dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion This study showed significant correlations between several graph metrics based on diffusion measures and psychometric scores. First, the increase of network degree with intelligence scores, confirmed that fiber bundles density of brain networks plays a significant role in children intelligence. Second, considering that modularity represents a measure of segregation level between graph modules, and that transitivity reflects the redundancy in the network connections, the low modularity and low transitivity observed in high IQ children confirmed that homogeneous brain organization correlates with intelligence. These findings show that children's high IQ may be related to a high fiber density and optimally distributed brain networks, thanks to the global sensitivity of such graph-based methods for the investigation of brain topological organization.
Moreover, nanometer scale effects such as optical coupling and phase change have to be considered together with micrometer scale thermal diffusion effects for microsecond scale duration (i. e. , time to return to equilibrium) with nanosecond scale time resolution. In this work, we use a TCAD package (named LIAB: LASSE Innovation Application Booster)  [16]  overcoming these challenges to simulate the MLA process in 3D structures by a careful optimization of the numerical meshing strategy, adapted boundary conditions, and an adaptive time-stepping algorithm. Specifically, the incident laser wave comes from the top of the simulated structure and a constant temperature of 300 K is set at the lower boundary of the system (i. e. , at the bottom of a 700 µm thick coarsely meshed Si substrate). Moreover, a condition of symmetry is imposed on the left and right boundaries, which is consistent for the periodic simulation of the surrounding equivalent FinFETs.
Bayesian network (BN) is a popular machine learning (ML) method used for system modelling and decision-making under uncertainty. This paper provides a systematic review and evaluation of existing research on the use of BN models in the wind energy sector. To conduct this literature review, all relevant databases from inception to date were searched, and a total of 70 sources (including journal publications, conference proceedings, PhD dissertations, industry reports, best practice documents and software user guides) which met the inclusion criteria were identified, excluding references used in other sections of the text for discussion. Our review findings reveal that the applications of BNs in the wind energy industry are quite diverse, ranging from wind power and weather forecasting to risk management, fault diagnosis and prognosis, structural analysis, reliability assessment, and maintenance planning and updating.
ML methods are capable to learn from training datasets, capture complex interactions that are difficult to model through analytical methods, and predict possible outcomes based on new observations. Of all the ML tools and techniques, the Bayesian Network (BN) seems to be the most promising framework for updating the information in (near) real-time. This is useful in situations where there is limited information about a complex system and the network has to be updated when additional information becomes available. BNs have been utilised in several industries, particularly because of their ability to treat uncertainties found in many applications. Wind energy farms, as complex systems that function under a high degree of uncertainty, can benefit from BN models by mitigating uncertainties inherent in the system's operational conditions or the surrounding environment.
It is proposed that the use of Bayesian models in assessment of reliability as well as scheduling of wind farm O&M activities can bring benefits in terms of reduction of maintenance costs and increase of power production. The aim of this paper is to review the state-of-the-art of BN models and identify their applications in the onshore and offshore wind energy sectors, especially as they relate to the reliability of wind energy systems. Relevant databases have been searched using appropriate keywords and the studies investigating the use of BNs in the wind energy industry have been identified. These studies range from journal articles and conference papers to university dissertations, industry reports, best practice documents and software user guides. Our review findings show that the applications of BN modelling in the wind energy industry are quite diverse.
, 2016). The nodes are connected via links to form different structures, including serial, converging and diverging. Fundamentally, Bayesian reasoning is used to revise beliefs based on updated evidence; thus, when a variable/node receives an evidence, it is said to be instantiated. There are two stages involved in the BN learning process (learning the manner in which BN distributions can be obtained from complete or incomplete datasets): learning the network structure and learning the parameters of the distributions. Further details about BN structures can be found in  Ibe, 2011; Boudali and Dugan (2005a)  and  Jensen and Nielsen (2007).
BN software tools A number of software packages have been developed to date for BN modelling. These include: Microsoft MSBNx, Netica, Hugin, WinBUGS, BayesiaLab, OpenBayes, AgenaRisk and Bayesfusion. Some of these software tools have gained popularity in real life applications, depending on the context for which they are used and the scale to which they can be applied. The most widely used tools are briefly described in the following sub-sections.
Microsoft MSBNx This is a component-based Windows application for creating, assessing, and analysing BNs  (Horvitz et al. , 2001). The tool is used to extract information from input database patterns as well as to show dependencies (conditional probabilities) between variables in a network. It can be used to incorporate inspection results and repair information to help asset managers perform fault diagnosis and analyse the costs/benefits of repair actions in a more timely and precise manner. An advantage it offers is that it can be integrated with other programmes to help with inference and decision-making when uncertain parameters or variables are involved. It uses Value of Information (VOI) analysis to estimate the benefit of obtaining improved posterior probabilities  (Friis-Hansen, 2000).
HUGIN HUGIN stands for Handling Uncertainty in General Inference Network. The tool is used to model parameters in a probabilistic network that are connected conditionally. It has different modules to develop, edit and analyse the BNs. These modules include a graphic user interface (GUI) module, an editor module and a compiler module. HUGIN develops a causal probabilistic network in such a way to support updating with new information for creating improved posterior probability distribution results  (Andersen et al. , 1989). In the context of wind energy industry, this software tool was used in  Ciobanu et al. (2017).
WinBUGS WinBUGS is an offshoot of the BUGS (Bayesian inference Using Gibbs Sampling) software tool (https://www. mrc-bsu. cam. ac. uk/software/bugs/the-bugs-project-winbugs/). It was developed for processing DAGs such as BNs. It is a user-friendly software that can be utilised to analyse different Bayesian models using different simulation methods or algorithms. With Gibbs sampling, when there is an update, each node is assigned an updated value based on the conditional dependence on the other nodes in the network. The modules can be extended by adding more components to the network and inputting new distribution functions within the system. The models can be presented by text or graphical form -if in text format, it is written in BUGS language. The modules making up the WinBUGS include graph, updater, monitors, BUGS, samples and Doodle.
BayesiaLab BayesiaLab is a desktop application that uses a GUI to support modelling, diagnosis, evaluation, simulation and optimisation (http://www. bayesia. com/). This application is made up of optimised learning algorithms that can execute both exact and approximate inferences with a property called observational inference. It also updates conditional probabilities of variables and analyses how this update may affect the network propagations, allowing the analyst to find explanations for the observations within the network. BayesiaLab discretises all the continuous data inputted. It has the capability to capture the conditional probability of all variables even when the inputs are not defined, no matter how large they are. It has a 'clustering' feature which allows for data and parameter grouping, and this is useful for large input datasets  (Conrady and Jouffe, 2013).
AgenaRisk The AgenaRisk software is used to build BN models for qualitative risk assessment problems (https://www. agenarisk. com/). In order to produce the posterior probabilities, it uses algorithms such as dynamic discretisation algorithm and ranked mode method, which are both useful for modelling the continuous and ranked variables, respectively. The software can be used for 'what-if' simulations and sensitivity analysis, because new observations (from, for instance, the inspections) can be inputted into any of the nodes within the network. Even when there is insufficient data, the model can be updated with new information. It is possible that both forward and backward inferences are made for new probability distributions. The output can also show the risk impact of different variables considered in the model.
Review methodology In this study, the literature from academic and industry sources including journal articles, conference proceedings, PhD dissertations, industry reports, best practice documents and software user guides were collated for review. Different databases such as Scopus, Web of Science, IEEE Xplore Digital Library and Google scholar were searched to identify relevant studies published on the subject. The search was based on two keywords of "Bayesian network" and "wind energy" and some inclusion and exclusion criteria were defined to streamline the search content to relevant literature for the review. After reviewing the titles, abstracts and the full texts, 70 documents were eventually considered for analysis. Our analysis indicates that, in recent years, there has been a significant increase in the number of studies applying BNs in the wind energy industry.
The model was a two-layer Probabilistic Signed Directed Graph (PSDG), which was made up of sensor data input and a possible-fault list input. Two sets of possible fault rankings were collected from the two layers of data input until they match. The authors used stochastic techniques to convert qualitative data into quantitative data, for ease of running the model. The method, then, uses a ranking technique to select the main faults from both layers to select the most appropriate fault type. These steps are iterated for a number of times to ensure the right fault is selected. Other references in this field include:  Asgarpour and Sørensen (2018b); Fernández-Cantí et al. (2015); De Bessa et al. (2016) ;  Joshuva and Sugumaran (2018) ;  Zhong et al. (2019) ;  Moghaddass and Sheng (2019); and Cai et al.
Case studies in risk assessment In a case study about risk assessment of wind turbines,  Ashrafi et al. (2015)  captured both continuous and discrete parameters using the AgenaRisk BN software and presented several factors for a more robust analysis. The environmental, organisational, human and technical factors were considered to improve the conditional probabilistic output. The continuous nodes represented the time to failure whereas the discrete nodes represented the system states. The BN technique was used to make inference regarding the system's reliability given the different contributing factors. The case study showed that the BN analysis was useful for monitoring the risk level and system's reliability and safety in continuous operation. Kougioumtzoglo (2015)  performed a study where BNs were used for ranking the criticality of wind turbine components based on their probability of failure.
Case studies in fault diagnosis and prognosis In a study,  Plumley et al. (2012)  applied the BN method to diagnose failures in wind turbines. The method was tested with a diagnostic program within the LabVIEW software (http://www. ni. com/engb/shop/labview. html) to simulate different failure modes in a wind turbine gearbox. The input variables included the parameters related to lubricant condition within the system (temperature, ferrous particle count and viscosity). Three sensors were used for data collection, which underpinned a condition monitoring method for estimating the system condition given input data. The reporting was done based on a traffic light system to help maintenance decision-makers. The second case study generated a DBN to determine the degradation rate of a wind turbine gearbox system under different conditions.
The GeNIe software tool (https://www. genieonline. com/program/) was used to create the DBN model. The condition monitoring data from the sensors were used to estimate the degradation state of the gearbox. The DBN probability estimates were based on updates from new data (temperature and particle count), which were fed into new time slices of the continuous model. It presented the application of probabilistic estimates based on the updates to inform PM decisions. In another study by  Stutzmann et al. (2017) , fatigue crack size distribution for an offshore wind turbine monopile structure was calculated using inspection data obtained by eddy current (EC) technique. An analysis was performed to reduce the uncertainty posed by the method of inspection. The POD by EC technique was found to be higher than that by other NDT techniques.
- Case studies in reliability assessment  Song et al. (2018)  applied a Bayesian approach to assess the health state of wind turbines. The case study was based on two 1. 5MW wind turbines. The 10-minutes data was obtained from SCADA system for a period of two month. The three input variables included in the model were wind speed, power output and generator speed. Two case studies were provided, including a classical case involving the use of wind speed and power output variables and another case involving all three input variables, to evaluate how reliable and robust the results from Bayesian approach are when more data is available. This was because whenever inferences are made, it will be easier to make a trade-off between accuracy and data training. The second case involved splitting the data set into five subsets.
This led to a reduction in total lifecycle cost by 23%. Dinwoodie (  2013 ) presented a case study on an offshore wind farm consisting of 60 wind turbines installed at 45m water depth. The failure rate distributions for wind turbine components such as gearbox, generator, blade and bearings were calculated. Three decision points during the lifecycle were considered: (i) early life strategy, (ii) strategy at year seven, and (iii) strategy for the remaining lifetime. In the case study, the uncertainties upon which the BN was built were primarily the failure rate and the electricity price. The analysis relied on both the inspection data as well as subjective expert opinions. A risk profile was also generated and the variables causing the highest risk were identified. Table  1  presents the case studies on different wind turbine components that have used Bayesian statistical methods in the reviewed literature.
Generating a probability distribution of weights is valuable for probabilistic modelling; but one key reservation regarding the applicability of this model lies in the fact that the prediction is expected to operate in real-time. However, its behaviour and performance were evaluated using pre-recorded wind speed and wind power data and evaluated offline. Additional research needs to be conducted on the real-time impact of BNN prediction scheme. Also, since the power predictions can only be reliable when there are no abrupt changes, it is difficult to state how ready this technology is to be applied to real scenarios outside of the modelling environment. If this can be improved, assumptions need to be validated for accurate results in practical terms for O&M managers  (Blonbou, 2011). BNs are useful for short-term probabilistic wind power forecasting and are also suitable for optimising model parameters when other tools are used to forecast the wind speed.
Risk assessment Risk assessment is at the heart of reliability and availability of wind turbines as wind turbines can only be efficient and available if they maintain high reliability and safety levels. Risk assessment is carried out in order to estimate the likelihood of occurrence and severity of hazards that may damage the integrity of wind farms or cause harm to the life of personnel. A number of tools and techniques are used for risk assessment, e. g. FMECA, HAZID, etc. In order to carry out risk assessments, it is more suitable to use a HBN, which has the capability to model dynamic states within nodes. Some particular properties of BNs as they relate to risk assessment are as follows  (Ashrafi et al. , 2015) : • Additional information and data about the maintenance activities can be implemented in BN and expanded.
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