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The article proposes the use of geospatial information to support managerial decision-making by public authorities in the field of reintegration of temporarily occupied territories in Donetsk, Luhansk regions and Crimea. Variants of application of decision support systems in the management of immovable military property of the Armed Forces of Ukraine, the existing algorithms and methods of the decision support system in land relations are analyzed. Proposals are presented to support the adoption of managerial decisions by public authorities on the basis of Earth remote sensing data. The algorithm for the functioning of the decision support system in solving the problems of reintegration of temporarily occupied territories has been improved. The proposed sequence of actions of the method of the process of supporting the adoption of administrative decisions by public authorities in the field of reintegration of temporarily occupied territories using geospatial information, mathematical constructions of category theory and predicate logic. The order of implementation of the sequence of actions of this method is given. By improving and detailing the proposed method, it is possible to form a more effective system for supporting the adoption of managerial decisions by public authorities using geospatial information. У статті запропоновано використання геопросторової інформації щодо підтримки прийняття управлінських рішень органами державної влади у сфері реінтеграції тимчасово окупованих територій у Донецькій, Луганській областях та в Криму. Проаналізовані варіанти застосування систем підтримки прийняття рішень в управлінні нерухомим військовим майном Збройних Сил України, існуючі алгоритми та методи системи підтримки прийняття рішень в земельних відносинах. Надані пропозиції щодо підтримки прийняття управлінських рішень органами державної влади на основі даних дистанційного зондування Землі. Удосконалений алгоритм функціонування системи підтримки прийняття рішень при вирішенні задач реінтеграції тимчасово окупованих територій. Запропонована послідовність дій методу процесу підтримки прийняття управлінських рішення органами державної влади у сфері реінтеграції тимчасово окупованих територій з використанням геопросторової інформації, математичні конструкції теорії категорій і логіки предикатів. Наведений порядок реалізації послідовності дій даного методу. Удосконалюючи та деталізуючи запропонований метод, можливо сформувати більш ефективну систему підтримки прийняття управлінських рішень органами державної влади з використанням геопросторової інформації.

Contrary to received opinion, the Aristotelian Square of Opposition (square) is logically sound, differing from standard modern predicate logic (SMPL) only in that it restricts the universe U of cognitively constructible situations by banning null predicates, making it less unnatural than SMPL. U-restriction strengthens the logic without making it unsound. It also invites a cognitive approach to logic. Humans are endowed with a cognitive predicate logic (CPL), which checks the process of cognitive modelling (world construal) for consistency. The square is considered a first approximation to CPL, with a cognitive set-theoretic semantics. Not being cognitively real, the null set Ø is eliminated from the semantics of CPL. Still rudimentary in Aristotle’s On Interpretation (Int), the square was implicitly completed in his Prior Analytics (PrAn), thereby introducing U-restriction. Abelard’s reconstruction of the logic of Int is logically and historically correct; the loca (Leaking O-Corner Analysis) interpretation of the square, defended by some modern logicians, is logically faulty and historically untenable. Generally, U-restriction, not redefining the universal quantifier, as in Abelard and loca, is the correct path to a reconstruction of CPL. Valuation Space modelling is used to compute the effects of U-restriction.

The authors propose analogical reasoning systems based on first-order predicate logic using deep learning. The proposed systems consist of a model combining recursive neural networks and Word2Vec. When unknown data is input in this trained model, similar outputs to those of the trained data are obtained. Previous studies on symbolic inference using deep learning have focused on deductive and inductive inferences, and they require rule templates that are manually created and provided to the models in advance. However, it is not enough to infer unknown rules from a large amount of data on the Internet. Analogical reasoning is a mode of inference that attempts to solve a new case based on similarity. Thus, it enables inference of rules that were challenging to comprehend in previous studies. As a result of the experiments, unknown rules can be efficiently inferred from known rules contained in the knowledge bases. Using the proposed systems, the authors can extract unknown rules from five of the eight datasets consisting of three types of knowledge bases described in Prolog. The proposed method is the first case of analogical reasoning based on the first-order predicate logic using deep learning. Furthermore, the models used in the proposed systems have shown high robustness when using Word2Vec. For this reason, it is suggested that the practical applications of the proposed systems enable efficient inferences from a large amount of data that include noisy data on the Internet.

Information on rainfall prediction has great significance in agricultural fields where most of the crop’s irrigation depends on the rain water. Thus, rainfall prediction is a mandatory and an important process recently. Classical logic is further extended into fuzzy logic. Fuzzy logic has many advantages over classical logic. Two types of connotations of Fuzzy logic are: In narrow sense, Fuzzy Logic is a logical system, which is an extension of multivalued logic. The expert system of fuzzy consists of linguistics rules concerning the input variable of the fuzzy membership functions to the output variable of the fuzzy membership function. Fuzzy production rule IF-THEN statements relate the input variables of each other to define the result. Predicate logic operators such as AND operator is used to relate input and output variable. This paper proposed the prediction of rainfall using expert system model based fuzzy logic system for the demanding operational responsibilities needed by the meteorological department in the nation.