We introduce the notion of Boolean measure algebra. It can be described shortly using some standard notations and terminology. If B is any Boolean algebra, let BN denote the algebra of sequences , xn B. Let us write pk BN the sequence such that pk = 1 if i K and Pk = 0 if k < i. If x B, denote by x* BN the constant sequence x* = . We define a Boolean measure algebra to (...) be a Boolean algebra B with an operation μ:BN → B such that μ = 0 and μ = x. Any Boolean measure algebra can be used to model non-principal ultrafilters in a suitable sense. Also, we can build effectively the initial Boolean measure algebra. This construction is related to the closed open Ramsey Theorem 193–198.). (shrink)

Automatic control of physiological variables is one of the most active areas in biomedical engineering. This paper is centered in the prediction of the analgesic variables evolution in patients undergoing surgery. The proposal is based on the use of hybrid intelligent modelling methods. The study considers the Analgesia Nociception Index to assess the pain in the patient and remifentanil as intravenous analgesic. The model proposed is able to make a one-step-ahead prediction of the remifentanil dose corresponding to the (...) current state of the patient. The input information is the previous remifentanil dose, the ANI variable and the electromyogram signal. Modelling techniques used are Artificial Neural Networks and Support Vector machines for Regression combined with clustering methods. Both training and validation were done with a real dataset from different patients. Results obtained show the potential of this methodology to calculate the drug dose corresponding to a given analgesic state of the patient. (shrink)

We establish several first- or second-order properties of models of first-order theories by considering their elements as atoms of a new universe of set theory and by extending naturally any structure of Boolean model on the atoms to the whole universe. For example, complete f-rings are "boundedly algebraically compact" in the language $(+,-,\cdot,\wedge,\vee,\leq)$ , and the positive cone of a complete l-group with infinity adjoined is algebraically compact in the language (+, ∨, ≤). We also give an example (...) with any first-order language. The proofs can be translated into "naive set theory" in a uniform way. (shrink)

This paper investigates the difficulties of creating economic, social, and environmental values when operating as a hybrid venture. Drawing on hybrid organizing and sustainable business model research, it explores the implications of alternative forms of business model experimented with by farmer owned, fairtrade social enterprise Cafédirect. Responding to changes and challenges in the market and societal environment, Cafédirect has tried multiple business model innovations to deliver on all three forms of value capture, with differing levels (...) of success. This longitudinal case study, therefore, provides a contribution to our understanding of how business models enact hybrid mission, providing a platform for triple-bottom-line value capture. In doing so, we are able to expand on the normative understandings of integrating hybrid objectives, and the complications of multiple types of value capture. (shrink)

Semantical arguments, based on the completeness theorem for first-order logic, give elegant proofs of purely syntactical results. For instance, for proving a conservativity theorem between two theories, one shows instead that any model of one theory can be extended to a model of the other theory. This method of proof, because of its use of the completeness theorem, is a priori not valid constructively. We show here how to give similar arguments, valid constructively, by using Boolean models. (...) These models are a slight variation of ordinary first-order models, where truth values are now regular ideals of a given Boolean algebra. Two examples are presented: a simple conservativity result and Herbrand's theorem. (shrink)

Hypertext may represent a new paradigm capable of exploring legal sources within which links are established according to pertinent relationships found between statute texts and case law. However, to discover relevant information in such a network, a browsing mechanism is not enough when faced with a large volume of texts. This paper describes a new retrieval model where documents are represented according to both their content and relationships with other sources of information.

Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company, customers who purchase additional products or customers intending to resign from the cooperation. During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid (...) predictive model combining decision trees and cluster analysis. During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality. (shrink)

Boolean-valued models generalize classical two-valued models by allowing arbitrary complete Boolean algebras as value ranges. The goal of my dissertation is to study Boolean-valued models and explore their philosophical and mathematical applications.In Chapter 1, I build a robust theory of first-order Boolean-valued models that parallels the existing theory of two-valued models. I develop essential model-theoretic notions like “Boolean-valuation,” “diagram,” and “elementary diagram,” and prove a series of theorems on Boolean-valued models, including the (strengthened) (...) Soundness and Completeness Theorem, the Löwenheim–Skolem Theorems, the Elementary Chain Theorem, and many more.Chapter 2 gives an example of a philosophical application of Boolean-valued models. I apply Boolean-valued models to the language of mereology to model indeterminacy in the parthood relation. I argue that Boolean-valued semantics is the best degree-theoretic semantics for the language of mereology. In particular, it trumps the well-known alternative—fuzzy-valued semantics. I also show that, contrary to what many have argued, indeterminacy in parthood entails neither indeterminacy in existence nor indeterminacy in identity, though being compatible with both.Chapter 3 (joint work with Bokai Yao) gives an example of a mathematical application of Boolean-valued models. Scott and Solovay famously used Boolean-valued models on set theory to obtain relative consistency results. In Chapter 3, I investigate two ways of extending the Scott–Solovay construction to set theory with urelements. I argue that the standard way of extending the construction faces a serious problem, and offer a new way that is free from the problem.Abstract prepared by Xinhe Wu.E-mail: [email protected]. (shrink)

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression, artificial neural network, and M5 tree model. (...) In the second scenario, a comparable AI model hybridized with genetic algorithm as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient and lowest value root mean square error. However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model. (shrink)

Boolean-valued models for first-order languages generalize two-valued models, in that the value range is allowed to be any complete Boolean algebra instead of just the Boolean algebra 2. Boolean-valued models are interesting in multiple aspects: philosophical, logical, and mathematical. The primary goal of this paper is to extend a number of critical model-theoretic notions and to generalize a number of important model-theoretic results based on these notions to Boolean-valued models. For instance, we will (...) investigate (first-order) Boolean valuations, which are natural generalizations of (first-order) theories, and prove that Boolean-valued models are sound and complete with respect to Boolean valuations. With the help of Boolean valuations, we will also discuss the Löwenheim-Skolem theorems on Boolean-valued models. (shrink)

Multi-level discrete models of genetic networks, or the more general piecewise affine differential models, provide qualitative information on the dynamics of the system, based on a small number of parameters (such as synthesis and degradation rates). Boolean models also provide qualitative information, but are based simply on the structure of interconnections. To explore the relationship between the two formalisms, a piecewise affine differential model and a Boolean model are compared, for the carbon starvation response network in (...) E. coli . The asymptotic dynamics of both models are shown to be quite similar. This study suggests new tools for analysis and reduction of biological networks. (shrink)

We show that it is consistent with ZFC (relative to large cardinals) that every infinite Boolean algebra B has an irredundant subset A such that 2 |A| = 2 |B| . This implies in particular that B has 2 |B| subalgebras. We also discuss some more general problems about subalgebras and free subsets of an algebra. The result on the number of subalgebras in a Boolean algebra solves a question of Monk from [6]. The paper is intended to (...) be accessible as far as possible to a general audience, in particular we have confined the more technical material to a "black box" at the end. The proof involves a variation on Foreman and Woodin's model in which GCH fails everywhere. (shrink)

I examine the warrants we have in light of the empirical successes of a kind of model I call ‘ hybrid models ’, a kind that includes climate models among its members. I argue that these warrants ’ strengths depend on inferential virtues that are not just explanatory virtues, contrary to what would be the case if inference to the best explanation provided the warrants. I also argue that the warrants in question, unlike those IBE provides, guide inferences (...) only to model implications about which there is real uncertainty. My conclusion provides criteria of adequacy for epistemologies of climate and other hybrid models. (shrink)

Research on hybrid bionic systems (HBSs) is still in its infancy but promising results have already been achieved in laboratories. Experiments on humans and animals show that artificial devices can be controlled by neural signals. These results suggest that HBS technologies can be employed to restore sensorimotor functionalities in disabled and elderly people. At the same time, HBS research raises ethical concerns related to possible exogenous and endogenous limitations to human autonomy and freedom. The analysis of these concerns requires (...) reflecting on the availability of scientific models accounting for key aspects of sensorimotor coordination and plastic adaptation mechanisms in the brain. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm (...) and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

We consider the model checking problem for Hybrid Logic. Known algorithms so far are global in the sense that they compute, inductively, in every step the set of all worlds of a Kripke structure that satisfy a subformula of the input. Hence, they always exploit the entire structure. Local model checking tries to avoid this by only traversing necessary parts of the input in order to establish or refute the satisfaction relation between a given world and a (...) formula. We present a framework for local model checking of Hybrid Logic based on games. We show that these games are simple reachability games for ordinary Hybrid Logic and weak Büchi games for Hybrid Logic with operators interpreted over the transitive closure of the accessibility relation of the underlying Kripke frame, and show how to solve these games thus solving the local model checking problem. Since the first-order part of Hybrid Logic is inherently hard to localise in model checking, we give examples, in the end, of how global model checkers can be optimised in certain special cases using well-established techniques like fixpoint approximations and divide-and-conquer algorithms. (shrink)

A hybrid model of cell dynamics is presented. It is illustrated by model examples and applied to study erythropoiesis (red blood cell production). In this approach, cells are considered as discrete objects while intra-cellular proteins and extra-cellular biochemical substances are described with continuous models. Spatial organization of erythropoiesis occurring in specific structures of the bone marrow, called erythroblastic island, is investigated.

With the increasing degrees A.I.s and machines are applied across different social contexts, the need for implementing ethics in A.I.s is pressing. In this paper, we argue for a pluralist hybrid model for the implementation of moral A.I.s. We first survey current approaches to moral A.I.s and their inherent limitations. Then we propose the pluralist hybrid approach and show how these limitations of moral A.I.s can be partly alleviated by the pluralist hybrid approach. The core ethical (...) decision-making capacity of an A.I. based on the pluralist hybrid approach consists of two systems. The first is a deterministic algorithm system that incorporates different moral rules for making explicit moral decisions. The second is a machine learning system that calculates the value of the variables required by the application of moral principles. The pluralist hybrid system is better than the existing proposals as (i) it better addresses the moral disagreement problem of the top-down approach by including different moral principles, while (ii) it reduces the opacity of ethical decision-making compared with a bottom-up system by implementing explicit moral principles for moral decision making. (shrink)

This third edition, now available in paperback, is a follow up to the author's classic Boolean-Valued Models and Independence Proofs in Set Theory. It provides an exposition of some of the most important results in set theory obtained in the 20th century: the independence of the continuum hypothesis and the axiom of choice.

The susceptible-transmissible-removed (STR) model is a deterministic compartment model, based on the susceptible-infected-removed (SIR) prototype. The STR replaces 2 SIR assumptions. SIR assumes that the emigration rate (due to death or recovery) is directly proportional to the infected compartment’s size. The STR replaces this assumption with the biologically appropriate assumption that the emigration rate is the same as the immigration rate one infected period ago. This results in a unique delay differential equation epidemic model with the delay (...) equal to the infected period. Hamer’s mass action law for epidemiology is modified to resemble its chemistry precursor—the law of mass action. Constructing the model for an isolated population that exists on a surface bounded by the extent of the population’s movements permits compartment density to replace compartment size. The STR reduces to a SIR model in a timescale that negates the delay—the transmissible timescale. This establishes that the SIR model applies to an isolated population in the disease’s transmissible timescale. Cyclical social interactions will define a rhythmic timescale. It is demonstrated that the geometric mean maps transmissible timescale properties to their rhythmic timescale equivalents. This mapping defines the hybrid incidence (HI). The model validation demonstrates that the HI-STR can be constructed directly from the disease’s transmission dynamics. The basic reproduction number (R0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_0$$\end{document}) is an epidemic impact property. The HI-STR model predicts that R0∝ρnB\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_0 \propto \root \mathfrak{B} \of {\rho_n}$$\end{document} where ρn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho_n$$\end{document} is the population density, and B\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathfrak{B}}$$\end{document} is the ratio of time increments in the transmissible- and rhythmic timescales. The model is validated by experimentally verifying the relationship. R0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_0$$\end{document}’s dependence on ρn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho_n$$\end{document} is demonstrated for droplet-spread SARS in Asian cities, aerosol-spread measles in Europe and non-airborne Ebola in Africa. (shrink)

Societies are composed of groups that interact. Symbiotic groups are those in which agents complement each other in resources that they have in excess. Symbiotic groups are useful especially when the resources in an environment are distributed unevenly, because they enable agents to trade resources easily. However, for trading to happen successfully, agents in symbiotic groups need to cooperate, i.e., they should be willing to donate resources when appropriate. Similarly, if some agents in a symbiotic group are defectors, they should (...) be identified by others and eliminated from the group for the well-being of the remaining agents. Accordingly, we first study Edmonds’ tag-based model of symbiotic groups to understand the lifespan of symbiotic groups (e.g., why some groups live shorter than others). Then, we enhance Edmonds’ model by adding the capability of reciprocal interactions to agents, thus achieving a hybrid model. We capture reciprocity in three different models and study their effects on the elimination of defectors in symbiotic groups. Our experimental results show that the groups that are built with the proposed hybrid model can eliminate more defectors and earlier than tag-based models. Further, the hybrid approach can generate symbiotic groups more effectively and efficiently. (shrink)

User intent classification is a vital component of a question-answering system or a task-based dialogue system. In order to understand the goals of users’ questions or discourses, the system categorizes user text into a set of pre-defined user intent categories. User questions or discourses are usually short in length and lack sufficient context; thus, it is difficult to extract deep semantic information from these types of text and the accuracy of user intent classification may be affected. To better identify user (...) intents, this paper proposes a BERT-Cap hybrid neural network model with focal loss for user intent classification to capture user intents in dialogue. The model uses multiple transformer encoder blocks to encode user utterances and initializes encoder parameters with a pre-trained BERT. Then, it extracts essential features using a capsule network with dynamic routing after utterances encoding. Experiment results on four publicly available datasets show that our model BERT-Cap achieves a F1 score of 0.967 and an accuracy of 0.967, outperforming a number of baseline methods, indicating its effectiveness in user intent classification. (shrink)

This article examines the use of organic and mechanistic metaphors that have underpinned the modeling of national governance in the social sciences and also framed the representation of the social impact of migration. It argues that the global patterns of migration and the contemporary forms of hybrid subjectivity do not fit well with these conceptual frameworks. The limits of this framework are examined through Harald Kleinschmidt's theory of residentialism, and the outlines of an alternative conceptual frame is proposed by (...) drawing on key terms that have been developed in complexity theory. (shrink)

Dynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system.

Sentence boundary detection (SBD) represents an important first step in natural language processing since accurately identifying sentence boundaries significantly impacts downstream applications. Nevertheless, detecting sentence boundaries within legal texts poses a unique and challenging problem due to their distinct structural and linguistic features. Our approach utilizes deep learning models to leverage delimiter and surrounding context information as input, enabling precise detection of sentence boundaries in English legal texts. We evaluate various deep learning models, including domain-specific transformer models like LegalBERT and (...) CaseLawBERT. To assess the efficacy of our deep learning models, we compare them with a state-of-the-art domain-specific statistical conditional random field (CRF) model. After considering model size, F1-score, and inference time, we identify the Convolutional Neural Network Model (CNN) as the top-performing deep learning model. To further enhance performance, we integrate the features of the CNN model into the subsequent CRF model, creating a hybrid architecture that combines the strengths of both models. Our experiments demonstrate that the hybrid model outperforms the baseline model, achieving a 4% improvement in the F1-score. Additional experiments showcase the superiority of the hybrid model over SBD open-source libraries when confronted with an out-of-domain test set. These findings underscore the importance of efficient SBD in legal texts and emphasize the advantages of employing deep learning models and hybrid architectures to achieve optimal performance. (shrink)

As they are conventionally formulated, Boolean games assume that players make their choices in ignorance of the choices being made by other players – they are games of simultaneous moves. For many settings, this is clearly unrealistic. In this paper, we show how Boolean games can be enriched by dependency graphs which explicitly represent the informational dependencies between variables in a game. More precisely, dependency graphs play two roles. First, when we say that variable x depends on variable (...) y, then we mean that when a strategy assigns a value to variable x, it can be informed by the value that has been assigned to y. Second, and as a consequence of the first property, they capture a richer and more plausible model of concurrency than the simultaneous-action model implicit in conventional Boolean games. Dependency graphs implicitly define a partial ordering of the run-time events in a game: if x is dependent on y, then the assignment of a value to y must precede the assignment of a value to x; if x and y are independent, however, then we can say nothing about the ordering of assignments to these variables—the assignments may occur concurrently. We refer to Boolean games with dependency graphs as partial-order Boolean games. After motivating and presenting the partial-order Boolean games model, we explore its properties. We show that while some problems associated with our new games have the same complexity as in conventional Boolean games, for others the complexity blows up dramatically. We also show that the concurrency in partial-order Boolean games can be modelled using a closure-operator semantics, and conclude by considering the relationship of our model to Independence-Friendly logic. (shrink)

The luminous value is high for many natural scenes, which causes loss of information and occurs in dark images. The High Dynamic Range (HDR) technique captures the same objects or scene for multiple times in different exposure and produces the images with proper illumination. This technique is used in the various applications such as medical imaging and observing the skylight, etc. HDR imaging techniques usually have the issue of lower efficiency due to capturing of multiple photos. In this paper, an (...) efficient method is proposed for HDR imaging technique to achieve better performance and lower noise. The Luminance-Chrominance-Gradient High Dynamic Range (LCGHDR) method is proposed to obtain the proper luminous value of images. The same scenario is captured at different exposure are processed by the proposed method. Based on these feature values extracted from the different images and exposure fusion technique was developed that helps for the proper imaging. This experiment was evaluated and analyzed by comparing with the other methods, which showed the efficiency of the proposed method. This method needs only 124.594 seconds for the computation, while existing method need 139.869 seconds for the same number of images. (shrink)