Logical Features Research Articles

Drivers and Limits of the Geoeconomic Turn in EU Infrastructure Policy

In recent years, the EU has increasingly applied state-interventionist practices to initiate and implement infrastructure policy projects. This stands in stark contrast to a phase of liberalization of infrastructure networks and services accompanying European integration and fiscal consolidation and infrastructure decay during the euro crisis. This article argues that the new state interventionism is strongly driven by the changing global constellation of a “new triad competition” where the EU is increasingly competing over infrastructures with the US and China. As a consequence, EU infrastructure policy undergoes a geoeconomic turn that aims to control transnational value chains and related political-economic spaces. Drawing on concepts of critical geography and international political economy, the article outlines the core features of this geoeconomic design logic of infrastructures and contrasts it with complementary or competing ones. The article substantiates these arguments by analyzing EU decision-making on two cases of high-tech infrastructure in the fields of communication and energy: the federated data infrastructure Gaia-X and the Hydrogen Strategy. Both cases provide evidence for the geoeconomic turn in EU infrastructure policy. Yet, the analysis also highlights that the turn is at times supported but also hampered by a capitalist logic that is reflected in the positioning of European and non-European businesses, as well as the EU’s reliance on private action. Furthermore, it illustrates that an ecological and a social-integrative design logic to key infrastructures are largely subordinated. The conclusions reflect on the discrepancy between the EU’s geoeconomic agenda and its less far-reaching implementation.

교육학 연구방법으로서 머신러닝 기법 적용에 대한 고찰

The aim of the study is to discuss the technical and ethical considerations regarding the current applications of the emerging data science techniques in the field of education. Based on the literature review, this study provides an overview of machine learning approaches that have been used to answer to educational research agenda in South Korea. By comparing the logical features of these computational techniques to the conventional statistical methodologies, the authors highlight the unique challenges and opportunities associated with educational data science and discuss what that means to developing scientific knowledge in the field of education research. The authors argue the importance of the awareness of the differences in data conditions and causal inferences between computational approaches and conventional statistical modelings. Computational techniques using big data are not a magical tool to discover knowledge. This study explains why researchers’ domain knowledge and rigorous data preparation have bigger impacts on drawing reliable and meaningful information for educational intervention. This study encourages critical inquiry into the implications of data science for education. By revisiting the goals of public education, the authors call for the future research to host open discussion of the potential of educational data sciences.

교육학 연구방법으로서 머신러닝 기법 적용에 대한 고찰

The aim of the study is to discuss the technical and ethical considerations regarding the current applications of the emerging data science techniques in the field of education. Based on the literature review, this study provides an overview of machine learning approaches that have been used to answer to educational research agenda in South Korea. By comparing the logical features of these computational techniques to the conventional statistical methodologies, the authors highlight the unique challenges and opportunities associated with educational data science and discuss what that means to developing scientific knowledge in the field of education research. The authors argue the importance of the awareness of the differences in data conditions and causal inferences between computational approaches and conventional statistical modelings. Computational techniques using big data are not a magical tool to discover knowledge. This study explains why researchers’ domain knowledge and rigorous data preparation have bigger impacts on drawing reliable and meaningful information for educational intervention. This study encourages critical inquiry into the implications of data science for education. By revisiting the goals of public education, the authors call for the future research to host open discussion of the potential of educational data sciences.

On the modeling of live possibilities

In this paper, I evaluate two ways to model the notion of live possibilities: the supervaluation-based approach, and the alternative-based approach. I argue that the alternative-based approach is more promising in fulfilling certain desirable constraints governing live possibilities. However, the existing alternative-based accounts fail to be fully satisfactory. To address this inadequacy, I devise a new alternative-based framework and explore its logical features.

Logical Form, Conditionals, Pseudo-Conditionals

This paper raises some questions about the formalization of sentences containing ‘if’ or similar expressions. In particular, it focuses on three kinds of sentences that resemble conditionals in some respects but exhibit distinctive logical features that deserve separate consideration: whether-or-not sentences, biscuit conditionals, and concessive conditionals. As will be suggested, the examples discussed show in different ways that an adequate formalization of a sentence must take into account the content expressed by the sentence. This upshot is arguably what one should expect on the view that logical form is determined by truth conditions.

SimRE: Simple contrastive learning with soft logical rule for knowledge graph embedding

Knowledge graphs serve as a pivotal framework for the structured representation of information regarding entities and relations. However, in the real world, these knowledge graphs are often incomplete and harboring missing facts. Knowledge graph completion (KGC) has emerged as a central research focus, entailing the automated prediction of these missing facts and garnering substantial scholarly attention in recent years. Text-based knowledge graph embedding methods have demonstrated considerable potential for tackling the challenges associated with KGC by employing pre-trained language models. However, their limitation lies in the lack of logical features, which constrains the efficacy of capturing intricate patterns within knowledge graphs. This paper proposed SimRE, a straightforward contrastive learning framework augmented with soft logic rules. SimRE introduces a self-supervised framework that leverages the input rule bodies to predict the corresponding rule heads through a contrastive objective. We introduced two rule sampling techniques to enhance the efficiency and accuracy of the model: in-batch rule negatives and pre-batch rule negatives. SimRE employs a simple method for integrating logical features with the text-based model. The experimental results on benchmark datasets demonstrate that the proposed approach outperforms state-of-the-art methods.

A Motion Logic Network for Pedestrian Motion Prediction

Accurate and fast motion prediction such as pedestrian motion prediction (PMP) is crucial for safe autonomous driving. Much research effort has been devoted to studying the reactive behaviors of pedestrians, such as the interaction between pedestrians or the interaction between pedestrians and the environment. However, compared with behavioral logic, the current motion state of pedestrians has a greater influence on the future trajectory. In this work, we propose a motion logic network (MLN) to improve both the accuracy and efficiency of pedestrian motion prediction. Compared with the traditional data-driven neural networks, the concept of motion logic is directly introduced into the network so that the training of the network is not required. In particular, instead of fitting the network based on inputs and target outputs, the proposed method directly adopts motion logic to predict the future trajectory. To illustrate the performance of the proposed MLN, several experiments have been performed. For acceleration and deceleration motion in practical experiment, the average displacement error (ADE) of MLN has an improvement of as high as 6.25% than CVM’s, while the final displacement error (FDE) of MLN has an improvement of 11.1%. In terms of efficiency, MLN is a hundred times faster than LSTM. It indicates that motion logic plays an important role in the development of prediction algorithms for pedestrian motion. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the effects of pedestrians’ different motion states on the future trajectory. For example, the pedestrians’ state may also change drastically in some situations such as dashing across the road or stopping in anticipation of a bicycle crossing the path. This work explores the use of pedestrians’ physical motion states to develop a network that emphasizes the logical features of pedestrian motion so as to directly estimate the future motion trajectory and trend based on the motion logic. For the first time, the concept of motion logic and the resultant training-free motion logic network (MLN) is proposed, which takes into account the accuracy and efficiency performance of the algorithm. Compared with the state-of-the-art pedestrian prediction methods, the proposed method can better predict the trajectory of pedestrians in more complex motion states. Moreover, a series of simulations and practical experiments with pseudo-constant velocity motion and acceleration/deceleration motion was taken to verify the performance of the proposed MLN.

Milliken’s Tree Theorem and Its Applications: A Computability-Theoretic Perspective

Milliken’s tree theorem is a deep result in combinatorics that generalizes a vast number of other results in the subject, most notably Ramsey’s theorem and its many variants and consequences. In this sense, Milliken’s tree theorem is paradigmatic of structural Ramsey theory, which seeks to identify the common combinatorial and logical features of partition results in general. Its investigation in this area has consequently been extensive. Motivated by a question of Dobrinen, we initiate the study of Milliken’s tree theorem from the point of view of computability theory. The goal is to understand how close it is to being algorithmically solvable, and how computationally complex are the constructions needed to prove it. This kind of examination enjoys a long and rich history, and continues to be a highly active endeavor. Applied to combinatorial principles, particularly Ramsey’s theorem, it constitutes one of the most fruitful research programs in computability theory as a whole. The challenge to studying Milliken’s tree theorem using this framework is its unusually intricate proof, and more specifically, the proof of the Halpern-Laüchli theorem, which is a key ingredient. Our advance here stems from a careful analysis of the Halpern-Laüchli theorem which shows that it can be carried out effectively (i.e., that it is computably true). We use this as the basis of a new inductive proof of Milliken’s tree theorem that permits us to gauge its effectivity in turn. The key combinatorial tool we develop for the inductive step is a fast-growing computable function that can be used to obtain a finitary, or localized, version of Milliken’s tree theorem. This enables us to build solutions to the full Milliken’s tree theorem using effective forcing. The principal result of this is a full classification of the computable content of Milliken’s tree theorem in terms of the jump hierarchy, stratified by the size of instance. As usual, this also translates into the parlance of reverse mathematics, yielding a complete understanding of the fragment of second-order arithmetic required to prove Milliken’s tree theorem. We apply our analysis also to several well-known applications of Milliken’s tree theorem, namely Devlin’s theorem, a partition theorem for Rado graphs, and a generalized version of the so-called tree theorem of Chubb, Hirst, and McNicholl. These are all certain kinds of extensions of Ramsey’s theorem for different structures, namely the rational numbers, the Rado graph, and perfect binary trees, respectively. We obtain a number of new results about how these principles relate to Milliken’s tree theorem and to each other, in terms of both their computability-theoretic and combinatorial aspects. In particular, we establish new structural Ramsey-theoretic properties of the Rado graph theorem and the generalized Chubb-Hirst-McNicholl tree theorem using Zucker’s notion of big Ramsey structure.

Analysis of structural-tectonic and petrophysical features of productive horizons of Mishovdag fold

&#x0D; &#x0D; &#x0D; The article analyzes the geological structure, lithofascial, tectonic, and morpho- logical features of the Mishovdag oil field, which is a productive area in terms of oil content. Morphologically, it represents a brachianticlinal fold located in the north-west and south-east directions. The Mishovdag fold is located on the Kalamaddin-Bandovan anticline zone between the Kichik Harami (in the northeast) and Galmaz (in the northwest) structures. The Mishovdag-Galmaz part of the anticlinal zone extends in the sub-southern direction. The structures included in the zone are represented on the surface mainly by Aghjagil, Absheron, and ancient Khazar sediments. Productive layer sediments come to the surface in few areas. The tectonic movements and activity of mud volcanoes (active or buried) have left characteristic traces in the geological structure of the research area. Brachianticlinal and other types of structures were formed here. Under the strong influence of tectonic movements, the layers forming them were deformed and broken into blocks by regional tectonic faults. The anticlinal structures of the zone are complicated by longitudinal and latitudinal faults. The Mishovdag fold is represented by a brachianticline, 20-25 km long and 4-6 km wide. Brachianticline has a sharply asymmet- rical structure. The southwestern limb is steep, and inclined at an angle of 25-450, while the northeastern limb is flat and characterized by an inclination angle of 7-250. As a result of the analysis of the cross-sections of deep wells drilled in the field, it was determined that Mesozoic, Paleogene-Miocene, and Pliocene-Anthropogenic sediments are developed in the section of the fold. Sediments repre- senting the cross-section are characterized by high reservoir properties. Geological-geophysical works are carried out in the field. Core materials were taken from wells. The lithological-petrophysical characteristics – carbonate, porosity, and permeability of reservoir rocks have been determined according to core samples taken from exploration wells drilled in the field. The lithological-petrophysical characteristic regularities of change were investigated depending on depth.&#x0D; &#x0D; &#x0D;

Design of suspension control system for bearingless induction motor using fuzzy-I controller

In the bearingless induction motor (BIM), the rotor of BIM is supported by the magnetic force, the radial position control technique is studied in this paper, an approximation and simplification are used to create a linearized model of suspension system. The objective of this paper is to design a control system that overcomes the limitations of the traditional controller and to improve the control performance and achieve high stability, even if it is exposed to external disturbance and internal disturbance (changes in load or speed). In order to solve this problem, the proposed controller is based on the fuzzy-I controller that combines the positive features of both fuzzy logic and the integration. In comparison with the conventional PID, the results show that the fuzzy-I controller reduces the peak-to-peak rotor deviation by 23% under effect of external disturbance force and 31% under effect of speed variation and 9% under effect of load variation.

MVHGN: Multi-View Adaptive Hierarchical Spatial Graph Convolution Network Based Trajectory Prediction for Heterogeneous Traffic-Agents

The future trajectory prediction of heterogeneous traffic-agents for autonomous vehicles in mixed traffic scene is of great significance for safe and reliable driving. Thus, we propose the Multi-View Adaptive Hierarchical Spatial Graph Convolution Network (MVHGN) to predict the future trajectories of heterogeneous traffic-agents. Firstly, multiple logical correlations are obtained based on the time series data of traffic-agents and a multi-view logical network is constructed. The multi-view logical feature extraction is realized based on the graph convolution module. Then, combining the multi-view logical features and the adaptive spatial topology network, the logical-physical features at the micro level are mined through the graph convolution module; based on the logical-physical features at the micro level and the regional clustering network at the macro level, the global logical-physical features are obtained. Finally, the model predicts the future trajectories of traffic-agents based on the encoder-decoder structure of the GRU. For the Apolloscape trajectory data set, the performance of our proposed method MVHGN is better than that of the comparison models.

Carcinoma mucinoso de mama: una estirpe histológica rara de pronóstico favorable

Mucinous carcinoma is a rare type of breast cancer, which represents less than 4% of all primary cancers. It usually occurs in postmenopausal patients, around the seventh decade of life. Clinically, it is characterized by the presence of a palpable nodule, rarely accompanied by other symptoms. Imaging tools, such as mammogram and ultrasound, are essential for its diagnosis, however, in some situations the diagnosis can be underestimated due to the similar charac¬teristics that it shares with other benign lesions. Definitive diagnosis is made by histopathology. Regarding treatment, there is no consensus on the most appropriate, due to the low incidence of these tumors. Many authors agree that surgical intervention continues to be the best option, showing a positive impact on survival and low recurrences. This can be accompanied later by adjuvant en¬docrine therapies. Fortunately, the prognosis of this type of tumor is usually favorable, even survival exceeds 90% at 5 years. Keywords: Breast cancer; mucinous carcinoma; clinicopatho¬logical features; histological subtypes.

Impact of Derived Features from the Controlled Environment Agriculture Scenarios on Energy Consumption Prediction Model

The high energy consumption CEA building brings challenges to the management of the energy system. An accurate energy consumption prediction model is necessary. Although there are various prediction methods, the prediction method for the particularity of CEA buildings is still a gap. This study proposes some derived features based on the CEA scenarios to improve the accuracy of the model. The study mainly extracts the time series and logical features from the agricultural calendar, the botanical physiological state, building characteristics, and production management. The time series and logical features have the highest increase of 2.8% and 3.6%, respectively. In addition, four automatic feature construction methods are also used to achieve varying degrees of influence from −9% to 8%. Therefore, the multiple feature extraction and feature construction methods proposed in this paper can effectively improve the model performance.

Dialectica principles via Gödel doctrines

Gödel's Dialectica interpretation was conceived as a tool to obtain the consistency of Heyting arithmetic in the 40s. In recent years, several proof theoretic transformations, based on Gödel's Dialectica interpretation, have been used systematically to extract new content from classical proofs, following a suggestion of Kreisel. Thus, the interpretation has found new relevant applications in several areas of mathematics and computer science. Several authors have explained the Dialectica interpretation in categorical terms. In particular, de Paiva introduced the notion of a Dialectica category as an internal version of Gödel's Dialectica Interpretation in her doctoral work. This was generalised by Hyland and Hofstra, who considered the interpretation in terms of fibrations. In our previous work, we introduced an intrinsic presentation of the Dialectica construction via a generalisation of Hofstra's work, using the notion of Gödel fibration and its proof-irrelevant version, a Gödel doctrine. The key idea is that Gödel fibrations (and doctrines) can be thought of as fibrations generated by some basic predicates playing the role of quantifier-free predicates. This categorification of quantifier-free predicates is crucial not only to show that our notion of Gödel fibration is equivalent to Hofstra's Dialectica fibration in the appropriate way, but also to show how Gödel doctrines embody the main logical features of the Dialectica Interpretation. To show that, we derive the soundness of the interpretation of the implication connective, as expounded by Troelstra, in the categorical model. This requires extra logical principles, going beyond intuitionistic logic, namely (suitable versions of) the Markov Principle (MP) and the Independence of Premise (IP) principle, as well as some choice. We show how these principles are satisfied in the categorical setting, establishing a tight correspondence between the logical system and the categorical framework. This tight correspondence should come in handy not only when discussing the traditional applications of Dialectica, but also when dealing with some newer uses of the interpretation, as in modelling games or concurrency theory. Finally, to complete our analysis, we characterise categories obtained as a result of the Hyland, Johnstone and Pitts tripos-to-topos construction when applied to Gödel doctrines.

Public diplomacy: terminological, ontological, and applied research results

Abstract. The article examines the theoretical and practical aspects of public diplomacy. Over time, the term ‘public diplomacy’ has undergone a semantic transformation, shifting from a mere designation of diplomatic style to encompassing the way of conducting relevant activities. It has also experienced a cognitive transformation, transitioning from life-experiential cognition to a scientific one. Within the problematic field of research, public diplomacy can be defined by the concept’s fundamental social and historical circumstances, content, or individual features. The empirical findings only present a terminological identification and propose it as a scientific hypothesis, lacking a complete and universally recognised scientific system. The multifaceted nature of the phenomenon and ambiguous interpretation of its possible application in practice require further research to identify the concept’s content and specify its institutional elements. The study aims to define the complex concept of public diplomacy by analysing its historical and logical features, encompassing the transformation of the term, and determining the fundamental socio-historical circumstances that led to its emergence. It also seeks to establish a cause-and-effect relationship between these circumstances and public diplomacy, explore its essential features, assess the cause-and-effect link between public diplomacy and its specific forms identified in scientific literature, and evaluate their alignment with the concept’s scope. Additionally, the study aims to determine the institutional elements of public diplomacy and estimate its significance as a resource of the state’s foreign policy power in Ukraine. The study employs general scientific methodological tools, including comparative analysis, categorical analysis, complex analysis, hypothetical and deductive methods, cause-and-effect analysis, generalisation, and systematisation. Keywords: state, soft foreign policy power, diplomacy, traditional diplomacy, public diplomacy.

ВЫЯВЛЕНИЕ КРИТИЧЕСКИХ СТАДИЙ РАННЕГО ОНТОГЕНЕЗА КАК МЕТОДОЛОГИЧЕСКИЙ ПОДХОД В ИЗУЧЕНИИ БИОЛОГИИ РАЗВИТИЯ РАСТЕНИЙ В БИОТЕХНОЛОГИЧЕСКИХ ЦЕЛЯХ

The earliest stage of ontogenesis of flowering plants propagating by amphimixis is zygotic embryogenesis. It is well known that zygotic embryogenesis is a single integrated process. At the same time, from the standpoint of the methodology of research on flowering plant embryogenesis it is proposed to distinguish different, although interrelated, stages of this process. Such stages reflect the pulsating nature of the development of the embryo as a dynamic system. The stages differ in duration, morphophysiological parameters, structural and functional characteristics and in general – in importance for the further development of the embryo. A number of authors regard some stages of flowering plant zygotic embryogenesis as critical, while offering various criteria for distinguishing such stages. This brief review examines both the critical stages of plant zygotic embryogenesis proposed in the literature and the criteria for their distinguishing. Issues concerning the cytophysio¬logical features of zygotic plant embryos at the critical stages of early ontogenesis are analyzed. It is suggested that these data can contribute to the further development of a number of biotechnologies based on the use of zygotic plant embryos (embryo culture in vitro, callus culture in vitro).

ANFIS based data rate prediction for cognitive radio

Intelligence is needed to keep up with the rapid evolution of wireless communications, especially in terms of managing and allocating the scarce, radio spectrum in the highly varying and disparate modern environments. Cognitive radio (CR) systems promise to handle this situation by utilizing intelligent software packages that enrich their transceiver with radio-awareness, adaptability and capability to learn. Its system participates in a continuous process, “the cognition cycle”, during which it adjusts its operating parameters, observes the results and, eventually takes actions, that is to say, decides to operate in a specific radio configuration (i.e., radio access technology, carrier frequency, modulation type, etc.) expecting to move the radio toward some optimized operational state. In such a process, learning mechanisms utilize information from measurements sensed from the environment, gathered experience and stored knowledge and guide in decision making. This paper evaluates learning schemes that are based on adaptive neuro-fuzzy inference system (ANFIS) for predicting the capabilities (e.g. data rate) that can be achieved by a specific radio configuration in cognitive radio. While CR is an intelligent emergent technology, where learning schemes are needed to assist in its functioning. On the other side, ANFIS based scheme is one of the good learning artificial intelligence method, that combines best features of neural network and fuzzy logic. Here proposed method is able to assist a cognitive radio system to help in selecting the best one radio configuration to operate in. Performance metric like root mean square error (RMSE), prediction accuracy of ANFIS learning has been used as performance index.

The Extended MOORA Method Based on Fermatean Fuzzy Information

The intelligent manufacturing system (IMS) is a framework that improves productivity by organizing the logical features involved in manufacturing. The procedure of intelligent manufacturing owns the capability to self-control the manufacturing of the products according to the specifications of design. Different IMSs are designed to deal with continuous changes in market which can adjust to make the modified environment easier. The central idea of this research article is to select an IMS that can adapt the updated situations faster than the existing competing systems and provide higher benefits in utilizing new possibilities. To select such IMS, the applicability of multiobjective optimization on the basis of the ratio analysis (MOORA) method has been explored using Fermatean fuzzy sets. The Fermatean fuzzy aggregated weighted operators are used to construct the decision matrices. Then, the ratio analysis-based MOORA method is developed to accomplish the ranking of under consideration IMSs. Furthermore, the conversion of qualitative attributes into quantitative attributes has been performed using Fermatean fuzzy numbers (FFNs). Finally, a brief comparative analysis of the developed technique with existing models is narrated to reveal the flexibility of the Fermatean fuzzy MOORA method.

Indexed and fibered structures for partial and total correctness assertions

AbstractHoare Logic has a long tradition in formal verification and has been continuously developed and used to verify a broad class of programs, including sequential, object-oriented, and concurrent programs. Here we focus on partial and total correctness assertions within the framework of Hoare logic and show that a comprehensive categorical analysis of its axiomatic semantics needs the languages of indexed and fibered category theory. We consider Hoare formulas with local, finite contexts, of program and logical variables. The structural features of Hoare assertions are presented in an indexed setting, while the logical features of deduction are modeled in the fibered one.

Sensory evaluation of microwave-ultrasound treated bottle gourd juice using hybrid features of fuzzy logic and proportional odd modelling approach.

In the present investigation, sensory evaluation of bottle gourd juice (BGJ) samples, obtained from microwave-ultrasound based combined treatment was performed. The raw (sample-1) and conventionally treated (sample-2) alongside microwave-ultrasound treated (sample-3) were considered for the assessment of sensory evaluation. An innovative approach of hybrid fuzzy logic and proportional odd modelling (FL-POM) was implemented for the analysis of the sensory scores. The similarity values for the juice samples and their quality attributes were resolved from the results obtained by fuzzy logic. These values were considered as input for hybridization with the POM approach. The assessed coefficients obtained from the results of POM were considered for the ranking of the samples and quality traits. The ranking of the BGJ samples was observed in the order of sample-1 > sample-3 > sample-2, and their related quality attributes ranked in the order color > taste > aroma > mouth feel. The microwave-ultrasound treated BGJ evinced as the best sample in comparison to the raw one.