The Encyclopedia of Neutrosophic Researchers, 5th Volume

Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. In the past years the fields of neutrosophics have been extended and applied in various fields, such as: artificial intelligence, data mining, soft computing, decision making in incomplete / indeterminate / inconsistent information systems, image processing, computational modelling, robotics, medical diagnosis, biomedical engineering, investment problems, economic forecasting, social science, humanistic and practical achievements. There are about 7,000 neutrosophic researchers, within 89 countries around the globe, that have produced about 4,000 publications and tenths of PhD and MSc theses, within more than two decades. This is the fifth volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation, with an introduction contains a short history of neutrosophics, together with links to the main papers and books

Decision-maker Trade-offs In Multiple Response Surface Optimization

The focus of this dissertation is on improving decision-maker trade-offs and the development of a new constrained methodology for multiple response surface optimization. There are three key components of the research: development of the necessary conditions and assumptions associated with constrained multiple response surface optimization methodologies; development of a new constrained multiple response surface methodology; and demonstration of the new method. The necessary conditions for and assumptions associated with constrained multiple response surface optimization methods were identified and found to be less restrictive than requirements previously described in the literature. The conditions and assumptions required for a constrained method to find the most preferred non-dominated solution are to generate non-dominated solutions and to generate solutions consistent with decision-maker preferences among the response objectives. Additionally, if a Lagrangian constrained method is used, the preservation of convexity is required in order to be able to generate all non-dominated solutions. The conditions required for constrained methods are significantly fewer than those required for combined methods. Most of the existing constrained methodologies do not incorporate any provision for a decision-maker to explicitly determine the relative importance of the multiple objectives. Research into the larger area of multi-criteria decision-making identified the interactive surrogate worth trade-off algorithm as a potential methodology that would provide that capability in multiple response surface optimization problems. The ISWT algorithm uses an ε-constraint formulation to guarantee a non-dominated solution, and then interacts with the decision-maker after each iteration to determine the preference of the decision-maker in trading-off the value of the primary response for an increase in value of a secondary response. The current research modified the ISWT algorithm to develop a new constrained multiple response surface methodology that explicitly accounts for decision-maker preferences. The new Modified ISWT (MISWT) method maintains the essence of the original method while taking advantage of the specific properties of multiple response surface problems to simplify the application of the method. The MISWT is an accessible computer-based implementation of the ISWT. Five test problems from the multiple response surface optimization literature were used to demonstrate the new methodology. It was shown that this methodology can handle a variety of types and numbers of responses and independent variables. Furthermore, it was demonstrated that the methodology can be successful using a priori information from the decision-maker about bounds or targets or can use the extreme values obtained from the region of operability. In all cases, the methodology explicitly considered decision-maker preferences and provided non-dominated solutions. The contribution of this method is the removal of implicit assumptions and includes the decision-maker in explicit trade-offs among multiple objectives or responses

Three Essays on the Resiliency of Urban Infrastructure and Public Systems

My dissertation focuses on issues related to the resiliency of urban infrastructure and public systems. In the first chapter, I examine the effect of the financial condition of local governments on housing values and depreciation of the U.S. housing stock. Housing values bear the burden of municipal fiscal stress reflecting prospective and current homeowners reduced willingness-to-pay for housing. Using panel data from the American Housing Survey from 1984 to 2011, I estimate linear, quantile, and semiparametric varying coefficient (VC) models to examine these effects. The findings from the linear and quantile estimation are compared to the estimation of the VC models, which allow for a nonparametric, smoothed specification of building age. The results suggest that aspects of municipal solvency have differential effects across the distribution of housing values and building age. New, lower priced houses see the largest increases in housing values from larger cash and long-run solvency ratios, which reflect greater spending on infrastructure and other long-run capital investment projects, whereas older, lower priced houses benefit from increases in service-level solvency, suggestive of greater spending on public amenities. Moreover, the results indicate that the housing stock depreciates more slowly in municipalities with larger values of revenue and expenditure per capita, with implied annual depreciation rates ranging from over 0.4 to 0.7 percent. The second chapter examines the performance of U.S. municipal governments prior to, during, and following the financial crisis over the period 1997--2012. Fully nonparametric methods are employed to estimate technical efficiencies of cities, both over time and by U.S. Census Region, utilizing recently-developed statistical results. The results strongly suggest non-convexity of the local governments\u27 production set, calling into question the results of previous studies examining municipal efficiency. Furthermore, the results suggest that mean efficiency and productivity declined in several U.S. regions during the financial crisis, and in some instances have not returned to their pre-crisis level. Finally, in the third chapter, I look at the impact of a regulation change in San Francisco restricting owner move-in (OMI) evictions. In the 1990s, San Francisco saw a large number of OMI evictions, a no-fault eviction frequently used by landlords to remove units from the rent-controlled market. To prevent landlords from further exploiting the use of OMI evictions, San Francisco passed Proposition G in 1998. This regulation imposed higher costs on landlords utilizing this eviction type by restricting the usage of OMI evictions in San Francisco rent-controlled buildings. My identification strategy is supported by the use of a stochastic rent frontier which allows me to obtain a measure of housing quality that is a linear function of observables, thereby capturing the causal impact of the regulation on housing quality and allowing me to observe trends in tenant filtering. Using household-level renter data from the American Housing Survey, my results indicate that following Proposition G, rent-controlled units in San Francisco saw lower housing quality. Additionally, I find that this result holds even after controlling for the presence of an on-site building owner. These findings are likely driven by lower levels of building maintenance and upkeep, reflecting the higher costs resulting from Proposition G. These findings suggest that while this regulation intended to preserve the stock of controlled housing in San Francisco, it resulted in several unintended consequences

A novel combination of Cased-Based Reasoning and Multi Criteria Decision Making approach to radiotherapy dose planning

In this thesis, a set of novel approaches has been developed by integration of Cased-Based Reasoning (CBR) and Multi-Criteria Decision Making (MCDM) techniques. Its purpose is to design a support system to assist oncologists with decision making about the dose planning for radiotherapy treatment with a focus on radiotherapy for prostate cancer. CBR, an artificial intelligence approach, is a general paradigm to reasoning from past experiences. It retrieves previous cases similar to a new case and exploits the successful past solutions to provide a suggested solution for the new case. The case pool used in this research is a dataset consisting of features and details related to successfully treated patients in Nottingham University Hospital. In a typical run of prostate cancer radiotherapy simple CBR, a new case is selected and thereafter based on the features available at our data set the most similar case to the new case is obtained and its solution is prescribed to the new case. However, there are a number of deficiencies associated with this approach. Firstly, in a real-life scenario, the medical team considers multiple factors rather than just the similarity between two cases and not always the most similar case provides with the most appropriate solution. Thus, in this thesis, the cases with high similarity to a new case have been evaluated with the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This approach takes into account multiple criteria besides similarity to prescribe a final solution. Moreover, the obtained dose plans were optimised through a Goal Programming mathematical model to improve the results. By incorporating oncologists’ experiences about violating the conventionally available dose limits a system was devised to manage the trade-off between treatment risk for sensitive organs and necessary actions to effectively eradicate cancer cells. Additionally, the success rate of the treatment, the 2-years cancer free possibility, has a vital role in the efficiency of the prescribed solutions. To consider the success rate, as well as uncertainty involved in human judgment about the values of different features of radiotherapy Data Envelopment Analysis (DEA) based on grey numbers, was used to assess the efficiency of different treatment plans on an input and output based approach. In order to deal with limitations involved in DEA regarding the number of inputs and outputs, we presented an approach for Factor Analysis based on Principal Components to utilize the grey numbers. Finally, to improve the CBR base of the system, we applied Grey Relational Analysis and Gaussian distant based CBR along with features weight selection through Genetic Algorithm to better handle the non-linearity exists within the problem features and the high number of features. Finally, the efficiency of each system has been validated through leave-one-out strategy and the real dataset. The results demonstrated the efficiency of the proposed approaches and capability of the system to assist the medical planning team. Furthermore, the integrated approaches developed within this thesis can be also applied to solve other real-life problems in various domains other than healthcare such as supply chain management, manufacturing, business success prediction and performance evaluation

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Inter-regional child mortality, programme efficiency, and throughput: an evaluation of the Ethiopian health extension programme.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Background: Despite the remarkable improvement Ethiopia had made in the overall health outcomes, such as reducing under-five child mortality rate, there were substantial variations in the rate of progress across its administrative regions over different periods of time. Moreover, compared to many other developing countries, the progress that Ethiopia made in child mortality reduction remained low, and accounted for three percent of the share of global under-five child deaths in 2015. While the community-based health extension programme contributed to improving health outcomes of the population, such as reducing child mortality, access to and use of basic healthcare services are limited, with significant variations across regions of the country. Much less is known about the factors affecting the inter-regional variations in under-five child mortality: how efficient the health extension programme is in delivering basic healthcare services to its rural societies across regions and the determinant factors affecting the health extension programme beneficiary households' graduation in rural settings of Ethiopia. This study is therefore the first attempt to explore determinants of inter-regional differentials in under-five child mortality, and to evaluate the efficiency and productivity changes of the community-based health extension programme in rural areas of Ethiopia at the national level. This study addresses three specific objectives. These are: i) to examine the determinant factors affecting the inter-regional differentials in under-five child mortality. ii) to evaluate the efficiency and productivity growth (changes) of the community-based health extension programme. iii) to identify the determinant factors influencing the health extension programme beneficiary households' graduation. Methodology: This study employed cross-sectional secondary data from the Ethiopian demographic and health survey, 2016, for a total of 4,200 deaths of under-five children. It also utilised data from the regional health bureaus of Ethiopia, constituting a sample of 1,552 health posts and 4,244 rural households for the years 2013 and 2014. The statistical methods employed include the extended Oaxaca-Blinder decomposition to count data model, Data Envelopment Analysis, Tobit, ordinary least square, and the multiple logistic regressions. Results: The main findings, which addressed the first objective of the study, revealed that the regional differentials in under-five child mortality were due to socio-economic factors (such as mother's age at first birth, antenatal healthcare services, parental education, households' wealth status, and household size), proximate factors (such as child's birth spacing, child's birth order, and size of the child at birth), and environmental factors (such as place of delivery). However, their relative contributions in explaining the regional differences varied significantly within and across groups in the regional comparisons. The main findings, which addressed the second objective of the study, indicated that there was a substantial variation in technical and scale efficiency estimates among health posts, both across the regions and over periods of time. The results indicated that about 5.67 percent of health posts were a variable return to scale (VRS) technical efficient, with an average technical efficiency estimate of 79.6 percent in 2014. Moreover, most of the health posts (91.24 percent) were operating below their optimal scale size, indicating a potential for improving the efficiency of the health extension programme by improving the scale size, the efficiency of the scale as well as technically inefficient health posts. Furthermore, the overall productivity change increased by about 6.7 percent due to the technological progress. In a subsequent study, results of the regression analysis indicated that households' travel distance to the nearest health posts, provision of supportive supervision to the health extension workers, religion, and region of residence of the health extension workers affected the disparities in technical efficiency estimates among the health posts. The main findings addressing the third objective of the study explained the reason behind the rate of graduating households as model households. The results indicated that family size, head of the household head, parental level of education, households' access to the agricultural extension programme, mothers' age, and the professional level of the health extension workers were the major determinant factors affecting the health extension programme beneficiary households' graduation. Conclusion: The explained part of the regional differentials in under-five child mortality was due to differences in socio-economic, proximate, and environmental factors among the regions, with significant differences in the magnitude of the effect. Most of the health posts were operating below their optimal scale size, with substantial variations in technical and scale efficiency estimates, suggesting potential room for improving the efficiency of the health extension programme. Therefore, this study suggests the need for sustained efforts with a due focus on improving households' economic status, maternal education, sustained in-service training and supportive supervision provision to the health extension workers across regions of the country

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order