A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

A kinetic description of the impact of agent competence and psychological factors on investment decision-making

The kinetic theory is employed to analyze influence of agent competence and psychological factors on investment decision-making.We assume that the wealth held by agents in the financial market is non-negative,and agents set their own investment strategies.The herding behavior is considered when analyzing the impact of an agent's psychological factors on investment decision-making.A nonlinear Boltzmann model containing herding behavior,agent competence and irrational behavior is employed to investigate investment decision-making.To characterize the agent's irrational behavior,we utilize a value function which includes current and ideal-investment decisions to describe the agent's irrational behavior.Employing the asymptotic procedure,we obtain the Fokker-Planck equation from the Boltzmann equation.Numerical results and the stationary solution of the obtained Fokker-Planck equation illustrate how herding behavior,agent competence,psychological factors,and irrational behavior affect investment decision-making,i.e.,herding behavior has both advantages and disadvantages for investment decision-making,and the agent's competence to invest helps the agent to increase income and to reduce loss.

Continuous-cyclic variations in the b-value of the earthquake frequency-magnitude distribution

Seismicity of the Earth （M ≥ 4.5） was compiled from NEIC, IRIS and ISC catalogues and used to compute b-value based on various time windows. It is found that continuous cyclic b-variations occur on both long and short time scales, the latter being of much higher value and sometimes in excess of 0.7 of the absolute b-value. These variations occur not only yearly or monthly, but also daily. Before the occurrence of large earthquakes, b-values start increasing with variable gradients that are affected by foreshocks. In some cases, the gradient is reduced to zero or to a negative value a few days before the earthquake occurrence. In general, calculated b-values attain maxima 1 day before large earthquakes and minima soon after their occurrence. Both linear regression and maximum likelihood methods give correlatable, but variable results. It is found that an expanding time window technique from a fixed starting point is more effective in the study of b-variations. The calculated b-variations for the whole Earth, its hemispheres, quadrants and the epicentral regions of some large earthquakes are of both local and regional character, which may indicate that in such cases, the geodynamic processes acting within a certain region have a much regional effect within the Earth. The b-variations have long been known to vary with a number of local and regional factors including tectonic stresses. The results reported here indicate that geotectonic stress remains the most significant factor that controls b-variations. It is found that for earthquakes with Mw ≥ 7, an increase of about 0.20 in the b-value implies a stress increase that will result in an earthquake with a magnitude one unit higher.

The effect of the magnitude uncertainty on the b value

Since the magnitude is usually not the same from one catalog to another for the same earthquake, the calculated b value will change with different catalogs. Big b error is usually introduced when the magnitudes of the events are not well measured. For this reason, based on the uncertainty of the observed magnitude, i.e., the 'apparent' magnitude, the frequency-magnitude relation (Gutenberg-Richter relation) was estimated by assuming the normal distribution of the apparent magnitude around the 'true' one. The number of the events with same 'true' magnitude was recalculated according to this distribution. The error of the b value obtained with the 'true' magnitude is largely reduced by taking into account the distribution of the 'apparent' one. In order to show the advantage of the present method, the b values in the three zones in China are calculated with both ordinary and present method. Furthermore, in consideration of the magnitude saturation of the large events, the data set was divided into two pats on M=7.0. Results obtained by the method presented in this paper are of higher precision than those with ordinary method.

Effect size in papers published by the Journal of Forestry Research:A missing treasure?

P values based on standard hypothesis testing are commonly reported in articles published by the Journal of Forestry Research(JFR).However,effect sizes are barely used and reported,even if they are of direct relevance to the primary questions of many of the published studies.The incorporation of effect sizes in studies published by JFR should be encouraged and promoted.Inclusion of effect sizes as a requirement in the journal guidelines will facilitate a major change in the way data are tested and interpreted,with the ultimate goal to exempt researchers from the custom of drawing conclusions merely based upon a dichotomous statistical result(P value).Such a policy can also lead to more informed decisions of whether identified effects are of practical relevance to the forestry.

A Study on the Factors Influencing Value-Added in the Cruise Ship Value Chain Based on the DEMATEL-ISM Model

Cruise value chain is to take the exchange of cruise products and services as the core in a certain spatial scope,and enterprises with core advantages within or between different industries establish associations in accordance with certain technical and economic conditions,so as to realise the multi-dimensional extension and value appreciation of the cruise value chain in the vertical and horizontal links,and ultimately establish a chain-network type of enterprise strategic alliance.This paper tries to analyse the value-added factors of the cruise industry chain by constructing a multi-level hierarchical structural model with reference to the influencing factor analysis methods of relevant literature-DEMATEL(Decision Making Experiment and Evaluation Experiment)and ISM(Interpretative Structural Model).The study shows that the innovation and scale value-added module in the upstream of the cruise industry chain is the core module of value-added of the whole cruise industry chain,and the value-added mainly originates from the design and manufacturing of cruise ships.The middle reaches of the cruise industry chain are mainly cruise operation enterprises,and the specificity of cruise operation determines that its brand value-added is mainly accomplished through the global layout of multinational corporations,and the cruise brand is able to drive the consumption demand and has value-added ability.The downstream value-added of the cruise industry chain is mainly realised through the increase in profits of cruise tourism service products.

Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.

Correcting the Gutenberg–Richter b-value for effects of rounding and noise

The effects of magnitude rounding and of the presence of noise in the rounded magnitudes on the estimation of the Gutenberg-Richter b-value are explored, and the ways to correct for these effects are proposed. For typical values, b = 1 and rounding interval △M = 0.1, the rounding error is approximately -10^-3 and it can be corrected to a negligible approximately -10^-5. For the same typical values, the effect of noise can be larger, depending on the characteristics of the noise distribution; for normally distributed noise with standard deviation σ = 0.1, the correct b-value may be underestimated by a factor - 0.97.

The spatiotemporal variation of the b-value and its tectonic implications in North China

In this study, we adopt an improved Bayesian approach based on free-knot B-spline bases to study the spatial and temporal distribution of the b-value. Synthetic tests show that the improved Bayesian approach has a superior performance compared to the Bayesian approach as well as the widely used maximum likelihood estimation （MLE） method in fitting the real variation of b-values. We then apply the improved Bayesian approach to North China and find that the b-value has a clear relevance to seismicity. Temporal changes of b-values are also investigated in two specific areas of North China. We interpret sharp decreases in the b-values as useful messages in earthquake hazard analysis.

An Analysis of the Value of Additional Information Provided by Water Quality Measurement Network

European Community policy concerning water is placing increasing demands on the acquisition of information about the quality of aquatic environments. The cost of this information has led to a reflection on the rationalization of monitoring networks and, therefore, on the economic value of information produced by these networks. The aim of this article is to contribute to this reflection. To do so, we used the Bayesian framework to define the value of additional information in relation to the following three parameters: initial assumptions (prior probabilities) on the states of nature, costs linked to a poor decision (error costs) and accuracy of additional information. We then analyzed the impact of these parameters on this value, particularly the combined role of prior probabilities and error costs that increased or decreased the value of information depending on the initial uncertainty level. We then illustrated the results using a case study of a stream in the Bas-Rhin department in France.

Adaptive Strategies for Accelerating the Convergence of Average Cost Markov Decision Processes Using a Moving Average Digital Filter

This paper proposes a technique to accelerate the convergence of the value iteration algorithm applied to discrete average cost Markov decision processes. An adaptive partial information value iteration algorithm is proposed that updates an increasingly accurate approximate version of the original problem with a view to saving computations at the early iterations, when one is typically far from the optimal solution. The proposed algorithm is compared to classical value iteration for a broad set of adaptive parameters and the results suggest that significant computational savings can be obtained, while also ensuring a robust performance with respect to the parameters.

Research Progress of the Planning Methods of Prior Conservation Areas

The planning of prior conservation areas is an important part of nature re- serve system, ：it is of great significance for biodiversity conservation. The methods of priority planning include hot-spots analysis, gap analysis, systematic conservation planning analysis and multi-criteria decision analysis. The key issues of priority area planning include the determination of the protect objects, the analyses of the protec- tion value and protection cost, the win-win development of protection and economy, and the changes of the management strategies of the protected areas. In this study, the features and research progress of different conservation methods were analyzed, and based on the discussion on the key issues of the planning of the protected areas, the application prospect of different methods was analyzed.

Tri-Intersectional Model of Leadership by Values:A Fuzzy Multi-Criteria Decision Making Open Technology Assessment System

Purpose-Nowadays successful organizations need to be masters at leadership by values to play in a constantly changing and transforming environment.But how can leaders and organizations effectively convene strategic and culture development based on values?This paper presents the Tri-Intersectional Model of Leadership by Values(TMLV)in which leaders and organizations can integrate a sustainable strategy,as well as a culture and value-based management system that simultaneously leverages human,financial,and social resources.With its three essential axes of values(economic-pragmatic,emotional-development,and ethical-social)at their intersection points,it allows leaders to focus on the strategy linkages:innovation-intersection between the economic-pragmatic values axis and the emotional-development values axis-allows them to develop sustainable innovations;survival-intersection between the economic-pragmatic values axis and the ethical-social values axis-enhances their organization’s survival;finally,sensibility-intersection between the economic-pragmatic values axis and the ethical-social values axis-makes them more humane and more socially-responsible.The application of the TMLV,using the Fuzzy Multi-Criteria Decision Making Open Technology Assessment System,can be a great inspiration for stimulating and working with values in organizations,as well as allowing leaders to develop a values-based,high-involvement,and performance-oriented culture.Methodology/Approach-This research considers empirical data analysis of the 37 case studies of the EU-InnovatE project(http//www.euinnovate.com)-a pioneering initiative to align innovation values to integrate the end user into the process of innovation and entrepreneurship related to a sustainable lifestyle and the green economy in Europe-using a fuzzy multiple-criteria decision making method and open technologies system,such as server-side PHP language,MariaDB Database,fork of MYSQL Database Management System,and JavaScript libraries to perform operation directly on the user’s browser.Findings-The application of the TMLV model,considering empirical analysis of the extracted values from the case studies,using the Fuzzy Multi-Criteria Decision Making Open Technology Assessment System,can be a great inspiration for stimulating and working with values in João organizations,as well as allowing leaders to develop suitable strategies and interventions for shaping a sustainable high-performance culture.Research implications-This research can be a starting point for further research to assess the effectiveness of the leadership model based on a decision-making open technology system in any given organization,as well as to invite researchers who have positive passion about working with values to participate in the improvement of this tool.Originality/value-The Tri-Intersectional Model of Leadership by Values using the Fuzzy Multi-Criteria Decision Making Open Technology Assessment System is presented as an evolution in leadership models that may be used to fuel innovation,survival,and a sensibility strategic focus that is necessary to optimize human and organizational performance and deliver effective solutions to the massive array of human,financial,and social problems we face today.

Determining the time and space domains forestimating b value in seismic zoning

To take the seismic zone that includes the great shock with M S8.5 as the statistical unit of estimating b value can often lead to more large variance, because the seismogenic zone of the great shock with M S8.5 are larger than that delineated in general seismic zone. Two-level statistical units are considered in this paper. The seismic province is the first level unit that is suitable for group of earthquakes including the great shock of M S8.5. A seismic province can be divided into several seismic zones. They can be taken as the second level unit for group of quakes in which the super magnitude of the greatest shock do not exceed 8. Because of the nonstationarity in time of seismic activity, the unbalancedness of data and differential of seismic temporal series feature in different areas need to be considered when we select the time period for estimating b value. According to local conditions, the time period is selected at one′s discretion in order to reflect seismicity level of this statistical unit in future 100 years.

Classification of Selfish and Regular Nodes Based on Reputation Values in MANET Using Adaptive Decision Boundary

A MANET is a cooperative network in which each node has dual responsibilities of forwarding and routing thus node strength is a major factor because a lesser number of nodes reduces network performance. The existing reputation based methods have limitation due to their stricter punishment strategy because they isolate nodes from network participation having lesser reputation value and thus reduce the total strength of nodes in a network. In this paper we have proposed a mathematical model for the classification of nodes in MANETs using adaptive decision boundary. This model classifies nodes in two classes: selfish and regular node as well as it assigns the grade to individual nodes. The grade is computed by counting how many passes are required to classify a node and it is used to define the punishment strategy as well as enhances the reputation definition of traditional reputation based mechanisms. Our work provides the extent of noncooperation that a network can allow depending on the current strength of nodes for the given scenario and thus includes selfish nodes in network participation with warning messages. We have taken a leader node for reputation calculation and classification which saves energy of other nodes as energy is a major challenge of MANET. The leader node finally sends the warning message to low grade nodes and broadcasts the classification list in the MANET that is considered in the routing activity.

Multi-Criteria Group Decision-Making Method Based on an Improved Single-Valued Neutrosophic Hamacher Weighted Average Operator and Grey Relational Analysis

This paper proposes a multi-criteria decision-making (MCGDM) method based on the improved single-valued neutrosophic Hamacher weighted averaging (ISNHWA) operator and grey relational analysis (GRA) to overcome the limitations of present methods based on aggregation operators. First, the limitations of several existing single-valued neutrosophic weighted averaging aggregation operators (i.e. , the single-valued neutrosophic weighted averaging, single-valued neutrosophic weighted algebraic averaging, single-valued neutrosophic weighted Einstein averaging, single-valued neutrosophic Frank weighted averaging, and single-valued neutrosophic Hamacher weighted averaging operators), which can produce some indeterminate terms in the aggregation process, are discussed. Second, an ISNHWA operator was developed to overcome the limitations of existing operators. Third, the properties of the proposed operator, including idempotency, boundedness, monotonicity, and commutativity, were analyzed. Application examples confirmed that the ISNHWA operator and the proposed MCGDM method are rational and effective. The proposed improved ISNHWA operator and MCGDM method can overcome the indeterminate results in some special cases in existing single-valued neutrosophic weighted averaging aggregation operators and MCGDM methods.

Importance Analysis of a Multi-state System Based on Direct Partial Logic Derivatives and Multi-valued Decision Diagrams

Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.

Data Mining for Flooding Episode in the States of Alagoas and Pernambuco—Brazil

The increasing volume of data in the area of environmental sciences needs analysis and interpretation. Among the challenges generated by this “data deluge”, the development of efficient strategies for the knowledge discovery is an important issue. Here, statistical and tools from computational intelligence are applied to analyze large data sets from meteorology and climate sciences. Our approach allows a geographical mapping of the statistical property to be easily interpreted by meteorologists. Our data analysis comprises two main steps of knowledge extraction, applied successively in order to reduce the complexity from the original data set. The goal is to identify a much smaller subset of climatic variables that might still be able to describe or even predict the probability of occurrence of an extreme event. The first step applies a class comparison technique: p-value estimation. The second step consists of a decision tree (DT) configured from the data available and the p-value analysis. The DT is used as a predictive model, identifying the most statistically significant climate variables of the precipitation intensity. The methodology is employed to the study the climatic causes of an extreme precipitation events occurred in Alagoas and Pernambuco States (Brazil) at June/2010.

Defining laboratory reference values and decision limits＇ populations, intervals, and interpretations

This article provides a brief overview of various approaches that may be utilized for the analysis of human semen test results. Reference intervals are the most widely used tool for the interpretation of clinical laboratory results. Reference interval development has classically relied on concepts elaborated by the International Federation of Clinical Chemistry Expert Panel on Reference Values during the 1980s. These guidelines involve obtaining and classifying samples from a healthy population of at least 120 individuals and then identifying the outermost 5% of observations to use in defining limits for two-sided or one-sided reference intervals. More recently, decision limits based on epidemiological outcome analysis have also been introduced to aid in test interpretation. The reference population must be carefully defined on the basis of the intended clinical use of the underlying test. To determine appropriate reference intervals for use in male fertility assessment, a reference population of men with documented time to pregnancy of 〈 12 months would be most suitable. However, for epidemiological assessment of semen testing results, a reference population made up ofunselected healthy men would be preferred. Although reference and decision limits derived for individual semen analysis test results will undoubtedly be the interpretational tools of choice in the near future, in the long term, multivariate methods for the interpretation of semen analysis alone or in combination with information from the female partner seem to represent better means for assessing the likelihood of achieving a successful pregnancy in a subfertile couple.

一种基于连续区间直觉模糊Theil测度的多属性群决策方法

针对区间直觉模糊信息的不确定性,提出了一种基于连续区间直觉模糊Theil(C-IVIFT)测度的多属性群决策方法。首先基于连续区间直觉模糊有序加权平均(C-IVIFOWA)算子定义了连续区间直觉模糊Theil测度,并研究该测度的一些性质;其次,构建以C-IVIFT测度偏差最小化为目标的最优化模型用以确定专家权重和属性权重,进而得到不同方案的综合属性值,并利用相似性函数和精确函数对方案进行排序;最后,通过ERP软件择优实例说明提出的决策方法的合理性和有效性。