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.

An In-depth Interpretation of the Universal Declaration of Human Rights With the Common Values of Humanity As the Research Paradigm

Interpreting the Universal Declaration of Human Rights from political,juridical and philosophical perspectives is es-sential for promoting the guiding principles of the Declaration,build-ing consensus on human rights,and advancing human rights practice in the new historical context.To conduct an academic,systematic in-terpretation of the Declaration that conforms to the trends of the times and answers the fundamental questions of the world,it is necessary to find a new research paradigm.The common values of humanity,namely peace,development,equity,justice,democracy and freedom,put forward by Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,provide the most explanatory and penetrating scientific paradigm for reaching the issue.This paper an-alyzes and reflects on the views,value foundation and principled(con-tractual)consensus of human rights in the Declaration,and narrates and foresees the far-reaching significance of the three global initia-tives(namely,the Global Development Initiative,the Global Security Initiative,and the Global Civilization Initiative)with the common val-ues of humanity as the soul in advancing the modernization of global human rights governance and building a new form of human rights civilization.

Constructive Postmodern Agriculture——Values-Based Rural Development and Chinese Process Philosophy

旨在表明中国的发展进程如何支撑中美在价值基础上的发展。本文分为八部分:(1)价值基础上的发展,(2)可持续发展的农村社区,(3)建设性的后现代农业,(4)西方可持续的农业,(5)良性农业的原则,(6)非普遍的发展蓝图,(7)中国的发展进程思想,(8)中国在西部的发展进程思想。

Curating Spaces of Hope: Coproducing Shared Values in Uncertain Times

This paper sets out a new paradigm of faith based organisation(FBO)called Curating Spaces of Hope.The paper sets out the paradigm and the interdisciplinary literatures into which the paradigm is applied namely,the diversifying belief landscape in the UK,the postsecular,the redefinition of FBOs,and liminality as the new norm in policy.The paper then turns to ethnographic research to evidence the ability of the paradigm to map and coproduce shared values,before considering applications of Curating Spaces of Hope in post-pandemic contexts in the north west of England through case studies with ecumenical Christian,non-religious,and Turkish Muslim and interfaith contexts.

ON THE ERROR OF QUADRATURE FORMULAE FOR CAUCHY PRINCIPAL VALUE INTEGRALS BASED ON PIECEWISE INTERPOLATION

We consider the computation of the. Cauchy principal value mtegral by quadrature formulaeof compound type, which are obtained by replacing f by a piecewise defined function F,[;]. The behaviour of the constants m the estimates where quadrature error) is determined for fixed i and which means that not only the. order, but also the coefficient of the main term of is determined. The behaviour of these error constants is compared -with the corresponding ones obtained for the. method of subtraction of the singularity. As it turns out, these error constants have, in general, the same asymptotic behaviour.

Application of Activity Based Costing in Customer Evaluation and Management

As plant develops, many industries＇ automatic level is very high leading to the increasing of indirect cost. Traditional costing methods, which use single standard of distributing indirect cost, cannot calculate cus- tomer cost accurately and satisfy managers any more. This paper is aimed on solving the aforesaid problem. In this paper we propose the thought of evaluating customer cost by using activity based costing（ABC） and the tra- ditional model of customer lifetime value（CLV） to improve the CLV model, then analyze the difference of using both models through an empirical study. As a result, ABC can account customer cost more accurately so that the CLV can help managers evaluate customer more effectively.

Managing Value-Based Activity Flexibility on Integrated ERP

On the background of integrated ERP development, activity-value-flexibility management (AVFM) is defined. By using economic-value-added (EVA) and corporate value creation as the objective of AVFM, custom value deviating rate, capital cost deviating rate, cash-flow-out per purchase deviating rate and cash-flow-in per sell deviating rate are developed to be the key responding variates for AVFM, and they also decide the rational quantity range for AVFM tactics. Method for rational AVFM tactics solution could be got by means of redesigning activity information process on integrated ERP.

A new investigation on drift of base-line value in geomagnetic observation

A new explanation on drift of base line (BL) value in geomagnetic observation was presented by means of detailed analysis on BL value of H variometer at Tianshui Observatory from 1991 to 1995, in association with some numerical simulation. It was confirmed that drift does not always exist. For variometers running normally for many years, drift appears to be zero. The temperature dependence of BL value is reversible below a certain temperature but irreversible above it. This irreversibility is the main reason that causes the BL value to show a monotonous declination with time, which has been mistaken for the drift in the past. As to the H variometer at Tianshui Observatory, no drift exists in BL value in these years. A new method was introduced to study the BL value variation with temperature by separating it into three parts.

Logic and logic-based control

This paper gives a matrix expression of logic. Under the matrix expression, a general description of the logical operators is proposed. Using the semi-tensor product of matrices, the proofs of logical equivalences, implications, etc., can be simplified a lot. Certain general properties are revealed. Then, based on matrix expression, the logical operators are extended to multi-valued logic, which provides a foundation for fuzzy logical inference. Finally, we propose a new type of logic, called mix-valued logic, and a new design technique, called logic-based fuzzy control. They provide a numerically computable framework for the application of fuzzy logic for the control of fuzzy systems.

Color-texture segmentation using JSEG based on Gaussian mixture modeling

An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.

Content-based image retrieval applied to BI-RADS tissue classification in screening mammography

AIM:To present a content-based image retrieval(CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification.METHODS:Breast density is characterized by image texture using singular value decomposition(SVD) and histograms.Pattern similarity is computed by a support vector machine(SVM) to separate the four BI-RADS tissue categories.The crucial number of remaining singular values is varied(SVD),and linear,radial,and polynomial kernels are investigated(SVM).The system is supported by a large reference database for training and evaluation.Experiments are based on 5-fold cross validation.RESULTS:Adopted from DDSM,MIAS,LLNL,and RWTH datasets,the reference database is composed of over 10000 various mammograms with unified and reliable ground truth.An average precision of 82.14% is obtained using 25 singular values(SVD),polynomial kernel and the one-against-one(SVM).CONCLUSION:Breast density characterization using SVD allied with SVM for image retrieval enable the development of a CBIR system that can effectively aid radiologists in their diagnosis.

Continuance intention to use the mobile interest-based community: An integrated theoretical model and empirical study

Purpose： This paper aims at an understanding of factors that influence the continuance intention to use mobile interest-based community applications, with a focus on the impacts of technology acceptance model （TAM） constructs and experiential value. Design/methodology/approach： Taking a hybrid model combining TAM and extended expectation confirmation model （ECM） as foundation, this study integrated experiential value into the research model. A survey method was adopted and the sample was constituted by 347 Chinese undergraduates who were experienced users of mobile interest-based community applications. Structural equation modeling was used to test the research model. Findings： Our findings suggest that 1） key determinants of user satisfaction with mobile interest-based community applications are confirmation, perceived usefulness （PU）, perceived ease of use （PEU） and experiential value. Both satisfaction and PU are directly correlated with continuance intention; 2） PU＇s impact on satisfaction and continuance intention has been confirmed again in this study. Although PEU has no direct impact on satisfaction and continuance intention, it may indirectly affect them via PU; 3） all the perceived experiential values （aesthetics, playfulness, service excellence and return on investment） have a positive influence on satisfaction. Research limitations： We did not examine the effects of individual user differences that may also be important for understanding satisfaction and continuance intention. Practical implications： The study findings can help service providers improve the use of mobile interest-based community applications. Originality/value： Our study contributes to a more systematic understanding of factors that influence continuous use of mobile interest-based community applications.

A Review of Vegetable Oil-Based Polymers: Synthesis and Applications

The reviewed work addressed the shift in focus from conventional polymers to bio-based and renewable polymers. The environmental attributes of the renewable polymers make them preferred choice of matrix. The properties of the matrices from renewable origin could compete in high end applications. The composition of the fatty acids in plant seed oil was discussed and the determination of the level of unsaturation used the iodine value. This review also extensively discussed the values of various fatty acid components present in the oils. Areas of application of the thermosetting polymers obtained from plant seed oils were also discussed.

Base-X Notation and Tri-Value Logic

This paper introduces the base-X notation and discusses the conversion between numbers of different bases. It also introduces the tri-value logic that is associated with the base-3 system.

多智能体深度强化学习研究进展

深度强化学习(Deep Reinforcement Learning,DRL)在近年受到广泛的关注,并在各种领域取得显著的成功.由于现实环境通常包括多个与环境交互的智能体,多智能体深度强化学习(Multi-Agent Deep Reinforcement Learning,MADRL)获得蓬勃的发展,在各种复杂的序列决策任务上取得优异的表现.本文对多智能体深度强化学习的工作进展进行综述,主要内容分为三个部分.首先,我们回顾了几种常见的多智能体强化学习问题表示及其对应的合作、竞争和混合任务.其次,我们对目前的MADRL方法进行了全新的多维度的分类,并对不同类别的方法展开进一步介绍.其中,我们重点综述值函数分解方法,基于通信的MADRL方法以及基于图神经网络的MADRL方法.最后,我们研究了MADRL方法在现实场景中的主要应用.希望本文能够为即将进入这一快速发展领域的新研究人员和希望获得全方位了解并根据最新进展确定新方向的现有领域专家提供帮助.

Modulus-Based Matrix Splitting Iteration Methods for a Class of Stochastic Linear Complementarity Problem

For the expected value formulation of stochastic linear complementarity problem, we establish modulus-based matrix splitting iteration methods. The convergence of the new methods is discussed when the coefficient matrix is a positive definite matrix or a positive semi-definite matrix, respectively. The advantages of the new methods are that they can solve the large scale stochastic linear complementarity problem, and spend less computational time. Numerical results show that the new methods are efficient and suitable for solving the large scale problems.

Intelligent Evidence-Based Management for Data Collection and Decision-Making Using Algorithmic Randomness and Active Learning

We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.

中国医保药品价格谈判回顾和展望

目的:总结2018—2023年中国医保药品价格谈判的实践经验,对未来的工作提出建议。方法:通过文献回顾、收集官方公布的数据和网络报道资料,对当前医保药品价格谈判的政策和实践进行总结分析。结果:谈判药品平均降价50%~60%,但也存在有待解决的问题。未来医保药品价格谈判工作需要更加关注价值谈判组织体系、标准和方法的建立,重视价值谈判的立法和规制,使价格谈判工作更加公正、公平和透明化。结论:我国的药品价格谈判成绩斐然,充分发挥了政府主导和市场机制的作用,通过价值谈判保障了创新药物的可得性和可及性,在新药需求无限性和医保基金有限性之间找到了平衡点。