On the Application of Fuzzy Decision Theory in Chemotherapy

1IntreductionTheliteratUreonmulti-Criteriondecisionmaking(MCDM)problemshas~tremendouslyintherecentpast.TwomajorareashaveevolvedwhiChbothconcentrateondecisionmakingwithseveralcriteria:multiobjectivedecisionmaking(MODM)andmulti-attributedecisionmaking(MADM).TheformerconcentratesoncontinuousdecisionspaceandthelatterfocusesonproblemswithdiscreteSPace.FuzzysettheoryhascontributedtoMODMproblemsaswellastheMADMProblems.ThegeneralMODMproblemcanbedeft.edLllasfollows:Twostagescangenerallybe…     

An improved approach to evaluate car sharing options

We develop an improved approach to evaluate car sharing options under uncertain environments with the combination of Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS), which consists of three steps. In the first step, we propose a SCUMN (Specific, Comprehensive, Understandable, Measurable, and Neutral) methodology to identify appropriate indicators and obtain a final list of 24 indicators according to their relevance to car sharing options. In the second step, we determine the weight of each indicator with F-AHP and conduct consistency check of the comparison matrix of selected indicators. In the third step, comparison of different options is performed with selected indicators and F-TOPSIS. A case study is provided to validate the proposed approach. Twenty-four indicators are identified to evaluate five different car sharing options and rank them according to their closeness coefficients in decreasing order. And thirty-one sensitivity analysis experiments are conducted to figure out the influence of indicators on decision making. The experimental results show that the proposed approach is capable of evaluating car sharing options with uncertainty and vagueness. F-AHP is able to determine the weight for each selected indicator and F-TOPSIS demonstrates its advantage in comparing potential options.     

Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS)

Artificial immune recognition system (AIRS) classification algorithm, which has an important place among classification algorithms in the field of artificial immune systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including breast cancer, Cleveland heart disease, diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by fuzzy-logic. This system, named as fuzzy-AIRS was used as a classifier in the diagnosis of lymph diseases, which is of great importance in medicine. The classifications of lymph diseases dataset taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. While only AIRS algorithm obtained 83.138% classification accuracy, fuzzy-AIRS classified the lymph diseases dataset with 90.00% accuracy. For lymph diseases dataset, fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.     

Resolving the Conflicts Between Biodiversity Conservation and Socioeconomic Development in China: Fuzzy Clustering Approach

Resolving the conflicts between biodiversity conservation and socioeconomic development is a global pursuit for the long-run prospects of the human species. Based on Wenchuan County, a typical county in southwestern China, a group of 20 indicators quantifying regional biodiversity and socioeconomic development was established to classify and evaluate the county area spatially. A fuzzy c-means clustering (FCM) algorithm was used as the classification method. Three indices including BD, DL and DR characterizing the value of biodiversity, the level and rate of socioeconomic development of the delineated regions were formulated. The results indicated that Wenchuan County was optimally classified into 4 types of regions (region I to IV). The area percentages of the regions vary widely from 4.3 to 65.7%. The sequences of the regions on biodiversity, socioeconomic development level, and socioeconomic development rate were, respectively, IV > II > III > I, I > III > II > IV and III >I >II >IV. The spatial strategy on coordinating biodiversity conservation and regional development is to develop mainly from the east(I, II, III) and to conserve mainly in the west(IV). Eco-industry, such as eco-tourism and eco-agriculture, need to be emphasized in the process of regional development. The quantitative methods used here may have a wide applicability.     

Habitat ecological integrity and environmental impact assessment of anthropic activities: A GIS-based fuzzy logic model for sites of high biodiversity conservation interest

In literature, an effective method enabling the classification, based on a single indicator, of habitats that need a priority protection intervention has not been identified yet. Moreover, the excessive number of landscape metrics, used to quantify integrity of habitats, can cause confusion, often providing redundant and inconsistent results.The aim of this work is to develop a method for evaluating the ecological vulnerability of the habitats in sites of high biodiversity conservation interest. In the first phase, we selected and analyzed, by using principal component analysis (PCA) and fuzzy logic, the landscape metrics, in order to obtain the map of the intrinsic ecological vulnerability index. In the second step, the result of this intrinsic vulnerability was connected, through another fuzzy model, to anthropogenic impacts, obtaining the integrated ecological vulnerability index. We developed specific spatial indicators (landscape metrics), which can examine the mutual position and morphology of the habitats present, along with indicators of human pressure, related to the type and intensity of use of the anthropic territory, with reference to the habitat itself as well as to the areas immediately adjacent. The developed fuzzy models are innovative, compared to the current ecological studies, and examine landscape metrics as well as the impact of human activities.The case study is the “Val Basento-Ferrandina Scalo” Site of Community Importance, Ferrandina-SCI (Basilicata Region, Southern Italy). The results allowed us to build a rank of the habitats based on their intrinsic and integrated ecological vulnerability. Moreover, the results show that, in the Ferrandina-SCI, the most important source of concern is not human activities, but rather the inherent risk of ecological fragility caused by geographical and landscape features of the different patches of habitats themselves.This model aims to be a tool for decision support in sustainable landscape management. It is easy to use and to apply on other regions, although it should always be accompanied by a sensitivity analysis to reduce the subjectivity.     

Comment on “Assessing water quality of five typical reservoirs in lower reaches of Yellow River,China: Using a water quality index method” by Wei Hou,Shaohua Sun,Mingquan Wang,Xiang Li,Nuo Zhang,Xiaodong Xin,Li Sun,Wei Li,and Ruibao Jia (2016) [Ecological Indicators, 61, 309–316]

Hou et al. (2016) recently developed a water quality index (WQI) for assessing water quality of five typical reservoirs. Despite all the merits of the practical WQI, it suffers from lack of uncertainty consideration; a fact that motivated the present discussion focusing on mitigation of uncertainty in water quality assessment. In this regard, superiority of employing fuzzy WQI (FWQI) rather than crisp WQI is emphasized. Due to robustness of FWQI in handling uncertainties surrounding data acquisition, employment of fuzzy concept can improve water quality assessment and monitoring to generate results which are more consistent with real world conditions.     

Application of fuzzy VIKOR for evaluation of green supply chain management practices

Environmentally sustainable activities have received an increasing interest among the firms to improve their practices in the supply chain. Although environmental regulations force firms consider these issues, but, green issues are new, evolving every day, and requires a continuous study in the field to gain a complete understanding of the problems. In this study, we illustrate the case of a laptop manufacturer in Malaysia that pursues to evaluate green supply chain management (GSCM) indicators among its practitioners. This paper develops a quantitative evaluation model to measure the uncertainty of GSCM activities and applies an approach based on Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method which is an extension of intuitionistic fuzzy environment aiming to solve the green multi-criteria decision making (GMCDM) problem. The triangular fuzzy numbers (TFNs) were used to handle imprecise numerical quantities. Then, a hierarchical multiple criteria decision making (MCDM) model was proposed based on fuzzy sets theory and VIKOR method to deal with the problem. The results show the alternative ranks of the four evaluated companies which was based on their performance in GSCM initiatives. The results also indicated that the main criteria of the research ranked as follows respectively: eco-design, green production, green purchasing, green recycling, green transportation and green warehousing. Finally, a comparative analysis of results by fuzzy VIKOR is presented. Additionally the scope for future studies is provided at the end of the paper.     

On‐line yeast propagation process monitoring and control using an intelligent automatic control system

The monitoring and control of bioprocesses is a challenging task. This applies particularly if the actions to the process have to be carried out in real‐time. This work presents a system for on‐line monitoring and control of batch yeast propagation under limiting conditions based on a virtual plant operator, which uses the concept of intelligent control algorithms by means of fuzzy logic theory. Process information is provided on‐line using a sensor array comprising the measurement of OD, operating temperature, pressure, density, dissolved oxygen, and pH value. In this context practical problems arising through on‐line sensing and signal processing are addressed. The preprocessed sensor data are fed to a neural network for on‐line biomass estimation. The root mean squared error of prediction is 4 × 10⁶ cells/mL. The proposed system then triggers temperature and aeration by usage of a temperature dependent metabolic growth model and sensor data. The deviation of the predicted biomass from that of the reference trajectory as modeled by the metabolic growth model and its temporal derivative are used as inputs for the fuzzy temperature controller. The inputs used by the fuzzy aeration controller are the deviation of measured extract from that of the reference trajectory, the predicted cell count, and the dissolved oxygen concentration. The fuzzy‐based expert system allows to provide the desired yeast cell concentration of 100–120 × 10⁶ cells/mL at a minimum residual extract limit of 6.0 g/100 g at the required point of time. Thus, a dynamic adjustment of the propagation process to the overall production schedule is possible in order to produce the required amount of biomass at the right time.     

基于数字土壤制图技术的土壤有机碳储量估算

精准的土壤属性空间分布信息有助于提升土壤有机碳储量估算的精度。本研究以河南省济源市南山林场为研究区,以地形因子为预测因子,利用模糊C均值(FCM)聚类方法对土壤有机碳含量、土壤容重、土壤厚度和土壤砾石含量进行数字土壤预测制图,基于数字制图结果实现土壤有机碳密度预测制图和土壤有机碳储量估算。结果表明: 基于数字土壤制图方法得到的研究区土壤有机碳密度平均值为4.24 kg·m^-2,其预测图的平均误差(ME)为0.08 kg·m^-2,平均绝对误差(MAE)为2.80 kg·m^-2,均方根误差(RMSE)为5.03 kg·m^-2,与传统类型方法相比,预测结果的精度和稳定性更高,具有较高的可信度,最终估算得到研究区土壤有机碳储量为3.08×10⁸ kg。基于数字土壤制图技术仅采用少量土壤样点即可实现较高精度的土壤有机碳密度制图和储量估算,且能表征土壤有机碳密度空间分布特征。本研究为土壤有机碳储量估算提供了新途径,有助于提升土壤有机碳储量估算的精度和效率。     

Developing a scuba trail vulnerability index (STVI): a case study from a Mediterranean MPA

Scuba diving is now one of the major form of commercial use of marine protected areas (MPAs) around the world and the control of its potential impacts on the marine environment represents a fundamental key to manage this recreational activity in highly dived areas. A potential tool to tackle such issues has been thought to be the definition of a value of recreational carrying capacity of an area, but this approach has been rarely considered management-effective. Therefore, the first step for effectively managing scuba-diving should be ‘bottom-up’: characterizing the benthic communities potentially affected by diving and evaluating their vulnerability. Aim of this paper is to propose a tool to define an index of vulnerability for dive trails (STVI: scuba trail vulnerability index). This has taken into consideration both physical and biological features of each trail. All the considered features are represented by non-quantitative variables, because either they are purely qualitative or their quantitative measurement is impractical. The management of such qualitative information and its translation into a formal methodology was performed by means of fuzzy logic, which has been repeatedly proposed as a powerful technique to develop indices of environmental quality. The approach adopted in this study provided a useful tool for the preliminary assessment of the potential vulnerability of benthic assemblages to scuba-diving and may represent an alternative method to the assessment of carrying capacity. The application of this index will enable management strategies for potentially reducing the degradation of benthic organisms/assemblages, and allowing a sustainable use of MPAs.