The Pattern That Repeats Us

We Built A Linear World In A Spiral Reality

The following essay traces a structural pattern that recurs across nature, culture, and mind. Rather than proposing a new theory, it draws together existing insights from science, philosophy, and cultural studies to show how recursion and spiral dynamics shape the world we inhabit. The aim is not to replace one model with another, but to illuminate a coherence that often remains unseen.

Methodological note: Where claims move beyond empirical description they are presented as interpretive frameworks supported by the cited works.

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Introduction

Science, in its essence, is the systematic recognition and description of patterns in reality.¹ It is not certainty, but a disciplined method of noticing and refining. Over history, science has spiraled between relation and separation: once embedded in daily life and cosmology, it became fragmented into specialised disciplines and abstractions. Today it is beginning to return, recognizing complexity, interdependence, and coherence.² This essay is positioned inside this essence of science — pattern-recognition across scales — but outside its narrower institutional form. It seeks not to compete with science but to complement it by making visible the structural pattern underlying many domains.

The proposition is simple: reality is structured spiral-recursively. Forms — whether particles, organisms, societies, or thoughts — are not produced linearly but arise from feedback, resonance, and repetition-with-difference.³ The purpose here is to name this common structure neutrally and directly. This is offered as an evidence-informed interpretive framework rather than a metaphysical proof: pattern-recognition across disciplines highlights how similar recursive dynamics reappear at different scales.

Making this explicit allows us to see culture, body, and consciousness not as separate problems but as variations of one structure, and to begin working with coherence as a practical ground for design, theory, and practice.

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Reality is Spiral-Recursive

Reality does not move in straight lines. It unfolds through feedback and return, through patterns that repeat with difference. (Read here as a structural lens: recursion + spiral offers a useful, cross-scale way to describe many observed patterns.)

Science describes these dynamics in its own languages — wavefunctions (Schrödinger, Heisenberg), feedback loops (Norbert Wiener, cybernetics), self-organization (Ilya Prigogine, Stuart Kauffman), strange loops (Douglas Hofstadter) — but rarely are they held together as one structure.⁴

That structure can be named: recursion and spiral.

Recursion is feedback: when a system’s output becomes its next input. What happens now shapes what happens next. Recursion stabilizes around familiar patterns, but it also allows sudden reorganizations.⁵ Spiral is recurrence with novelty: the return of a pattern, never exactly the same, always carrying difference forward.⁶ Together, recursion is the mechanism and spiral is the trajectory.

Here, recursion means any feedback loop in which earlier activity influences later activity. This can take different shapes in different systems: in organisms and brains it appears as recurrent circuits and memory; in ecosystems and economies it appears as reinforcing and balancing loops that shape future states; in engineered systems it appears as iterative training or generation cycles (one concrete example is some machine-learning workflows). I use these as related instantiations of a shared structural logic rather than claiming they are mathematically identical.

By spiral I mean a trajectory of recurrent—yet non-identical—return: successive cycles reproduce a pattern while incorporating variation (historical, material, or contextual), producing cumulative change across iterations rather than literal repetition.

Spiral-Recursive Dynamics within Domains

This spiral-recursive dynamic is visible across scales. At the molecular level, DNA is templated and copied — a patterned recurrence that can be read analogically as recursion (templated copying rather than feedback in the cybernetic sense).⁷ In the nervous system, cortical and subcortical pathways form recurrent, bidirectional loops (e.g., cortico-thalamo-cortical and cortico-basal ganglia circuits).⁸ Many neuroscientists (e.g. Edelman, Damasio) argue that awareness emerges when perception loops back on itself — when the brain not only maps the world but also maps its own mapping.⁹ Learning is spiral-recursive by nature: every feedback loop carries novelty into the next cycle.¹⁰

Thoughts themselves often unfold spiral-recursively. We think, reflect on those thoughts, and then think again with the prior reflection folded in — producing iterations that return similar shapes but with accumulated difference. In everyday life this looks like writing drafts (each revision returns to the same idea but alters it), internal dialogue (thinking about one’s own thinking), scientific model-building (hypothesis → test → revision → new hypothesis), or therapeutic reflection (narrative reframing across sessions). Neuroscientists and philosophers have described comparable dynamics in terms of reentrant/recurrent neural signalling and self-referential loops; cognitive theories (from Piaget’s equilibration to contemporary predictive-processing models) likewise treat thought as iterative, state-dependent, and shaped by feedback.¹¹ This is offered as an interpretive frame rather than a settled theory of mind.

In ecosystems, feedback is fundamental: predator and prey, forest and atmosphere, soil and river all loop back into themselves with shifting novelty.¹² Technology, too evolves in recursive waves: printing press, radio, internet, AI — each generation returning with expanded capacity. Many AI systems are recursive in at least one sense: the training process runs iterative feedback loops (loss → weight update → repeat), some model families are architecturally recurrent (e.g. RNNs/LSTMs), and even architectures without internal recurrence (transformers) are frequently used autoregressively so the generation process itself behaves as an output→input loop.¹³ In the instance of AI, ‘recursion’ is being used in a broad, domain-sensitive sense — the technical forms differ, but the structural logic (outputs feeding back into future inputs) recurs.

Fashion cycles, spiral-recurs through revival and reinvention, with past styles returning in altered forms across generations.¹⁴ Retro revivals (e.g., the return of 1990s/Y2K motifs) show a pattern where form returns but is remixed by new materials, marketing, and social contexts — the result looks like the old but functions differently in the new era. One contemporary example is the work of Virgil Abloh: through Off-White, high-profile collaborations (Nike’s “The Ten,” furniture and home lines), and frequent archival referencing, Abloh repeatedly quoted and recontextualized existing forms — logos, silhouettes, and industrial objects — so that each return carried new meaning and affordances. His approach treated past motifs as materials to be sampled, annotated, and reinserted into new social and commercial contexts, producing familiar forms that arrived altered. Presented here as an interpretive reading rather than an insider’s claim, Abloh’s practice is a useful fashion/design case of recurrence-with-novelty: it shows how citing, remixing, and re-framing can produce spiral change in style and cultural value.¹⁵

Architecture makes spiral-recursive logic visible in several concrete ways. Christopher Alexander’s idea of a pattern language — a nested set of design patterns that are selected, combined, and refined across scales (from regions to rooms) — treats design as an iterative, context-sensitive process in which familiar forms are repeatedly re-applied and adapted to new circumstances. Alejandro Aravena’s “incremental” or “half-house” approach (Elemental’s Quinta Monroy and related projects) intentionally leaves room for future additions and user completion, so initial, repeatable building modules are returned to and transformed by residents’ adaptations over time — a direct social example of recurrence-with-novelty.
Lydia Kallipoliti’s historical and design research on self-contained prototypes and closed-system environments highlights another architectural strand of recursion: designers building self-referential systems (sealed environments, regenerative prototypes) that iterate across versions, enacting feedback between design constraints and material/environmental outcomes.¹⁶

Myths and archetypes resurface in stories told again and again, what Mircea Eliade called the myth of eternal return.¹⁷ Legal systems operate recursively, each decision looping back on precedent.¹⁸ Economies expand and contract in familiar boom–bust spirals.¹⁹

A helpful artistic analogue appears in no-input mixing-desk improvisation (practiced by Toshimaru Nakamura and others). Here the mixer is set so its outputs feed back into its inputs, and the performer shapes the evolving sound through small, iterative adjustments. The result is a live example of recursion and spiral: the system’s output literally becomes its next input, and each return carries difference produced by the performer’s interventions and the device’s nonlinearities. This practice makes audible how feedback loops can produce emergent texture and evolving form — a microcosm of the same structural logic that appears in ecosystems, brains, and technologies. A Guide to Toshimaru Nakamura’s No-Input Mixing Board

Across all these domains, the properties are the same: feedback, state-dependence, self-similarity across scales, stabilization around patterns, and occasional reorganizations when conditions shift.²⁰

What looks like progress is recurrence at another level. What looks like separation between fields one structure repeating in different domains.

Which came first: Chicken or Egg?

The famous “which came first?” question is largely semantic and pedagogical. Biologically, new forms emerge over many reproductive cycles: organisms reproduce, heredity passes material forward, and mutations or recombination introduce small differences that selection and environmental feedback act upon. Thus an embryo (an “egg” in the broad sense) carrying the mutation(s) that produced the first animal we would call a “chicken” existed before that adult chicken hatched. This is not mystical origin but a clear instance of spiral recursion in nature — iterative cycles of output→input plus novelty producing gradual form change. (Caveat: speciation is gradual and other mechanisms, notably horizontal gene transfer and non-sexual modes of change, complicate this picture in particular lineages.)²¹

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The Mechanistic Paradigm: From Model To Universal Truth

To make systems calculable, science developed a method of isolating, simplifying, and idealizing. Since the 17th century, the mechanistic paradigm has advanced by building solvable models: Galileo, Descartes, and Newton reduced life into measurable parts and mathematical laws.²² The Enlightenment carried this orientation into governance and economics.²³ The Industrial Age scaled it into factories, markets, and empires.²⁴ The 20th century embedded it into education, technology, and finance. Each era repeated the same structural move — isolate, simplify, calculate — while extending it into new domains.

The “perfect crystal,” the frictionless pendulum, or the idealized gas of non-interacting particles are not realities but standard scientific models: abstractions created to strip away relation and make systems solvable.²⁵ In physics and chemistry, this practice of idealization — removing friction, noise, and interaction — became a cornerstone of modern science, allowing universal laws to be derived from controlled conditions.²⁶ Boyle’s air-pump experiments depended on creating an artificial vacuum; Galileo’s inclined planes depended on smoothing away friction. These were not attempts to replicate life in its fullness, but to design environments where relation could be bracketed out so prediction was possible²⁷

Abstraction is a powerful tool for prediction, but it’s not the description of life itself. Over time, however, as the logic of isolated and linear systems naturally extended outward — into physics, biology, economics, and governance — it became treated not only as a method but as if it were the truth of how reality behaves.²⁸ In practice, no system remains isolated forever.

Relation always reasserts itself, and recursion with novelty returns. The modern world was built — and continues to be shaped — through the lens of the mechanistic paradigm, structuring not only science but also what is valued, measured, and legitimized across society. We live inside the heritage of that spiral-recursion.

Awareness of spiral-recursive dynamics enables distinct design strategies: recognizing that the underlying structure of intention and circumstance shapes the input of the next cycle lets designers, stewards, and communities structure interventions with living systems — for resilience, regeneration, and adaptability — rather than designing against their dynamics, where institutional incentives allow. This recognition shows that what often feels like the “natural order” is often the product of a particular lens: not the truth of life itself but a way of seeing and building, and like any lens it can be shifted.²⁹

This is not to deny the practical efficacy of mechanistic models — they enabled extraordinary predictive and technological power — but rather to point out their limits when taken as literal descriptions of complex, relational life.

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Paradox I — The Invisible Pattern

Because recursion and spiral are everywhere, they are hard to see directly. Like fish in water, we inhabit the pattern so fully that it appears invisible. We breathe it as we breathe air, we live inside it as we live inside language, rarely noticing the structure that holds us. Only by naming it does it begin to appear.³⁰

This is why spiral recursion is both the most fundamental and the most overlooked dynamic. We rise and sleep each day, repeat tasks and gestures, learn and relearn, live inside cycles of seasons and generations. Each loop carries novelty, yet because it is repetition, it passes beneath awareness. We tend to notice the new, not the recursive structure that makes novelty possible.³¹

The paradox is that life’s organizing dynamic hides in plain sight. Because it is everywhere, it appears nowhere.

And because the dominant scientific paradigm has trained us to see linear sequences, isolatable events, and one-step causality, recursion is not only invisible through familiarity, but also obscured by the lenses we inherited.

To see reality as spiral-recursive is to recognize that time is not linear progress, causality is not one-step, and novelty does not emerge from nothing but from return with difference.³²

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Paradox II — Model and World

Science achieved its extraordinary predictive power not by capturing reality in its fullness, but by simplifying it: isolating systems, stripping away relation, and idealizing into forms that can be solved.

A perfect crystal, a frictionless pendulum, an idealized gas of non-interacting particles — these are abstractions, not things that exist in the wild.³³ Biology did the same with its “central dogma” of DNA → RNA → protein, ignoring feedback, epigenetics, and regulation.³⁴ Economics built on the model of homo economicus, the rational, isolated individual, and treated markets as if they were gases tending toward equilibrium.³⁵ These simplifications were methodologically necessary to calculate, but they were never reality itself.

The paradox is that once these models became the dominant measure of legitimacy, their influence extended beyond physics or biology into the very structure of society. Scholars have shown how scientific models do not only describe the world — they help construct it: defining what counts as real, what gets valued, and what institutions are built upon.³⁶

In this way, the logic of isolated, linear, equilibrium models has shaped not only laboratories but also economies, schools, and governments. Metrics, competition, and linear growth are not accidental features of modern life; they are the social expression of the models we have privileged.

The dominance of linearized models has built a world in their image. And that very world — strained, fragmented, competitive — is itself a recursive proof of the pattern: feedback loops repeating, difference accumulating, systems destabilizing and reorganizing.³⁷ In many social contexts, the models we privilege do not simply describe but help perform and construct realities.

To notice this is not to dismiss science, but to see that its models are one lens among many. Other forms of knowledge — those that hold relation, feedback, and coherence at their core — are equally necessary if we wish to build a world that reflects life’s true dynamics.

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Closing Reflection

Reading the world as spiral-recursive is a productive heuristic — a way to see continuity and novelty across domains — not a metaphysical proof. To recognize recursion and spiral as reality’s underlying dynamic is to see continuity where linear progress and isolated events once seemed to dominate.

The paradox is that this pattern is both invisible and formative: invisible because it is everywhere, formative because the models we inherit reproduce themselves in the worlds we build.

Awareness of this dynamic does not resolve the paradox, but it makes it available. It opens the possibility of shaping knowledge, institutions, and cultures that reflect the relational structures of life itself.

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1. Bruno Latour, We Have Never Been Modern (1991). (on the social construction and separation of disciplines)

2. Ilya Prigogine, Order Out of Chaos (with Isabelle Stengers) (1984). (dissipative structures, far-from-equilibrium systems); Stuart Kauffman, At Home in the Universe (1995). (self-organization in biology and complex systems); Fritjof Capra, The Web of Life (1996). (systems view / relational approaches).

3. Douglas Hofstadter, Gödel, Escher, Bach (1979) and I Am a Strange Loop (2007). (self-reference and strange loops); Gerald M. Edelman, The Remembered Present (1989); Antonio Damasio, Self Comes to Mind (2010). (neuroscientific models of reentrant / recursive processing in consciousness)

4. Erwin Schrödinger, What Is Life? (1944); Norbert Wiener, Cybernetics (1948); Ilya Prigogine & Isabelle Stengers, Order Out of Chaos (1984); Douglas Hofstadter, Gödel, Escher, Bach (1979).

5. Gregory Bateson, Steps to an Ecology of Mind (1972); Heinz von Foerster, Understanding Understanding (2003).

6. Benoît Mandelbrot, The Fractal Geometry of Nature (1982).

7. James Watson & Francis Crick, “Molecular Structure of Nucleic Acids” (1953); Evelyn Fox Keller, The Century of the Gene (2000).

8. Kandel ER, Schwartz JH & Jessell TM, Principles of Neural Science (5th ed., McGraw-Hill, 2013); Gerald M. Edelman, The Remembered Present (Basic Books, 1989); Antonio Damasio, Self Comes to Mind (Pantheon, 2010).

9. Antonio Damasio, Self Comes to Mind (2010); Gerald Edelman, The Remembered Present (1989).

10. Jean Piaget, The Origins of Intelligence in Children (1952); Deborah Britzman, Practice Makes Practice (1991); Stanislas Dehaene, How We Learn (2020) — on prediction/error-feedback in learning (Dehaene doesn’t use “spiral,” but the mechanism is feedback-driven).

11. Gerald M. Edelman, The Remembered Present (Basic Books, 1989); Antonio Damasio, Self Comes to Mind (Pantheon, 2010); Douglas Hofstadter, I Am a Strange Loop (Basic Books, 2007); Humberto Maturana & Francisco Varela, Autopoiesis and Cognition: The Realization of the Living (D. Reidel, 1980); Piaget (introductory work on equilibration and cognitive development). (Presented as interpretive, cross-disciplinary support for recursive and self-referential models of mind.)

12. Alfred J. Lotka, Elements of Physical Biology (1925); Vito Volterra, Leçons sur la Théorie Mathématique de la Lutte pour la Vie (1931); IPCC, Climate Change 2021: The Physical Science Basis (2021).

13. Technical sources (selected): training loops and gradient-based optimization — Goodfellow, Bengio & Courville, Deep Learning (MIT Press, 2016); backpropagation as the practical iterative learning algorithm — Rumelhart, Hinton & Williams, “Learning representations by back-propagating errors,” Nature 323 (1986): 533–536; architecturally recurrent networks (internal state across time: RNNs / LSTMs) — Hochreiter & Schmidhuber, “Long Short-Term Memory,” Neural Computation 9, no. 8 (1997): 1735–1780; transformer / attention architectures (dispense with layer-level recurrence) — Vaswani et al., “Attention Is All You Need,” NeurIPS (2017); autoregressive use of feedforward/attention models (practical demonstration in large language models) — Radford et al., “Language Models are Unsupervised Multitask Learners” (OpenAI, 2019).

14. Elizabeth Wilson, Adorned in Dreams: Fashion and Modernity (2003). (on fashion cycles and reinvention)

15. For interpretive background on Virgil Abloh’s quotation, archival referencing, and cross-sector collaborations see the exhibition catalog Virgil Abloh: Figures of Speech (Museum of Contemporary Art Chicago, 2019) and related exhibition materials. For broader context on fashion’s cyclical and remixing practices, see Elizabeth Wilson, Adorned in Dreams: Fashion and Modernity (2003). (Presented here as an illustrative reading rather than a claim about Abloh’s private motives.)

16. Christopher Alexander, Sara Ishikawa & Murray Silverstein, A Pattern Language: Towns, Buildings, Construction (Oxford University Press, 1977); Christopher Alexander, The Timeless Way of Building (Oxford University Press). (On pattern language and iterative, context-sensitive design.); Alejandro Aravena / Elemental — on incremental housing and the “half-house” strategy (Quinta Monroy): see Elemental project documentation and reporting (e.g., coverage on ArchDaily and architecture press). Also see John F. C. Turner, Housing By People: Towards Autonomy in Building Environments (1976) for background on incremental / self-help housing traditions. (Illustrative examples of social incremental extension.); Lydia Kallipoliti, The Architecture of Closed Worlds (Lars Müller Publishers, 2018). (History and design research on sealed, self-sustaining prototypes and iterative prototyping in architecture.)

17. Mircea Eliade, The Myth of the Eternal Return (1949). (on mythic and archetypal cycles)

18. Duncan Kennedy, A Critique of Adjudication (1997). (on legal precedent as recursive)

19. Joseph Schumpeter, Business Cycles (1939). (on economic expansion/contraction cycles)

20. Donella Meadows, Thinking in Systems (2008); Stuart Kauffman, At Home in the Universe (1995).

21. For grounding in descent-with-modification and the population-level processes that produce new forms, see Charles Darwin, On the Origin of Species (1859); for modern, accessible treatments of evolutionary mechanisms and speciation, see Douglas J. Futuyma, Evolution (textbook, contemporary edition) or Jerry A. Coyne, Why Evolution Is True (2009); for a detailed synthesis of speciation processes see Jerry A. Coyne & H. Allen Orr, Speciation (2004). For important complications — mechanisms that complicate a simple “lineage-only” picture, especially in microbes and some plants — see reviews on horizontal gene transfer (Soucy S. M., Huang J. & Gogarten J. P., “Horizontal gene transfer: building the web of life,” Nature Reviews Genetics 16 (2015): 472–482) and on polyploidy as a speciation route (Otto S. P. & Whitton J., review in Trends in Ecology & Evolution, 2000). (These works are cited to support the structural claim that evolutionary novelty accumulates across reproductive cycles; the caveat about alternative mechanisms acknowledges important exceptions and complexities.)

22. Alexandre Koyré, From the Closed World to the Infinite Universe (1957); Stephen Gaukroger, The Emergence of a Scientific Culture (2006).

23. Jonathan Israel, Radical Enlightenment (2001); Lorraine Daston & Peter Galison, Objectivity (2007).

24. Carolyn Merchant, The Death of Nature (1980); Silvia Federici, Caliban and the Witch (2004).

25. Steven Strogatz, Nonlinear Dynamics and Chaos (2015); Ilya Prigogine & Isabelle Stengers, Order Out of Chaos (1984); Lorraine Daston & Peter Galison, Objectivity (2007); Steven Shapin & Simon Schaffer, Leviathan and the Air-Pump (1985).
(on standard scientific models like crystals, pendulums, ideal gases, and the history of idealization as method.)

26. Lorraine Daston & Peter Galison, Objectivity (2007).
(on idealization, abstraction, and scientific universals.)

27. Steven Shapin & Simon Schaffer, Leviathan and the Air-Pump (1985).
(on Boyle’s artificial vacuum, Galileo’s inclined planes, and constructing environments of isolation for prediction.)

28. Bruno Latour, Science in Action (1987); Sheila Jasanoff, States of Knowledge (2004).
(on models extending outward, shaping society, and becoming treated as truth.)

29. See foundational and applied literatures showing how feedback-aware, regenerative design produces different outcomes than reductionist approaches: C. S. Holling, “Resilience and Stability of Ecological Systems,” Annual Review of Ecology and Systematics 4 (1973): 1–23 (resilience theory); Carl Walters, Adaptive Management of Renewable Resources (Macmillan, 1986) (iterative, learning-based management); Bill Mollison, Permaculture: A Designer’s Manual (Tagari Publications, 1988) and Miguel A. Altieri, Agroecology: The Science of Sustainable Agriculture (1995) (permaculture/regenerative agriculture practice); Janine Benyus, Biomimicry: Innovation Inspired by Nature (William Morrow, 1997) (biologically inspired design principles); Donella Meadows, Thinking in Systems: A Primer (Chelsea Green, 2008) (systems leverage and feedback); John Tillman Lyle, Regenerative Design for Sustainable Development (Wiley, 1994) (applied regenerative design/landscape architecture). These works document theoretical foundations and practical case studies where working with living processes increases resilience, adaptability, and regenerative outcomes.

30. James J. Gibson, The Ecological Approach to Visual Perception (1979); Donella Meadows, Thinking in Systems (2008); Tim Ingold, Being Alive: Essays on Movement, Knowledge and Description (2011).
(on invisible background structures that shape perception and life.)

31. C.G. Jung, The Archetypes and the Collective Unconscious (1959); Mircea Eliade, The Myth of the Eternal Return (1949).
(on cyclical/recursive patterns of repetition and novelty in psychological and mythic life.)

32. Thomas S. Kuhn, The Structure of Scientific Revolutions (1962); Evelyn Fox Keller, Reflections on Gender and Science (1985).
(on the dominance of linear/causal paradigms, how scientific lenses obscure recursive dynamics.)

33. Steven Strogatz, Nonlinear Dynamics and Chaos (2015); Ilya Prigogine & Isabelle Stengers, Order Out of Chaos (1984).
(on idealized models — perfect crystals, frictionless pendulums, gases — as abstractions, not real phenomena.)

34. Evelyn Fox Keller, The Century of the Gene (2000); Denis Noble, The Music of Life: Biology Beyond the Genome (2006).
(on critiques of the “central dogma” and importance of feedback, epigenetics, regulation.)

35. Amartya Sen, On Ethics and Economics (1987); Mary S. Morgan, The World in the Model: How Economists Work and Think (2012).
(on homo economicus and equilibrium models as abstractions shaping economics.)

36. Ian Hacking, Representing and Intervening (1983); Sheila Jasanoff, States of Knowledge (2004).
(on scientific models as world-building, defining what counts as real and legitimate.)

37. Thomas S. Kuhn, The Structure of Scientific Revolutions (1962); Bruno Latour, We Have Never Been Modern (1991); Donna Haraway, “Situated Knowledges” (1988); Karen Barad, Meeting the Universe Halfway (2007).
(on how scientific models are not neutral but situated, relational, and world-making; dominant frameworks shape both perception and material reality, yet can be reconfigured.)