a narrative literature review, systematic review, face to face interviews with farmers, individual interviews with key informants and focus groups with the women's group. Climate data on temperature between 1971-2013 and rainfall between 1967-2013 were analysed. Additionally, secondary data on crop yield from 1980 to 2016 were also analysed to gain a better empirical understanding of the relations between climate change and yield pattern and to triangulate and validate the findings from the interviews. Quantitative data on cereal crop yields and climate data were systematically tabulated and further statistically analysed using software R. This study employed the Bayesian approach to statistical modelling. Besides, this study undertook an integrated risk assessment of food insecurity using the Bayesian Belief Network model to reflect how the risk of food insecurity is influenced under two scenarios: (1) current climate conditions and (2) the influences of different adaptation strategies employed. NVivo was used for content analysis for qualitative data obtained from the key informant interview and focus group data and analysis of transcripts from farmers' structured interviews. The findings showed that agricultural practices have undergone various changes over the past 30 years. Climate change impacts were experienced by farmers in all three agro-ecological zones of Nepal. However, the impacts varied between these zones in terms of frequency and intensity. The effect of climate change was highly pronounced in the Mountains zone followed by the Hills and the Terai. The results confirmed that rural subsistence smallholder farmers dependent on rainfed agriculture are vulnerable to climate change impacts. Moreover, it disproportionately affects the poor farmers whose income hinges solely on agriculture and associated activities. Boosting agricultural production and empowering these smallholders is key to enhancing their food security. The experiences reported by farmers are well aligned with the trend of the climatic variables obtained from climate data analysis, highlighting the importance of perception-based survey in complementing climate research. The study demonstrated both the climatic and non-climatic factors are affecting agricultural practices as well as household food security of these farming communities. It is, therefore, difficult to isolate the influence of any of these factors. This was supported by the findings from the risk assessment undertaken by Bayesian modelling. Based on Bayesian modelling, the smallholder farmers mainly at the Mountain zone were at the risk of food insecurity. The measures to increase the adaptive capacity of these smallholders were found to help them manage the risk of food insecurity. Addressing the complicated and multifaceted concerns of climate change and food security needs multidisciplinary and multisectoral adaptation interventions acknowledging underlying biophysical, social, economic, geographical and environmental circumstances. Farmers have taken some actions to adapt and reduce the worsening impacts of climate change. Nevertheless, farmers encountered several barriers in effectively adapting to climate change. This study concluded there is an urgent need for a transformative level of intervention that warrants a coordinated action and collaboration between relevant stakeholders working in this field, including governments and non-governmental organizations, to target the most vulnerable and the needy smallholder farmers addressing the constraints and pressures they face. Policy and decision-makers should work extensively and sensitively with smallholders to ensure the maintenance of their livelihood and to guarantee their food security. Combining local and scientific knowledge is needed to help direct research and tailor adaptation solutions that meet local conditions and needs.

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na<TEX>$\ddot{i}$</TEX>veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy (OA), precision, recall, F-measure, and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

Status and trends of habitats of terrestrial vertebrates in relation to land management in the interior Columbia river basin