Defuzzification in the process of managerial estimating the value of agricultural lands

Purpose. The main purpose of the research is to substantiate the methodological approach of defuzzification and to define its peculiarities in the process of estimating the value of agricultural lands. Methodology / approach. The research purpose included the use of a set of appropriate methods. In particular, the fuzzy logic techniques formed the basis of the research. The system approach was used in order to determine the role of land resources in the enterprise potential and the corresponding spheres of their management. The analysis and synthesis methods were used in the process of definition of impact factors of land resources value. The cartographic method was used for the needs of graphical display of humus content in the land plots of the analyzed enterprise. The generalization method was used in the process of forming conclusions. Results. The article defines the peculiarities of defuzzification in the process of estimating the value of agricultural lands. The results provided the methodological basis for considering the qualitative metrics in the process of estimation as well as for granting the numerical interpretation for linguistic variables. The corresponding methodology was overviewed at the example of land plot size. The quantitative reference limits for “small”, “medium” and “large” land plots were defined. Research results made it possible to form the sequence of stages, which are to be undertaken, in order to provide numerical values for qualitative characteristics of agricultural lands. A decision tree was built for the needs of formation of management decisions. According to the data of researched enterprise, the dependence of the value of agricultural lands (for the needs of management accounting) on the size of the land plot and the humus content was determined. Originality / scientific novelty. The article improves the methodological approach to determining the value of agricultural lands as of an element of enterprise potential based on the use of fuzzy logic techniques, which, in contrast to existing approaches, allows taking into account both quantitative and qualitative factors in the process of estimating the value of land resources for the needs of their management. Applying the respective approach increases the level of accuracy, relevance, and adequacy to market realities of the results of estimating the value of agricultural lands for the needs of their management. Practical value / implications. The results of the research provided an opportunity to improve the quality and efficiency of the process of estimating the value of agricultural lands. The corresponding process is characterized by a high level of complexity and uncertainty due to the presence of a significant number of qualitative factors influencing the value of the land resources. The approach considered in the article makes it possible to take into account the influence of relevant qualitative factors by giving them numerical certainty through the use of fuzzy logic techniques. The proposed approach will provide an opportunity to increase the accuracy and relevance of estimating the value of land resources as of an element of enterprise potential for the making of corresponding managerial decisions. The proposed methodological approach was implemented with the use of data of agricultural enterprise, which made it possible to take into account linguistic variables (land plot size and chemical properties of the soil) when forming the managerial decisions about land plots. The decision tree was also formed, which serves as a means of supporting management decisions in the process of forming the value of agricultural lands.

„Prošao život“ Jovana Radulovića – roman alegorija o nestanku jednog naroda

Јovan Radulovic introduced himself to the readership as a writer of main- land, karst Dalmatia, as well as the literary successor of Simo Matavulj. The first two decades of Radulović’s litarary work were oriented exclusively to the topics related to his homeland. Only since the novel Braća po materi has his interests spread to larger urban areas (Zadar, Šibenik, Zagreb, Belgrade, Ljubljana). By skillfully inserting a multitude of data about the Knin region, about its historical, geographical, social and cultural circum- stances, into the narrative of the novel Prošao život (1997), Radulović highlights the great significance and continuity of Serbian culture in Croatia. In this novel, says the writer, I paid homage to all those who successfully built what was called ’Serbian intelligence on the Coast’. The basic idea that unites all thematic, motivational, characterological, narrative, expressive and qualitative factors of the novel is reduced to the allegory of passed life. It permeates topics, characters, time, events ‒ all elements of the novel. The basic idea is diversified in many different ways and by using different stylistic means: contrasts, sym- bols, allusions, allegories. The idea is particularly emphasized in the passages describing everyday life of the people, as well as cultural and historical events. Even the characters have one common role: to form a coherent whole, to revitalize the collective spirit of the people that, by losing its natural habitat, has ceased to be what it was there. The novel is completely built on Christian symbolism: there is a calendar cycle of the most important Christian and Serbian holidays: Epiphany, Easter, Vidovdan, Saint Nicholas and Christmas; mention is made of Gojko’s Golgotha, wiping feet with a sinner’s hair, mourning the dead, the riders of the apocalypse who come in the form of Ignjat’s killers. The basic idea can also be interpreted as a symbol of the Christ-like suffering of an entire nation.

Analyzing Factors Inhibiting the Growth of the Church According to the Apostles and Their Relevance to the Challenges of the Church Today

Church growth is a phenomenon that is very commonly discussed by people, especially in the Christian world. Often church growth is seen from the quantity (the number of congregations growing rapidly and many) without regard to the quality of the congregation. Although the Bible does not specifically talk about church growth, the principle of church growth is understood in the words of Jesus "I will build my church and the gates of hell will not overpower it", (Matt. 16:18), the church can live and grow even though the number of membership / attendance do not change. According to Rick Warren, healthy church growth is multi-dimensional growth, namely a church that grows closer to each other through fellowship, grows more earnestly through discipleship, and grows stronger through worship (Waren, 2019). He further said that the growth of the church is the result of a healthy natural where the preaching of the Bible and the mission carried out are balanced. In the church there are factors that inhibit church growth, namely: quantitative factors, qualitative factors, organic factors, historical trauma factors, theological misunderstanding factors, religious factors. For this reason, the Church must experience changes in dealing with any situation and influence the world. The church can not only rotate in one place. The church must make a difficult change, namely by preaching the gospel of salvation to those in need, in order to be saved. The church must make new breakthroughs in an increasingly changing world.

Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors (IDETC2021-71570)

Scientific and engineering problems often require the use of artificial intelligence to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners, they have difficulties in accommodating big datasets, qualitative inputs, and multi-type responses obtained from different simulators, which has become a common challenge for data-driven design applications. In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned challenges simultaneously. The method is built upon the latent variable Gaussian process (LVGP) model where qualitative factors are mapped into a continuous latent space to enable GP modeling of mixed-variable datasets. By extending variational inference to LVGP models, the large training dataset is replaced by a small set of inducing points to address the scalability issue. Output response vectors are represented by a linear combination of independent latent functions, forming a flexible kernel structure to handle multi-type responses. Comparative studies demonstrate that the proposed method scales well for large datasets, while outperforming state-of-the-art machine learning methods without requiring much hyperparameter tuning. In addition, an interpretable latent space is obtained to draw insights into the effect of qualitative factors, such as those associated with “building blocks” of architectures and element choices in metamaterial and materials design. Our approach is demonstrated for machine learning of ternary oxide materials and topology optimization of a multiscale compliant mechanism with aperiodic microstructures and multiple materials.

Data-Driven Design via Scalable Gaussian Processes for Multi-Response Big Data With Qualitative Factors

Scientific and engineering problems often require an inexpensive surrogate model to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners in surrogate modeling, they have difficulties in accommodating big datasets, qualitative inputs, and multi-type responses obtained from different simulators, which has become a common challenge for a growing number of data-driven design applications. In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned challenges simultaneously. The method is built upon the latent variable Gaussian process (LVGP) model where qualitative factors are mapped into a continuous latent space to enable GP modeling of mixed-variable datasets. By extending variational inference to LVGP models, the large training dataset is replaced by a small set of inducing points to address the scalability issue. Output response vectors are represented by a linear combination of independent latent functions, forming a flexible kernel structure to handle multi-type responses. Comparative studies demonstrate that the proposed method scales well for large datasets with over 104 data points, while outperforming state-of-the-art machine learning methods without requiring much hyperparameter tuning. In addition, an interpretable latent space is obtained to draw insights into the effect of qualitative factors, such as those associated with “building blocks” of architectures and element choices in metamaterial and materials design. Our approach is demonstrated for machine learning of ternary oxide materials and topology optimization of a multiscale compliant mechanism with aperiodic microstructures and multiple materials.

Fusion of interval-valued neutrosophic sets and financial assessment for optimal renewable energy portfolios with uncertainties

Renewable energy has been actively researched and developed in many countries to replace the conventional energy resources that come from fossil fuels. As social and environmental awareness of the renewable energy has grown, it is essential to address both quantitative factors and qualitative factors in determination of the optimal renewable energy portfolio. This paper proposes a novel approach to integrate a financial model and a fuzzy model to analyze both quantitative and qualitative factors. The financial model is utilized to calculate the quantitative factors, thereby assisting experts make judgments more accurately in the fuzzy model. The fuzzy model is utilized to evaluate the qualitative factors based on the expert judgements. Moreover, this paper proposes multi-segment judgment model that analyzes the evaluation of different groups, including government, investor and user groups. The results show that each group has different priority order. For example, the highest-priority factor of Government, Investor and User is environmental (with a score of 0.665), economic (with a score of 0.854), and technological criteria (with a score of 0.771), respectively. The results also indicated that small-scale onshore wind energy, large-scale onshore wind energy and solar energy is the best option for Government, Investor and User, respectively.

Entrepreneurial Finance in the Twenty-first Century, a Review of Factors Influencing Venture Capitalist’s Decision

Investments in new ventures are risky due to lack of conventional form of quantitative information and untested products. Venture capitalists (VCs) are seen to target such new ventures for high-risk premium but with little success. Existing research has investigated and identified a variety of qualitative factors that impact VCs’ investment decisions; however, many research gaps still exist. Works published in the last two decades show the evolution in the preference of factors with the focus shifting from venture’s team and product to factors such as intellectual property rights, economic crisis and social capital. It was found that the factor’s role was limited to the binary scale (positive and negative), which undermines its effect. The purpose of this review is to provide a comprehensive framework of factors that influence VCs’ investment decisions and show theoretical research gaps. Accordingly, we have segmented factors into two macro-categories: ‘internal’ and ‘external environment’, and presented a detailed framework of the factors that influence VCs’ investment decisions. Further, we argue to consider the subjectivity of qualitative factors and to explore the role of a factor in the decision-making.

One Note for Fractionation and Increase for Mixed-Level Designs When the Levels Are Not Multiple

Mixed-level designs have a wide application in the fields of medicine, science, and agriculture, being very useful for experiments where there are both, quantitative, and qualitative factors. Traditional construction methods often make use of complex programing specialized software and powerful computer equipment. This article is focused on a subgroup of these designs in which none of the factor levels are multiples of each other, which we have called pure asymmetrical arrays. For this subgroup we present two algorithms of zero computational cost: the first with capacity to build fractions of a desired size; and the second, a strategy to increase these fractions with M additional new runs determined by the experimenter; this is an advantage over the folding methods presented in the literature in which at least half of the initial runs are required. In both algorithms, the constructed fractions are comparable to those showed in the literature as the best in terms of balance and orthogonality.