Applying an Ordinal Priority Approach Based Neutrosophic Fuzzy Axiomatic Design Approach to Develop Sustainable Geothermal Energy Source

Geothermal energy is considered a renewable,environmentally friendly,especially carbon-free,sustainable energy source that can solve the problem of climate change.In general,countries with geothermal energy resources are the ones going through the ring of fire.Therefore,not every country is lucky enough to own this resource.As a country with 117 active volcanoes and within the world’s ring of fire,it is a country whose geothermal resources are estimated to be about 40%of the world’s geothermal energy potential.However,the percentage used compared to the geothermal potential is too small.Therefore,this is the main energy source that Indonesia is aiming to exploit and use.However,the deployment and development of this energy source are still facing many obstacles due to many aspects from budget sources due to high capital costs,factory construction location,quality of resources,and conflicts of the local community.In this context,determining the optimal locations for geothermal energy sites(GES)is one of the most important and necessary issues.To strengthen the selection methods,this study applies a two-layer fuzzy multi-criteria decision-making method.Through the layers,the Ordinal Priority Approach(OPA)is proposed to weight the sub-criteria,the main criterion,and the sustainability factors.In layer 2,the Neutrosophic Fuzzy Axiomatic Design(NFAD)is applied to rank and evaluate potential locations for geothermal plant construction.Choosing the right geothermal energy site can bring low-cost efficiency,no greenhouse gas emissions,and quickly become the main energy source providing electricity for Indonesia.The final ranking shows Papua,Kawah Cibuni,and Moluccas as the three most suitable cities to build geothermal energy systems.Kawah Cibuni was identified as the most potential GES in Indonesia,with a score of 0.46.Papua is the second most promising GES with a score of 0.45.Next is the Moluccas,with a score of 0.39.However,the three least potential sites among the 15 studied sites are Lumut Balai,Moluccas and Patuha,with scores of 0.08,0.11 and 0.17,respectively.The conclusion of this study also classifies positions into groups to aid in decision-making.

The Accelerated Expansion of the Universe in the Light of the Maximum Ordinality Principle

The main aim of the paper is to present (and at the same time offer) a differ-ent perspective for the analysis of the accelerated expansion of the Universe. A perspective that can surely be considered as being “in parallel” to the tradition-al ones, such as those based, for example, on the hypotheses of “Dark Matter” and “Dark Energy”, or better as a “com-possible” perspective, because it is not understood as being “exclusive”. In fact, it is an approach that, when con-firmed by experimental results, always keeps its validity from an “operative” point of view. This is because, in analogy to the traditional perspectives, on the basis of Popper’s Falsification Principle the corresponding “Generative” Logic on which it is based has not the property of the perfect induction. The basic difference then only consists in the fact that the Evolution of the Universe is now modeled by considering the Universe as a Self-Organizing System, which is thus analyzed in the light of the Maximum Ordinality Principle.

Generativity of Self-Organizing Processes and Their Correlative Description in Terms of a Formal Language of Meta-Ordinal Generative Nature, in the Light of the Maximum Ordinality Principle and the Explicit Solution to the “Three-Body Problem”

The main objective of this paper is to demonstrate that the internal processes of Self-Organizing Systems represent a unique and singular process, characterized by their specific generativity. This process can be modeled using the Maximum Ordinality Principle and its associated formal language, known as the “Incipient” Differential Calculus (IDC).

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

Three-Drug Therapies in Psychiatry in the Light of the Maximum Ordinality Principle and the Explicit Solution to the “Three-Body Problem”—D.D. 23 Luglio 2023, Tempo Ordinario (3.00 e 10.20)

The present paper aims at showing the possible adoption in Psychiatry of a general methodology finalized to prescribe the most appropriate Therapy based on the knowledge of its correlative effects in advance, instead of recognizing them ex post. The specific case here considered is the “bipolar disorder”, in which the adoption of three different drugs is the most common practice, although with a possible differentiation between the prescription in the morning and in the evening, respectively. Thus, the proposed methodology will consider the Ordinal Interactions between the various drugs by evaluating their combined effects, which will result as being not a simple additive “sum”, because they are evaluated on the basis of the Maximum Ordinality Principle (MOP) and, in addition, in Adherence to the Explicit Solution to the “Three-Body Problem”. In this way the Methodology here proposed is able to suggest how to account for the synergistic effects of the various drugs, especially when the latter are characterized by different concentrations and, at the same time, by generally different half-lives respectively.

Solution to the “Three-Body Problem” in the Light of the Maximum Ordinality Principle, as a “Suggestion” for a Ri-Orientation of the Present Scientific Perspective in “Favor” of the “Irreducible Quality”

This paper presents the Solution to the “Three-body Problem” in the Light of the Maximum Ordinality Principle. In the first part, however, it starts with the Solution to the Solar System, made up of “11 Bodies”. This is because, in such a context, the “Three-body Problem” can be analyzed in its all descriptive possibilities. Nonetheless, the paper also presents the Solution to the “Three-body Problem” with reference to Systems totally independent from the Solar System, such as, for example, the “Triple Stars” and the “Triple Galaxies”. In this way, the paper offers a sufficiently complete framework concerning the Solution to the “Three-body Problem”, always in the Light of the Maximum Ordinality Principle, described in detail in Appendix A.

农民工城市融合路径及影响因素研究——基于三阶段Ordinal Logit模型的实证

本文依据浙江省农民工城市融合的调查数据,构建三阶段Ordinal Logit模型实证分析了农民工城市融合路径及影响因素。结果表明,农民工城市融合经历经济生存融合、社会交往融合和心理认同三阶段发展路径,经济生存融合由强到弱受职业资格、就业公平、婚姻状况、初始社会资本和居住条件的影响;社会交往融合由强到弱受居住格局、经济生存融合、经济生存状态的未来期许和上网时间的影响;心理认同由强到弱受社会交往融合、长期居留意愿和乡土记忆的影响。整合分析表明,经济生存融合极大制约了农民工城市融合的纵向提升,当前农民工城市融合仍停留在经济生存融合阶段。促进农民工城市融合的政策建议应着重考虑以上因素。

基于Ordinal Logistic模型的事故严重性影响因素分析——以翻车事故为例

为准确分析影响交通事故严重性的各项因素,引入Ordinal Logistic模型研究驾驶员、车辆、环境因素对事故严重程度的影响,利用统计学方法对模型进行显著性检验。并使用该模型对北卡罗来纳州2010—2014年385个翻车事故样本进行严重性影响因素分析,所得回归模型符合平行线检验(=0.115 1)和似然比检验(≤0.000 1)。通过模型分析得出变量"安全带"(≤0.000 1)、"路面状况"(=0.013 6)、"道路线形"(=0.005 7)对翻车事故的严重性具有显著影响,研究结果表明:Ordinal Logistic模型适合于分析与揭示影响交通事故严重性的各个因素。

农村学校撤并对学生上学距离的影响——基于GIS和Ordinal Logit模型的分析

农村义务教育学校布局调整是以撤并小规模学校为主要手段,旨在促进教育资源均衡配置的一项重大教育政策。通过广东省某山区县2003—2009年的学校布局规划数据,从物理距离、时间距离和文化距离的分类视角,运用GIS技术和Ordinal Logit模型分析农村学校撤并对学生上学距离产生的直接影响。研究结果表明:撤并农村学校导致学生上学的物理距离和时间距离增加,偏远学生上学难问题凸显;对学生的文化距离同样产生显著的负面影响。在未来学校布局调整的进程中,县域教育决策者应将关注点从一味追求规模效益转移到教育公平和教育质量上来。要保证农村学生就近入学,设定上学距离的限制;促进学校间教育资源的均衡配置,保证所有学生享受同等质量的教育资源;采用科学的规划程序完善布局调整政策的实施。

甲状腺良、恶性结节发病的影响因素及其Ordinal回归分析

目的了解不同人群中甲状腺良、恶性结节的发病情况及其影响因素。方法采取分阶段整群随机抽样方法,选取在某医院进行体检的人群进行一般情况及甲状腺疾病发生影响因素的问卷调查,数据资料采用Wilcoxon秩和检验和Kruskal-Wallis H检验,并进行Ordinal回归分析。结果共调查8 090名体检者,有效问卷8 039份,有效率99.37%。甲状腺正常者2 912名,占36.2%,结节性甲状腺肿、其他甲状腺良性疾病和甲状腺癌分别为2 470,1 539和1 118例,各占30.7%、19.1%和13.9%。单因素分析显示,工作单位、生存环境、从事职业、饮食习惯、电磁辐射、尾气尘埃等对体检人群发生甲状腺良、恶性结节的影响差异有统计学意义(P<0.05)。Ordinal回归分析显示,从事电化企业、银行工作、厨卫保洁的人群甲状腺疾病发生的概率分别是医药卫生系统的8.53倍(OR=8.53,95%CI:7.02~9.89)、1.69倍(OR=1.69,95%CI:1.26~1.94)和7.29倍(OR=7.29,95%CI:5.81~9.03)。与普通街区比较,生活或工作在干道工地旁、电器高压线旁和新近装修、尾气油烟尘埃之中为发生甲状腺疾病的危险因素。接触手机、电脑等电磁辐射越多,以及碘、钙、氟的过度摄入,甲状腺疾病发生的概率越高。结论甲状腺良、恶性结节发病的风险因素有手机、电脑、微波、电器等电磁辐射,建筑尘埃、雾霾、装修以及油烟尾气等;保护性因素则是花园式绿色环境。

基于Ordinal Logistic回归分析的慢性非传染性疾病患者治疗负担的现状研究

背景随着社会经济的发展和老龄化的加剧,慢性非传染性疾病(NCD)的发病率逐年上升,NCD需面临服用药物、自我监测、临床检查、就诊和自我管理等临床治疗和健康管理,给患者造成沉重的负担。目的探讨广东省东莞市长安镇NCD患者的治疗负担情况及影响因素。方法 2016年7月以广东省东莞市长安镇为研究点,采用多阶段分层整群随机抽样方法从13个社区各抽取4～6个小区,采用整群抽样方法每个小区随机抽取50户家庭,共纳入1 472例NCD患者。采用NCD患者治疗负担调查问卷对患者进行调查,包括一般人口学调查表、NCD情况调查表、卫生服务利用调查表、治疗负担量表(TBQ)。结果采用K类中心聚类分析法根据治疗负担程度将患者分为轻度组(721例)、中度组(540例)、重度组(211例),其中,轻度组TBQ总分为(24.4±10.5)分,中度组TBQ总分为(53.6±9.3)分,重度组TBQ总分为(85.1±13.6)分。3组医保类型构成情况、慢性疼痛、骨骼及结缔组织炎症、患NCD种数、近12个月内需住院而未住院、近12个月内住院所占比例比较,差异均有统计学意义(P<0.05)。Ordinal Logistic回归分析结果显示,城镇职工医保[OR=-1.046,95%CI(-1.786,-0.306)]、城镇居民医保[OR=-0.767,95%CI(-1.449,-0.086)]、患NCD种数为1种[OR=-0.364,95%CI(-0.660,-0.070)]和近12个月内住院[OR=0.605,95%CI(0.250,0.960)]是NCD患者治疗负担的影响因素(P<0.05)。结论东莞市长安镇NCD患者治疗负担以轻度治疗负担为主,医疗机构应加强NCD的监控和NCD患者健康管理,以达到降低患者的治疗负担、提高健康水平的目的。

基于Ordinal Logistic回归探讨儿童肺炎支原体肺炎的预后影响因素

目的探讨儿童肺炎支原体肺炎(MPP)的预后相关影响因素。方法回顾性分析2017年1月至2017年12月在上海中医药大学附属龙华医院中医儿科病房住院并接受治疗的250例MPP患儿的临床资料,评价所有患儿的临床疗效,采用Ordinal Logistic回归分析探讨影响MPP预后的因素。结果250例MPP患儿经治疗后,痊愈195例(78.0%)、显效43例(17.2%)、有效12例(4.8%)、无效0例(0%)。Ordinal Logistic回归分析结果表明,中西医结合配合外治和住院前使用中药均有利于MPP的预后,C反应蛋白(CRP)异常、血小板(PLT)计数升高和合并其他病原体则是影响MPP预后的危险因素。结论中西医结合配合外治及早期中药干预均有利于MPP患儿的预后,而合并其他病原体、CRP异常及PLT水平升高则提示MPP患儿预后较差。

基于Ordinal Logistic回归的徐州市区老年人生活满意度影响因素研究

目的:了解徐州市区老年人生活满意度及其影响因素。方法:采取分层随机整群抽样的研究方法,应用自行设计的问卷进行入户调查;采用SPSS19.0对老年的人生活满意度的影响因素进行单因素分析和Ordinal Logistic回归分析。结果:调查的1044位老年人中,对老年生活感到满意的占59.1%;主要影响因素按影响力由大到小分别为:自觉健康状况、是否患慢性病、医疗服务态度、医疗技术水平、就医方便程度、养老方式、社区是否定期提供精神文化服务。结论:健康状况是老年人生活质量的重要组成部分,按老年需求建设好社区医疗和服务,是提高老年人生活满意度的重要途径。

Dynamic programming methodology for multi-criteria group decision-making under ordinal preferences

A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the total inconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making （MCGDM） and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result.

Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels

The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)loss.However,when the label presents an intrinsic ordinal property in nature,e.g.,the development from benign to malignant tumor,CE loss cannot take into account such ordinal information to allow for better generalization.To improve model generalization with ordinal information,we propose a novel meta ordinal regression forest(MORF)method for medical image classification with ordinal labels,which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.The merits of the proposed MORF come from the following two components:A tree-wise weighting net(TWW-Net)and a grouped feature selection(GFS)module.First,the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree.Hence,all the trees possess varying weights,which is helpful for alleviating the tree-wise prediction variance.Second,the GFS module enables a dynamic forest rather than a fixed one that was previously used,allowing for random feature perturbation.During training,we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian matrix.Experimental results on two medical image classification datasets with ordinal labels,i.e.,LIDC-IDRI and Breast Ultrasound datasets,demonstrate the superior performances of our MORF method over existing state-of-the-art methods.

Excellent plasma electrolytic oxidation coating on AZ61 magnesium alloy under ordinal discharge mode

In the preparation of plasma electrolytic oxidation(PEO)coating,the rapid heating of freely-happened electron avalanche under traditional discharge(TD)mode inevitably results in a strong eruption of electric breakdown melt.The PEO coating is loose and invariably composed of a very thin inner dense layer and an outer loose layer,as a result of which its properties and application have been limited greatly.In this work,for purpose of weakening the eruption of breakdown melt,thickening the inner dense layer,densifying the outer loose layer and improving the performance of PEO coating,ordinal discharge(OD)mode of PEO coating is developed by regulating the mass ratio of MgF_(2) to MgO(α)and voltage in the PEO investigation on AZ61 magnesium alloy in KF-KOH electrolyte.The formation mechanism under different discharge mode,electrochemical corrosion and wear of PEO coatings are investigated.The results show that the suitableαand voltage for effective OD are 1.3 and 130 V under which the freely-happened electron avalanche in MgF_(2) under TD mode can be restricted by the adequate adjacent MgO.Compared with TD mode,the inner dense layer,in which the(10¯1)plane of MgF_(2) is parallel to the(111)plane of MgO at their well-knit semi-coherent interface,is thickened to 2.4∼7.2 times,the corrosion potential(E_(corr))improvement is enlarged to 3.6∼13.2 times and the corrosion current intensity(I_(corr))is reduced from 10.8∼9.499 to 0.433(10^(−6) A/cm^(2)).The outer loose layer is densified and the wear rate is lessened 65.5%∼89.8%by the evident melioration in surface porosity,impedance and hardness.This work deepens the understanding about the discharge of PEO coating and provides an available OD mode for preparing excellent PEO coating.

Increasing of Resistance and Resilience of an Urban System against Calamities in the Light of the Maximum Ordinality Principle

The present paper aims at showing how it is possible to requalify the structures of an urban system, in order to increase its resistance and its correlative resilience, against natural calamities (earthquakes, hurricanes, etc.), by adopting as reference criterion the Maximum Ordinality Principle (MOP). In this sense, the paper opens a radically new perspective in this field. In fact, the village assumed as a case study was modelled as a Self-Organizing System. This is because, although the village is usually considered as being solely made of buildings, streets, places and so on, in reality it has been conceived, planned and realized by human beings during several centuries. In addition, the people who actually leave in such an urban center, systematically deal with its maintenance, in order to possibly increase its functionality. This justifies the assumption of the village as being a Self-Organizing System and, consequently, it has been analyzed in the light of the MOP, which represents a valid reference principle for analyzing both “non-living”, “living” and “conscious” self-organizing systems.

ORDINAL REGRESSION FOR INFORMATION RETRIEVAL

This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effec- tiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM sig- nificantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.

Log-Link Regression Models for Ordinal Responses

The adjacent-categories, continuation-ratio and proportional odds logit-link regression models provide useful extensions of the multinomial logistic model to ordinal response data. We propose fitting these models with a logarithmic link to allow estimation of different forms of the risk ratio. Each of the resulting ordinal response log-link models is a constrained version of the log multinomial model, the log-link counterpart of the multinomial logistic model. These models can be estimated using software that allows the user to specify the log likelihood as the objective function to be maximized and to impose constraints on the parameter estimates. In example data with a dichotomous covariate, the unconstrained models produced valid coefficient estimates and standard errors, and the constrained models produced plausible results. Models with a single continuous covariate performed well in data simulations, with low bias and mean squared error on average and appropriate confidence interval coverage in admissible solutions. In an application to real data, practical aspects of the fitting of the models are investigated. We conclude that it is feasible to obtain adjusted estimates of the risk ratio for ordinal outcome data.

Ordinal Decision Trees

In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications.In this work,we propose a new measure of feature quality, called rank mutual information.Then,we design an ordinal decision tree(REOT) construction technique based on rank mutual information.The theoretic and experimental analysis shows that the proposed algorithm is effective.