The fundamental building block of the loop quantum gravity (LQG) is the spin network which is used to quantize the physical space-time in the LQG. Recently, the novel quantum spin is proposed using the basic concepts of the spin network. This perspective redefines the notion of the quantum spin and also introduces the novel definition of the reduced Planck constant. The implication of this perspective is not only limited to the quantum gravity; but also (...) found in the quantum mechanics. Using this perspective, we also propose the quantization of the mind-stuff. Similarity between the physical space-time and the space-time of the mind-stuff provides novel notions to study the space-time scientifically as well philosophically. The comparison between the physical- space-time and the space-time of the mind-stuff is also studied. (shrink)

Objective: To evaluate the potential of arterial spin labeling (ASL) as an alternative to FDG-PET in the diagnosis of disorders of consciousness (DOC), we conducted a comparative study of the two modalities. Methods: A total of 36 DOC patients (11 female; mean age = 49.67 ± 14.54 years) and 17 healthy control (HC) participants (9 female; mean age = 31.9 ± 9.6 years) underwent both FDG-PET scans that measure metabolism via glucose uptake and ASL scans that measure cerebral blood (...) flow (CBF). CBF and metabolism in DOC and HC were compared, globally and for seven functional networks. Comparability of the two modalities was estimated using Spearman partial correlation for both global and network levels. Furthermore, a support vector machine (SVM) algorithm was used to train two classifiers to distinguish DOC patients from HC with normalized CBF or metabolism values from the seven networks. Performance of these classifiers was first evaluated through leave-one-subject-out (LOSO) cross-validation within their respective modalities. Subsequently, cross-modal validation was conducted: testing the ASL-trained classifier with PET data (ASL-to-PET validation) and vice versa (PET-to-ASL validation). Performance of each classifier was assessed using receiver operating characteristic (ROC) analysis, with area under the curve (AUC) as the metric. Results: Both modalities showed agreement in decreased CBF and metabolism in DOC patients compared to HC, at both global and network levels. The global brain and most networks showed significant positive partial correlation between CBF and metabolism. SVM classifiers, utilizing activity from seven networks as features, performed well in the both LOSO cross-validation (ASL-trained classifier: accuracy = 83.02%, AUC = 0.95; PET-trained classifier: accuracy = 98.12%, AUC = 0.98) and cross-modal validation (ASL-to-PET validation: accuracy = 84.90%, AUC =0.92; PET-to-ASL validation: accuracy = 73.58%, AUC = 0.93). Conclusion: Our results demonstrate that ASL provides information comparable to FDG-PET for DOC patients; moreover, classifiers trained on ASL data perform comparably well to those trained on FDG-PET data. Therefore, ASL could be a valuable alternative to FDG-PET in the clinical diagnosis of DOC, especially in light of its advantages: ease of acquisition, avoidance of radiation exposure, brevity of scanning time, and lower-cost. (shrink)

# Theory of Harmonic Time (THT): A Speculative Framework *Disclaimer* This paper presents a highly speculative and theoretical framework, exploring potential connections between harmonic time and various established theories. The ideas discussed herein are hypothetical and require further development, refinement, and experimental verification. *Introduction* The Theory of Harmonic Time (THT) proposes that harmonic oscillations underlie the fundamental nature of time and the universe. This framework attempts to integrate concepts from various theories, including String Theory/M-Theory, Loop Quantum Gravity (LQG), Causal Dynamical (...) Triangulation (CDT), Asymptotic Safety, and Emergent Gravity. *THT-String Theory Proposal* Consider the harmonic oscillations of strings in String Theory/M-Theory. These oscillations give rise to a fractal structure, where the same harmonic patterns repeat at different scales. This fractal structure could be the fundamental unit of time. *THT-LQG Proposal* In LQG, consider the spin networks and holonomies that give rise to the fabric of spacetime. Introduce harmonic resonance, where spin networks and holonomies exhibit harmonic oscillations. These oscillations could give rise to a fractal structure. *THT-CDT Proposal* In CDT, consider the discretized spacetime, composed of simple geometric building blocks. Introduce harmonic oscillations, where these building blocks exhibit harmonic resonance. This resonance could give rise to a fractal structure. *THT-Asymptotic Safety Proposal* In Asymptotic Safety, consider gravitational waves and fluctuations. Introduce harmonic resonance, where these fluctuations exhibit harmonic oscillations. These oscillations could give rise to a fractal structure. *THT-Emergent Gravity Proposal* In Emergent Gravity, consider the collective behavior of particles. Introduce harmonic resonance, where particles exhibit harmonic oscillations. These oscillations could give rise to a fractal structure. *Conclusion* The Theory of Harmonic Time (THT) presents a speculative framework, attempting to integrate harmonic oscillations with various established theories. While highly speculative, this framework might offer new insights into the nature of time and the universe. Further development, refinement, and experimental verification are necessary to explore the potential implications of THT. (shrink)

This paper treats cognition as a function of consciousness and seeks to isolate neural correlates of consciousness as physical properties within the brain. We explore fundamental attributes of qualia paired with physical objects, suggesting all physical things have some relative quale. In this sense we require perception as input, a global workspace, cations, a calcium channel, awareness, attention, and a neural network. Given the hypothesized binding frequency of 40 Hz and Libet’s temporal factors, under Schrodinger’s time-dependent equation in n-dimensions (...) and potential energy given by spin attenuated under spherical properties as radial waves, we see energy levels on the quantum scale approximately equal to the energy of neuronal spikes on a classical scale separated by powers of ten. By treating the brain as a complex, compact manifold homotopic to submanifolds, under knot theory and braid groups we liken the model to quantum information processing and equilibria given by smoothness. Understanding that a model of binding is a partial solution to the hard problem of consciousness, we see consciousness emerges under the complexity of biological components as a function of neural energy. The result of the model is descriptive and explanatory, showing semantic information encoded within brain waves as brain waves signify consciousness can be encapsulated in physical properties of the brain such that binding occurs. With respect to binding and virtual particles, a partial solution to the hard problem of consciousness is proposed. (shrink)

Let us start by some general definitions of the concept of complexity. We take a complex system to be one composed by a large number of parts, and whose properties are not fully explained by an understanding of its components parts. Studies of complex systems recognized the importance of “wholeness”, defined as problems of organization (and of regulation), phenomena non resolvable into local events, dynamics interactions in the difference of behaviour of parts when isolated or in higher configuration, etc., in (...) short, systems of various orders (or levels) not understandable by investigation of their respective parts in isolation. In a complex system it is essential to distinguish between ‘global’ and ‘local’ properties. Theoretical physicists in the last two decades have discovered that the collective behaviour of a macro-system, i.e. a system composed of many objects, does not change qualitatively when the behaviour of single components are modified slightly. Conversely, it has been also found that the behaviour of single components does change when the overall behaviour of the system is modified. There are many universal classes which describe the collective behaviour of the system, and each class has its own characteristics; the universal classes do not change when we perturb the system. The most interesting and rewarding work consists in finding these universal classes and in spelling out their properties. This conception has been followed in studies done in the last twenty years on second order phase transitions. The objective, which has been mostly achieved, was to classify all possible types of phase transitions in different universality classes and to compute the parameters that control the behaviour of the system near the transition (or critical or bifurcation) point as a function of the universality class. This point of view is not very different from the one expressed by Thom in the introduction of Structural Stability and Morphogenesis (1975). It differs from Thom’s program because there is no a priori idea of the mathematical framework which should be used. Indeed Thom considers only a restricted class of models (ordinary differential equations in low dimensional spaces) while we do not have any prejudice regarding which models should be accepted. One of the most interesting and surprising results obtained by studying complex systems is the possibility of classifying the configurations of the system taxonomically. It is well-known that a well founded taxonomy is possible only if the objects we want to classify have some unique properties, i.e. species may be introduced in an objective way only if it is impossible to go continuously from one specie to another; in a more mathematical language, we say that objects must have the property of ultrametricity. More precisely, it was discovered that there are conditions under which a class of complex systems may only exist in configurations that have the ultrametricity property and consequently they can be classified in a hierarchical way. Indeed, it has been found that only this ultrametricity property is shared by the near-optimal solutions of many optimization problems of complex functions, i.e. corrugated landscapes in Kauffman’s language. These results are derived from the study of spin glass model, but they have wider implications. It is possible that the kind of structures that arise in these cases is present in many other apparently unrelated problems. Before to go on with our considerations, we have to pick in mind two main complementary ideas about complexity. (i) According to the prevalent and usual point of view, the essence of complex systems lies in the emergence of complex structures from the non-linear interaction of many simple elements that obey simple rules. Typically, these rules consist of 0–1 alternatives selected in response to the input received, as in many prototypes like cellular automata, Boolean networks, spin systems, etc. Quite intricate patterns and structures can occur in such systems. However, what can be also said is that these are toy systems, and the systems occurring in reality rather consist of elements that individually are quite complex themselves. (ii) So, this bring a new aspect that seems essential and indispensable to the emergence and functioning of complex systems, namely the coordination of individual agents or elements that themselves are complex at their own scale of operation. This coordination dramatically reduces the degree of freedom of those participating agents. Even the constituents of molecules, i.e. the atoms, are rather complicated conglomerations of subatomic particles, perhaps ultimately excitations of patterns of superstrings. Genes, the elementary biochemical coding units, are very complex macromolecular strings, as are the metabolic units, the proteins. Neurons, the basic elements of cognitive networks, themselves are cells. In those mentioned and in other complex systems, it is an important feature that the potential complexity of the behaviour of the individual agents gets dramatically simplified through the global interactions within the system. The individual degrees of freedom are drastically reduced, or, in a more formal terminology, the factual space of the system is much smaller than the product of the state space of the individual elements. That is one key aspect. The other one is that on this basis, that is utilizing the coordination between the activities of its members, the system then becomes able to develop and express a coherent structure at a higher level, that is, an emergent behaviour (and emergent properties) that transcends what each element is individually capable of. (shrink)

The extended dual-aspect monism framework of consciousness, based on neuroscience, consists of five components: (1) dual-aspect primal entities; (2) neural-Darwinism: co-evolution and co-development of subjective experiences (SEs) and associated neural-nets from the mental aspect (that carries the SEs/proto-experiences (PEs) in superposed and unexpressed form) and the material aspect (mass, charge, spin and space-time) of fundamental entities (elementary particles), respectively and co-tuning via sensorimotor interaction; (3) matching and selection processes: interaction of two modes, namely, (a) the non-tilde mode that is (...) the material and mental aspect of cognition (memory and attention) related feedback signals in a neural-network, which is the cognitive nearest past approaching towards present; and (b) the tilde mode that is the material and mental aspect of the feed forward signals due to external environmental input and internal endogenous input, which is the nearest future approaching towards present and is a entropy-reversed representation of non-tilde mode; (4) the segregation and integration of information, and (5) the necessary ingredients of SEs (such as wakefulness, attention, re-entry, working memory, stimulus at or above threshold level, and neural-net PEs). This framework leads to structural and functional coherence between the mind and the brain, bridges the explanatory gap (the gap between SEs and their neural-correlates), and leads to our mundane subjective experiences. This extended dual-aspect monism (eDAM) framework (Vimal, 2008, 2010, 2013, 2015a, 2015b) could be the fundamental basis of various religions and philosophies. This is a Western perspective. On the other hand, Eastern perspectives emphasize the practical methods for achieving altered experience at samadhi state. An important corollary of these methods (such as yogic method) is the sublimation of negative aspects of seven groups of self-protective energy system (desire, anger, ego, greed, attachment, jealousy, and selfish-love) into their positive aspects. Their negative aspects create war and suffering, whereas their positive aspects advance science and technology, family values, peace, and happiness. Here, the Western perspective framework is extended to include the concepts of the sublimation process to encompass Eastern perspectives. The four elements (war, suffering, peace, and happiness) are ubiquitous in both space and time because they are essential contributors to the variations for natural selection in our evolutionary system. The sublimation process optimizes the system: minimizes war and suffering, maximizes peace and happiness, and enhances family values and individual progress. This is consistent with both Eastern and Western perspectives. (shrink)

Internodons are a formalization of Hennig's concept of species. We present an alternative construction of internodons imposing a tree structure on the genealogical network. We prove that the segments (trivial unary trees) from this tree structure are precisely the internodons. We obtain the following spin-offs. First, the generated tree turns out to be an organismal tree of life. Second, this organismal tree is homeomorphic to the phylogenetic Hennigian species tree of life, implying the discovery of a multi-level tree (...) of life: this phylogenetic tree can be obtained by zooming out from the organismal tree, or conversely, the organismal tree of life can be generated by expanding the phylogenetic nodes into unary trees. Finally, the definition of the organismal tree allows an efficient algorithmic transformation of a given genealogical network into its corresponding phylogenetic species tree of life. The latter will be presented in a separate paper. (shrink)

Theory of everything must include consciousness. In this Part I of the series of three articles, we introduce the subjective experience (SE) and/or proto‐experience (PE) aspect of consciousness in classical physics, where PEs are precursors of SEs. In our dualaspect‐ dual‐mode PE‐SE framework, it was hypothesized that fundamental entities (strings or elementary particles: fermions and bosons) have two aspects: (i) material aspect such as mass, charge, spin, and space‐time, and (ii) mental aspect, such as experiences. There are three competing (...) hypotheses: (1) superposition based H1 (SEs/PEs are superposed in the mental aspect of entities; when a specific stimulus is presented to the neural‐network, the associated specific SE is selected by the matching and selection process and experienced by this network), (2) superposition‐then‐integration based H2 (only PEs are superposed, which are integrated by neural‐Darwinism leading to specific SEs) and (3) integration based H3 (each entity has its own PE, which keeps on transforming appropriately as matter evolves from elementary particles to neuralnetworks; it is a dual‐aspect panpsychism). We found that the followings, in classical physics, are invariant under the PE‐SE transformation: electromagnetic strength tensor, electromagnetic stress‐energy tensor, the electromagnetic theory (Maxwell's equations), Newtonian gravitational field, the entropic force, Special and General Theory of Relativity. Our analysis suggests that (i) SEs are embedded in space‐time geometry for the structure of space‐time (empty space or the vacuum without matter). (ii) For matter field, SEs can move with spatiotemporal coordinates of matter because it is in the mental aspect of matter as both mental and material aspects are always together in the dual‐aspect‐dual‐mode optimal PE‐SE framework. (iii) Our specific SE is the result of matching and selection processes and can change with space and time. For example, the experience redness has V4/V8/VO‐red‐green neural‐network with rednessstate as neural correlates. When a subject moves, the specific SE redness also moves with the subject’s correlated neural‐network. In addition, SEs can change with time as stimuli change. In other words, SEs in a subject change with space‐time. We conclude that it is possible to introduce the SE/PE aspect of consciousness in classical physics. In Parts II and III, the SE aspect of consciousness will be introduced in orthodox quantum physics and modern quantum physics (such as loop quantum gravity and string theory), respectively. Thus, the introduction of the SE aspect of consciousness in physics leads us to unify consciousness with known four fundamental forces, which entails towards a theory of everything. (shrink)

A general definition of consciousness is: ‘consciousness is a mental aspect of a system or a process, which is a conscious experience, a conscious function, or both depending on the context’, where the term context refers to metaphysical views, constraints, specific aims, and so on. One of the aspects of visual consciousness is the visual subjective experience (SE) or the first person experience that occurs/emerges in the visual neural-network of thalamocortical system (which includes dorsal and ventral visual pathways and (...) frontal cortex) during dynamic interactions among widely distributed neuronal groups. In this article, however, consciousness and SE are interchangeably used unless noted. Consciousness can be either access (reportable) or phenomenal consciousness. For access consciousness, the interactions are between feed forward stimulus dependent signals and fronto-parietal feedback attentional signals. The necessary ingredients for access (reportable) consciousness are (i) wakefulness, (ii) reentrant interactions among neural populations, (iii) fronto-parietal and thalamic-reticular-nucleus attentional signals that modulate consciousness, (iv) working memory that retains information for consciousness, (v) stimulus at or above threshold level, and (vi) neural-network proto-experiences (PEs) that are superposed SEs embedded in a neural-network. Attention and the ability to report are not necessary for phenomenal consciousness. The neural source for the arousal system is the ascending reticular activating system in the brain stem, which brings the thalamocortical neural networks to wakeful state as a baseline for consciousness to occur. Reentrant interactions among neural populations bind stimulus attributes (such as location and features) and entail consciousness. Attention could be the results of reentry and competitive interactions, and modulates the stimulus related feed forward signal and consciousness. The ‘sources’ of attention may be thalamic reticular nucleus for bottom-up or frontal cortex for top-down direction. The ‘target’ of visual attention is ‘V4/V8/VO’ for Red-Green (R-G) channel. The neural correlates of the psychophysical entity R-G channel appear to be ‘V4/V8/VO’-neural-network (retina → LGN ↔ V1 ↔ V2 ↔ ‘V4/V8/VO’, and areas for attention, memory, and wakefulness). The psychophysical Red-Green Channel, its neurophysiological correlates V4/V8/VO-neural-network, and related experience (such as redness) are integrated. The dual-aspect-dual-mode PE-SE framework (Vimal, J Integr Neurosci 7:49–73, 2008) was used to address the explanatory gap problem of materialism. Neural-network and related SEs are the results of the co-evolution and co-development of the material aspect (mass, charge, spin, force, quanta, and space-time) and the mental aspect of fundamental particles (strings or elementary particles (fermions and bosons)). Their mental aspects are considered as the carriers of superposed multiple possible experiences (SEs/PEs) in unexpressed form. These possibilities are actualized when neural-networks are formed via neural Darwinism, and a specific SE is selected by a matching process when the necessary ingredients of consciousness/SE are satisfied. A simple experimental design is suggested to address the necessary and sufficient attributes of consciousness. (shrink)

One of the most conspicuous features of contemporary modeling practices is the dissemination of mathematical and computational methods across disciplinary boundaries. We study this process through two applications of the Ising model: the Sherrington-Kirkpatrick model of spin glasses and the Hopfield model of associative memory. The Hopfield model successfully transferred some basic ideas and mathematical methods originally developed within the study of magnetic systems to the field of neuroscience. As an analytical resource we use Paul Humphreys's discussion of computational (...) and theoretical templates. We argue that model templates are crucial for the intra- and interdisciplinary theoretical transfer. A model template is an abstract conceptual idea associated with particular mathematical forms and computational methods. (shrink)

In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful (...) in explaining the properties of non-decomposable systems. Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems, and does so without succumbing to problems associated with combinatorial explosion. The article concludes with a comparison between the uses of network representation analyzed in the main discussion, and an entirely distinct use of network representation that has recently been discussed in connection with mechanistic modeling. (shrink)

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water (...) quality monitoring and decision-making for stakeholders and policymakers. (shrink)

This paper engages with the following closely related questions that have recently received some attention in the literature: what is the status of the equivalence principle in general relativity?; how does the metric field obtain its property of being able to act as a metric?; and is the metric of GR derivative on the dynamics of the matter fields? The paper attempts to complement these debates by studying the spin-2 approach to gravity. In particular, the paper argues that three (...) lessons can be drawn from the spin-2 approach: different from what is sometimes claimed in the literature, central aspects of the non-linear theory of GR are already derivable in classical spin-2 theory; in particular, ‘universal coupling’ can be considered a derived ‘theorem’ in both the classical and the quantum spin-2 approach; this provides new insights for the investigation of the equivalence principle; the ‘second miracle’ that Read et al. argue characterises GR is explained in the classical as well as in the quantum version of the spin-2 approach; the spin-2 approach allows for an ontological reduction of the metrical part of spacetime to the dynamics of matter fields. (shrink)

In this chapter, I consider three ‘promises’ of bringing phenomenology into dialogue with networked learning. First, a ‘conceptual promise’, which draws attention to conceptual resources in phenomenology that can inspire and inform how we understand, conceive of, and uncover experiences of participants in networked learning activities and environments. Second, a ‘methodological promise’, which outlines a variety of ways that phenomenological methodologies and concepts can be put to use in empirical research in networked learning. And third, a ‘critical promise’, which suggests (...) how work done in the realm of critical phenomenology is complementary to and useful for exploring the social justice and emancipatory aims of networked learning researchers. Through this framework, I reflect upon multiple ways in which phenomenology has and might continue to fruitfully inform and shape networked learning research. In doing so, I emphasise that deploying phenomenology in the context of networked learning not only enriches our understanding of networked learning but that phenomenology itself is enriched through testing, finetuning, and expanding its methods, concepts, and understanding, as well as unveiling its own limitations and constraints. (shrink)

Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing (...) about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds. (shrink)

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and (...) others’, in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. (shrink)

The self-interaction spin-2 approach to GR has been extremely influential in the particle physics community. Leaving no doubt regarding its heuristic value, we argue that any view of the metric field of GR as nothing but a stand-in for a self-coupling field in at spacetime runs into a dilemma: either the view is physically incomplete in so far as it requires recourse to GR after all, or it leads to an absurd multiplication of alternative viewpoints on GR rendering any (...) understanding of the metric field as nothing but a spin-2 field in at spacetime unjustified. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with (...) 97.50 % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

Accurate assessment and comprehension of smoking behavior are pivotal for elucidating associated health risks and formulating effective public health strategies. In this study, we introduce an innovative approach to predict and analyze smoking prevalence using an artificial neural network (ANN) model. Leveraging a comprehensive dataset spanning multiple years and geographic regions, our model incorporates various features, including demographic data, economic indicators, and tobacco control policies. This research investigates smoking trends with a specific focus on gender-based analyses. These findings are (...) pivotal for enhancing public health initiatives aimed at mitigating tobacco use, assessing associated health risks, and mitigating smoking-related diseases. (shrink)

Bruno Latour claims to have shown that a Kantian model of knowledge, which he describes as seeking to unite a disembodied transcendental subject with an inaccessible thing-in-itself, is dramatically falsified by empirical studies of science in action. Instead, Latour puts central emphasis on scientific practice, and replaces this Kantian model with a model of “circulating reference.” Unfortunately, Latour's alternative schematic leaves out the scientific subject. I repair this oversight through a simple mechanical procedure. By putting a slight spin on (...) Latour's diagrammatic representation of his theory, I discover a new space for a post-Kantian scientific subject, a subject brilliantly described by Ludwik Fleck. The neglected subjectivities and ceaseless practices of science are thus re-united. (shrink)

Quelle stratégie d’innovation choisir ? Comment la mettre en oeuvre ? Dans un contexte général de digitalisation et d’ubérisation des marchés, l’innovation, et notamment les innovations disruptives, sont des objectifs usuellement affichés dans les entreprises.

Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description logic (...) as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation C(a) for some concept C” or “what are the roles R that connect the individuals a and b to degree of sharedness ε.” . (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of (...) three layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

In this paper, we review a general technique for converting the standard Lagrangian description of a classical system into a formulation that puts time on an equal footing with the system's degrees of freedom. We show how the resulting framework anticipates key features of special relativity, including the signature of the Minkowski metric tensor and the special role played by theories that are invariant under a generalized notion of Lorentz transformations. We then use this technique to revisit a classification of (...) classical particle-types that mirrors Wigner's classification of quantum particle-types in terms of irreducible representations of the Poincaré group, including the cases of massive particles, massless particles, and tachyons. Along the way, we see gauge invariance naturally emerge in the context of classical massless particles with nonzero spin, as well as study the massless limit of a massive particle and derive a classical-particle version of the Higgs mechanism. (shrink)

Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit communities can maintain high levels of linguistic complexity and variability. However, the role of social network structure in the cultural evolution of languages has never been tested experimentally. Here, we present (...) results from a behavioral group communication study, in which we examined the formation of new languages created in the lab by micro‐societies that varied in their network structure. We contrasted three types of social networks: fully connected, small‐world, and scale‐free. We examined the artificial languages created by these different networks with respect to their linguistic structure, communicative success, stability, and convergence. Results did not reveal any effect of network structure for any measure, with all languages becoming similarly more systematic, more accurate, more stable, and more shared over time. At the same time, small‐world networks showed the greatest variation in their convergence, stabilization, and emerging structure patterns, indicating that network structure can influence the community's susceptibility to random linguistic changes (i.e., drift). (shrink)

The purpose of this chapter is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - and therefore the learning of knowledge - is distributive, that is, not located in any given place (and therefore not 'transferred' or 'transacted' per se) but rather consists of the network of connections formed from experience and interactions with a knowing community. (...) And another part of this thinking is centered around the new, and the newly empowered, learner, the member of the net generation, who is thinking and interacting in new ways. These trends combine to form what is sometimes called 'e-learning 2.0' - an approach to learning that is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather, as embedded in meaningful activities such as games or workflows. (shrink)

Cyber espionage campaigns pose significant challenges to global security, exploiting vulnerabilities in network infrastructures. This research paper explores advanced network traffic analysis models tailored for detecting sophisticated cyber espionage operations. The study focuses on leveraging machine learning algorithms, anomaly detection systems, and hybrid threat detection frameworks to identify subtle yet malicious activities within network traffic. Through a review of research, this paper synthesizes key findings and outlines practical applications, offering a roadmap for enhancing cybersecurity frameworks. Findings highlight (...) the efficacy of deep learning and unsupervised models in mitigating espionage-related risks, paving the way for more robust detection systems. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to (...) novel capabilities of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)

In this paper I discuss the ontological status of actants. Actants are argued as being the basic constituting entities of networks in the framework of Actor Network Theory (Latour, 2007). I introduce two problems concerning actants that have been pointed out by Collin (2010). The first problem concerns the explanatory role of actants. According to Collin, actants cannot play the role of explanans of networks and products of the same newtork at the same time, at pain of circularity. The (...) second problem is that if actants are, as suggested by Latour, fundamentally propertyless, then it is unclear how they combine into networks. This makes the nature of actants inexplicable. -/- I suggest that both problems rest on the assumption of a form of object ontology, i.e. the assumption that the ontological basis of reality consists in discrete individual entities that have intrinsic properties. I argue that the solution to this problem consists in the assumption of an ontology of relations, as suggested within the framework of Ontic Structural Realism (Ladyman & Ross, 2007). Ontic Structural Realism is a theory concerning the ontology of science that claims that scientific theories represent a reality consisting on only relation, and no individual entities. -/- Furthermore I argue that the employment of OSR can, at the price of little modification for both theories, solve both of the two problems identified by Collin concerning ANT. -/- Throughout the text I seek support for my claims by referring to examples of application of ANT to the context of networked learning. As I argue, the complexity of the phenomenon of networked learning gives us a convenient vantage point from which we can clearly understand many important aspects of both ANT and OSR. -/- While my proposal can be considered as an attempt to solve Collin's problems, it is also an experiment of reconciliation between analytic and constructivist philosophy of science. -/- In fact I point out that on the one hand Actor Network Theory and Ontic Structural Realism show an interesting number of points of agreement, such as the naturalistic character and the focus on relationality. On the other hand, I argue that all the intuitive discrepancies that originates from the Science and Technology Studies’ criticism against analytic philosophy of science are at a closer look only apparent. (shrink)

This article brings together two research fields in applied ethics - namely, information ethics and business ethics- which deal with the ethical impact of information and communication technologies but that, so far, have remained largely independent. Its goal is to articulate and defend an informational approach to the conceptual foundation of business ethics, by using ideas and methods developed in information ethics, in view of the convergence of the two fields in an increasingly networked society.

We propose a percolation based M2M networking architecture and its data transmission method. The proposed network architecture can be server free and router free, which allows us to operate routing efficiently with percolations based on six degrees of separation theory in small world network modeling. The data transmission can be divided into two phases routing and data transmission phase. In the routing phase, probe packets will be transmitted and forwarded in the network thus path selections are performed (...) based on small world strategy. In the second phase, the information will be encoded, say, with the fountain codes, and transmitted using the paths selected at the first phase. In such a way, an efficient routing and data transmission mechanism can be built, allowing us to construct a low cost, flexible and ubiquitous network. Such a networking architecture and data transmission can be used in many M2M communications, such as the stub network of internet of things, and deep space networking, and so on. Soujanya Ambala | Dr. Srinivas Ambala | Sreedhar Ambala "M2M Networking Architecture for Data Transmission and Routing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-1 , December 2016. (shrink)

This essay inquires into the possibility of extending Randall Collins' analysis (as it is presented in The Sociology of Philosophies) of the process of innovation within intellectual networks.

I explored the association between components constituting the basis for moral and optimal human functioning, i.e., moral reasoning, moral identity, empathy, and purpose, via network analysis. I employed factor scores instead of composite scores that most previous studies used for better accuracy in score estimation in this study. Then, I estimated the network structure among collected variables and centrality indicators. For additional information, the structure and indicators were compared between two groups, participants who engaged in civic activities highly (...) versus lowly. The results demonstrated significant intra- and inter-scale associations in the network. The network structure was invariant across the two groups. Despite the network invariance, I found that the global connectivity between and centrality indicators of examined factors were higher among the high civic engagement group in general. I discussed the implications of the findings in research on moral functioning based on moral psychology and virtue ethics. (shrink)

In this paper, we explore the conceptual problems arising when using network analysis in person- centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises boundary problems, i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that we can make (...) more explicit what kind of questions personalized network models can address in PCC, given their representational and explanatory boundaries, using perspectival reasoning. (shrink)

A small consortium of philosophers has begun work on the implications of epistemic networks (Zollman 2008 and forthcoming; Grim 2006, 2007; Weisberg and Muldoon forthcoming), building on theoretical work in economics, computer science, and engineering (Bala and Goyal 1998, Kleinberg 2001; Amaral et. al., 2004) and on some experimental work in social psychology (Mason, Jones, and Goldstone, 2008). This paper outlines core philosophical results and extends those results to the specific question of thresholds. Epistemic maximization of certain types does show (...) clear threshold effects. Intriguingly, however, those effects appear to be importantly independent from more familiar threshold effects in networks. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method (...) is suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

We consider how an epistemic network might self-assemble from the ritualization of the individual decisions of simple heterogeneous agents. In such evolved social networks, inquirers may be significantly more successful than they could be investigating nature on their own. The evolved network may also dramatically lower the epistemic risk faced by even the most talented inquirers. We consider networks that self-assemble in the context of both perfect and imperfect communication and compare the behaviour of inquirers in each. This (...) provides a step in bringing together two new and developing research programs, the theory of self-assembling games and the theory of network epistemology. (shrink)

Depression is a common and devastating instance of ill-being which deserves an account. Moreover, the ill-being of depression is impacted by digital technology: some uses of digital technology increase such ill-being while other uses of digital technology increase well-being. So a good account of ill-being would explicate the antecedents of depressive symptoms and their relief, digitally and otherwise. This paper borrows a causal network account of well-being and applies it to ill-being, particularly depression. Causal networks are found to provide (...) a principled, coherent, intuitively plausible, and empirically adequate account of cases of depression in every-day and digital contexts. Causal network accounts of ill-being also offer philosophical, scientific, and practical utility. Insofar as other accounts of ill-being cannot offer these advantages, we should prefer causal network accounts of ill-being. (shrink)

The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. The model architecture is designed to extract intricate features, enabling accurate identification even in challenging scenarios such as varying lighting conditions, occlusions, or similar species appearances. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring comprehensive validation. Results indicate significant accuracy (...) improvements over traditional machine learning approaches, demonstrating the potential of deep learning in species identification. This project holds promise for applications in wildlife monitoring, ecological research, and educational tools, promoting awareness and conservation efforts. Future work may include integrating the system into mobile applications or deploying it for real-time bird species identification in field conditions. (shrink)

Intelligent Tutoring Systems (ITS) has a wide influence on the exchange rate, education, health, training, and educational programs. In this paper we describe an intelligent tutoring system that helps student study computer networks. The current ITS provides intelligent presentation of educational content appropriate for students, such as the degree of knowledge, the desired level of detail, assessment, student level, and familiarity with the subject. Our Intelligent tutoring system was developed using ITSB authoring tool for building ITS. A preliminary evaluation of (...) the ITS was done by a group of students and teachers. The results were acceptable. (shrink)

Categorization and memory for specific items are fundamental processes that allow us to apply knowledge to novel stimuli. This study directly compares categorization and memory using delay match to category (DMC) and delay match to sample (DMS) tasks. In DMC participants view and categorize a stimulus, maintain the category across a delay, and at the probe phase view another stimulus and indicate whether it is in the same category or not. In DMS, a standard item working memory task, participants encode (...) and maintain a specific individual item, and at probe decide if the stimulus is an exact match or not. Constrained Principal Components Analysis was used to identify and compare activity within neural networks associated with these tasks, and we relate these networks to those that have been identified with resting state-fMRI. We found that two frontoparietal networks of particular interest. The first network included regions associated with the dorsal attention network and frontoparietal salience network; this network showed patterns of activity consistent with a role in rapid orienting to and processing of complex stimuli. The second uniquely involved regions of the frontoparietal central executive network; this network responded more slowly following each stimulus and showed a pattern of activity consistent with a general role in role in decision-making across tasks. Additional components were identified that were associated with visual, somatomotor and default mode networks. (shrink)

—Industrial networks are increasingly based on open protocols and platforms that are also employed in the IT industry and Internet background. Most of the industries use wireless networks for communicating information and data, due to high cable cost. Since, the wireless networks are insecure, it is essential to secure the critical information and data during transmission. The data that transmitted is intercepted by eavesdropper can be predicted by secrecy capacity. The secrecy capacity is the difference between channel capacity of main (...) link and wiretap link of wireless transmission. When the secrecy capacity gets fades, then it is known that transmitted data is intercepted. In the event, of applying optimal sensor scheduling scheme a sensor with highest secrecy capacity is chosen and data is transmitted. It is followed with cooperative localization algorithm to enhance nodes, predict and maintain power of each sensor nodes. Through localization algorithm best sensor nodes are get predicted for transmission. Moreover, RC4 encryption and decryption algorithm is enhanced for data while transmission. This will enable protection to data to an extent. Also, an asymptotic intercept probability analysis is performed to provide an imminent into the impact of the sensor scheduling on the wireless security. (shrink)

This draft investigates Genetic Networks as a special case with comparisons to other networks. The intention is to discover the mapping between generic and abstract network properties and specific case studies.

The article addresses the problem of how semantic information can be upgraded to knowledge. The introductory section explains the technical terminology and the relevant background. Section 2 argues that, for semantic information to be upgraded to knowledge, it is necessary and sufficient to be embedded in a network of questions and answers that correctly accounts for it. Section 3 shows that an information flow network of type A fulfils such a requirement, by warranting that the erotetic deficit, characterising (...) the target semantic information t by default, is correctly satisfied by the information flow of correct answers provided by an informational source s. Section 4 illustrates some of the major advantages of such a Network Theory of Account (NTA) and clears the ground of a few potential difficulties. Section 5 clarifies why NTA and an informational analysis of knowledge, according to which knowledge is accounted semantic information, is not subject to Gettier-type counterexamples. A concluding section briefly summarises the results obtained. (shrink)

Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. It (...) involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an (...) average error of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

It is common for social theorists to utilize the metaphors of ‘flow’, ‘fluidity’, and ‘liquidity’ in order to substantiate the ways in which speed and mobility form the basis for a new kind of information or network society. Yet rarely have these concepts been sufficiently theorized in order to establish their relevance or appropriateness. This article contends that the notion of flow as utilized in social theory is profoundly metaphysical in nature, and needs to be judged as such. Beginning (...) with a discussion of the accelerating timescape that characterizes the network society, it will then move on to examine three main issues with this ‘metaphysics of flux’. First, that the concept of flows unjustly privileges the process of becoming and, as a result, is unable to account for the materiality, substantiality, and agency of the objects being mobilized, and the contingency of their mediation. Second, that it posits the accelerating tendencies of capital as an ontological inevitability, thus discounting resistance to such forces. Finally, that it ignores the human faculty for reason and speculative thought in developing alternative means of political praxis. The solution, it will be argued, is not to abandon metaphysical accounts of the network society, but rather to challenge those accounts that, in exhibiting a crude empiricism, work to justify the status quo. (shrink)

This research paper delves into the realm of Financial Network Analytics, unraveling the opportunities and challenges it presents in the domains of systemic risk management and investment analysis. The study navigates the intricate web of interconnected financial entities, employing advanced analytics techniques to uncover meaningful insights. Through a comprehensive exploration, the paper identifies opportunities for leveraging financial network data to enhance systemic risk detection mechanisms and refine investment strategies. Simultaneously, it addresses the inherent challenges, such as data complexity (...) and dynamic network structures, offering nuanced perspectives on their implications. The findings contribute to the evolving landscape of financial analytics, providing a foundation for the development of robust tools and methodologies that can aid financial institutions and investors in navigating the complexities of modern financial networks. (shrink)

Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In (...) particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks. (shrink)

The philosophical literature on personal relationships is focused on dyads: close relationships between just two people. This paper aims to characterise the value of looser and larger relational networks, particularly from the perspective of liberal political theory. We focus on relational networks' value vis-a-vis the important good of felt security. We begin by characterising felt security and analysing how felt security is produced within dyads. We highlight the ambivalent nature of dyadic relationships as a source of felt security. We then (...) theorise relational networks, explaining how they too can provide felt security. We explore three unappreciated benefits that arise when felt security is garnered from a network, rather than from a dyad. These benefits take liberal political theory as their starting point. We close by considering the implications - for both individual action and institutional design - of using relational networks as a source of felt security. (shrink)