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The first person to introduce possibility theory was Lotfi A. Zadeh, in 1977. It was, of course, of no coincidence that he directly combined it with the theory of fuzzy sets. Later, several researchers dealt with the mathematical foundations of the theory of possibilities. They introduced possibility distribution as a concept, and they directly combined it with fuzzy numbers. A fuzzy number corresponds to a possibility distribution and vice versa. This correspondence gave a key advantage to possibility theory over probability theory. This advantage is the facility of operations. However, there is also a basic: problem how is a possibility distribution generated? In this paper, we introduce a method of constructing a possibility distribution via a cumulative probability function. The advantage of this method is the simplicity of construction, which is nothing more than the construction of a fuzzy triangular or trapezoidal number via a cumulative probability function. This construction introduces a way to determine a fuzzy number without relying on the experience or intuition of the researcher. We should, of course, emphasize that this specific construction is within the framework of a theoretical model. We do not apply it to specific data. We also considered that the theoretical construction model should be presented through the theory of possibilities, thus avoiding the theory of probabilities. [ABSTRACT FROM AUTHOR]

In this research paper, a generator of fuzzy methods based on theorems and axioms of fuzzy logic is derived, analyzed and applied. The family presented generates fuzzy implications according to the value of a selected parameter. The obtained fuzzy implications should satisfy a number of axioms, and the conditions of satisfying the maximum number of axioms are denoted. New theorems are stated and proven based on the rule that the fuzzy function of fuzzy implication, which is strong, leads to fuzzy negation. In this work, the data taken were fuzzified for the application of the new formulae. The fuzzification of the data was undertaken using four kinds of membership degree functions. The new fuzzy functions were compared based on the results obtained after a number of repetitions. The new proposed methodology presents a new family of fuzzy implications, and also an algorithm is shown that produces fuzzy implications so as to be able to select the optimal method of the generator according to the value of a free parameter. [ABSTRACT FROM AUTHOR]

In this paper, we present a new Fuzzy Implication Generator via Fuzzy Negations which was generated via conical sections, in combination with the well-known Fuzzy Conjunction. The new Fuzzy Implication Generator takes its final forms after being configured by the fuzzy strong negations and combined with the most well-known fuzzy conjunctions T M , T P , T L K , T D , and T n M . The final implications that emerge, given that they are configured with the appropriate code, select the best value of the parameter and the best combination of the fuzzy conjunctions. This choice is made after comparing them with the Empiristic implication, which was created with the help of real temperature and humidity data from the Hellenic Meteorological Service. The use of the Empiristic implication is based on real data, and it also reduces the volume of the data without canceling them. Finally, the MATLAB code, which was used in the programming part of the paper, uses the new Fuzzy Implication Generator and approaches the Empiristic implication satisfactorily which is our final goal. [ABSTRACT FROM AUTHOR]

In this paper we introduced a new class of strong negations, which were generated via conical sections. This paper focuses on the fact that simple mathematical and computational processes generate new strong fuzzy negations, through purely geometrical concepts such as the ellipse and the hyperbola. Well-known negations like the classical negation, Sugeno negation, etc., were produced via the suggested conical sections. The strong negations were a structural element in the production of fuzzy implications. Thus, we have a machine for producing fuzzy implications, which can be useful in many areas, as in artificial intelligence, neural networks, etc. Strong Fuzzy Negations refers to the discrepancy between the degree of difficulty of the effort and the significance of its results. Innovative results may, therefore, derive for use in literature in the specific field of mathematics. These data are, moreover, generated in an effortless, concise, as well as self-evident manner. [ABSTRACT FROM AUTHOR]

Following the successful production of the radioimmunotheranostic nuclide 67Cu through the reaction of a 70Zn beam on a hydrogen gas target, our interest turned to the 99Mo/99mTc generator system. We produced 99Mo in inverse kinematics, with a100Mo beam at 12.0 MeV/nucleon impinging on a 4He gas target. The gas target was kept at liquid nitrogen temperature and pressures of 0.134(3), 0.280(6) and 1.33(3) atm. The produced isotopes and the beam were collected on a thick Al catcher foil. The 99Mo radioisotope can be produced in a one nucleon transfer occurring in peripheral reactions. Two competing factors determine the yield in such reactions, one is the cross-section which favors high mass targets, the other is the effective range which favors low-mass targets. Thus, at low 4He densities, higher beam energy at the exit of the gas target, most of the 99Mo production is generated in the Al foil, while at higher densities, most is generated in the gas target. The activity obtained at the end of the bombardment (given in Becquerel per particle nano-Amps per hour of irradiation), including the 4He gas and Al catcher contribution, were determined as 4.8(14), 2.5(7) and 3.8(11) kBq/(pnAh) for ρ gas = 0.83, 0.18 and 0.09 mg/cm3, respectively. These results are competitive to other accelerator production methods; however, the method reported has the additional advantages of needing significantly less amount of expensive enriched isotope and offers an easier and cleaner collection/separation method. • A novel approach to medical radioisotope production using inverse kinematics. • Radioisotope production using Cyclotron accelerators. • 99Mo/99mTc generator system. • 100Mo beam at 12.0 MeV/nucleon impinging on a 4He gas target. • Off-line γ-spectroscopy analysis and End of Bombardment Activities. [ABSTRACT FROM AUTHOR]