Physna is a geometric deep-learning and 3D search company that searches, compares, and analyzes 3D models. ... Physna may be growing as it is associated with the 3D design file-sharing platform Thangs, which is mentioned in the context of transforming the 3D design file-sharing industry.

Physna - Crunchbase Company Profile & Funding

Physna | 2,737 followers on LinkedIn. The world&#x27;s leading 3D AI and geometric search platform, and owner of Thangs.com | Unlocking the value of your 3D models is what Physna is all about. Using proprietary algorithms and advanced geometric deep learning technology, Physna codifies 3D models ...

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Physna Company Profile: Valuation & Investors | PitchBook

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Physna

After two years of testing, Physna is close to signing a licensing deal that could put its software to work in factories all over the globe. If it all pans out as expected, the company will reach $100 million in annual revenue in the next three to five years and be worth over $1 billion.

Physna could soon be a $1 billion software company

Find company research, competitor information, contact details &amp; financial data for Physna Inc. of Cincinnati, OH. Get the latest business insights from Dun &amp; Bradstreet.

Physna Inc. Company Profile | Cincinnati, OH | Competitors, ...

View Physna (physna.com) location in Ohio, United States , revenue, industry and description. Find related and similar companies as well as employees by title and much more.

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The funding was led by venture capital firm Tiger Global with participation of Google Ventures and Sequoia Capital. It brings the intelligent 3D platform&#x27;s total financing to $86 million.

3D Geometric Search Company Physna Raises $56 Million In Series ...

Kvamme is co-founder and partner at Drive Capital, a leading investment firm focused on funding innovative technology companies in the Midwest. Prior to establishing Drive Capital in 2012, he was a general partner at Sequoia Capital in Menlo Park, California, where he led investments in top ...

About – Physna

Physna has 28 total employees and 3 sales reps that carry a quota. They have 30 customers an engineering team of 18 and a marketing team of 2 ... Forbes 30 Under 30 winner. Successful serial entrepreneur in B2B technology development and SaaS companies. Law degree from University of Heidelberg ...

Physna revenue is $5.5M and more than 30 paying Customers

Paul Powers co-founded Physna in late 2015, unaware that this company would one day revolutionize 3D technology in CAD design, engineering design and quality control throughout manufacturing and…

Startup Spotlight Q&A: Physna. Paul Powers co-founded Physna in ...

Find out what it&#x27;s like to work at Physna. See what kind of people work at Physna, career paths working at Physna, company culture, salaries, employee political affiliation, and more. Browse 2 Jobs at Physna.

Working At Physna: Company Overview and Culture - Zippia

/PRNewswire/ -- Physna®, the geometric deep-learning and 3D search solutions company, today announced it raised $20M in Series B funding. The round was led by...

Sequoia Leads $20M Series B in Physna, an Ohio-Based Geometric ...

Physna posted a video &quot;Physna Overview Series | Getting Started with Projects and Folders&quot; on YOUTUBE ... Discover new companies using five search parameters, including sector, location, public/private company status, employee count, and annual revenue.

Physna’s Competitors, Revenue, Number of Employees, Funding, ...

Physna, Inc. is a privately-held geometric deep-learning and 3D search company that searches, compares, and analyzes 3D models. Physna, Inc. was founded in 2015 by Glenn Warner and Paul Powers. Physna, Inc. headquarters are located in Cincinnati, Ohio.

Physna, Inc. Company Profile: Financials, Valuation, and Growth ...

Physna has 5 employees at their 1 location and $84.9 m in total funding,. See insights on Physna including office locations, competitors, revenue, financials, executives, subsidiaries and more at Craft.

Physna Company Profile - Office Locations, Competitors, Revenue, ...

Physna has raised $56 million in funding led by Tiger Global, with participation from GV and Sequoia. The Ohio-based company is the developer of a 3D design platform intended to help procurement and engineering teams measure operational efficiencies. Physna was valued at $100 million in January, ...

Physna: Revenue, Competitors, Alternatives

That’s a $1.1 billion venture ... development agency, JobsOhio, for the Kasich administration. Physna is hoping Kvamme’s experience as an early investor in Google and LinkedIn will help the three-year-old company become the world’s first search engine for 3D mode...

Software startup Physna LLC to create dozens of jobs with $6.9M ...

Typical contracts for the company’s software range from $25,000 to $50,000 for enterprise customers, but the software that powers Physna’s product is more than just a single application, according to Powers.

Three-dimensional search engine Physna wants to be the Google of ...

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Aspects of Thermal QCD Phenomenology at Intermediate Gauge/'t Hooft Coupling: Bibliography

Scientific American, Vol. XXXVII.—No. 2. [New Series.], July 14, 1877 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . CURIOUS CARNIVOROUS PLANTS. here CURIOUS CARNIVOROUS PLANTS. The is one of the most curious of that wonderful series of carnivorous plants which at the present time are engaging the closest scrutiny of naturalists. It is a true trap in one sense—inasmuch as it captures the victim which ventures near it; but it relies on little or no mechanical means for securing its prey, but stupefies the living insect by its odor. The flower is horn-shaped, about 11 inches in length, with an opening some 5 inches in diameter. The color within is a dull dark violet, while the interior of the spathe is lined with black, hooked bristles, the whole appearance of the flower being thoroughly repulsive. The illustrations herewith presented, Figs. 1 and 2, represent it at one third its natural size, Fig. 2 showing a section of the flower. It is not certain what attracts the insects, which are usually of the species known as the meat fly and the common house fly. They do not seem to seek for the small quantity of nectar concealed, and yet they cluster about the fatal opening, as if drawn by some overpowering fascination. Overcome by lethargy, they fall inert upon the flower, are lightly held by the bristles, and finally die asphyxiated by the carbonic acid which the plant disengages in large quantities during its inflorescence. Strange as is the action of the , the method whereby the takes its prey is even more wonderful. To illustrate on a magnified scale, let the reader imagine a surface thickly covered with strong iron posts, on the sides of which are numerous keen barbs pointing downward. Then between these posts, suppose that jars overflowing with honey are placed. An elephant, let it be imagined, attracted by the profusion of sweetness, inserts his trunk between the posts and finds easy access to the honey. But while he can force his proboscis downward past the barbs turned in that direction, when he attempts to withdraw it he finds the keen points catch in the flesh, and render it impossible to do so. A terrible struggle follows, the unfortunate animal twisting and writhing in every direction, until finally by an Herculean effort the head is torn from the body, and the latter becomes digested by some potent gastric juice, exuding from the colossal organism of which the trap forms but a portion. Of course this is vastly exaggerated, and it would puzzle an elephant to pull his own head off; but if for the post studded trap, we substitute the surface of a flower, and if we replace the elephant by a fly, we shall have conceived an accurate picture of what takes place in the peculiar receptacle with which Nature has provided the . This is very beautifully shown in Fig. 3; and at A, in same figure, is represented the barbed bristles grasping the highly magnified proboscis of the fly. Between the barbed bristles are mushroom-shaped projections, from the summits of which a viscous nectar exudes. This is the honey bait which induces the insect to insert his trunk between the fatal barbs. There is still another plant, , which captures butterflies by grasping the proboscis. The construction of the flower is quite complicated, so that the insects are compelled to insert their trunks through a narrow and winding passage in order to reach the nectar. The organ then necessarily comes in contact with an adhesive substance, which prevents its removal. arum Dracunculus arum mentzelia mentzelia ornata physianthus albens The , Fig. 4, is another plant trap, which catches no flies nor possesses any such wonderfully adapted devices as the plants already described. It simply has its branches covered with double barbed bristles of great strength which attach themselves to anything brought in contact with them. The bristles are strong enough to hold lizards, as represented by our engraving, the points inserting themselves in the interstices of the scaly covering of the reptile. Of course the lizard thus held starves to death, and small birds often follow a like fate. We are indebted to for the illustrations. Gronovia scandens La Nature About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2012). Scientific American, Vol. XXXVII. —No. 2. [New Series.], July 14, 1877. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/38481/pg38481-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

CURIOUS CARNIVOROUS PLANTS

Twenty Thousand Leagues under the Sea by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . A WALK ON THE BOTTOM OF THE SEA here CHAPTER XV: A WALK ON THE BOTTOM OF THE SEA This cell was, to speak correctly, the arsenal and wardrobe of the . A dozen diving apparatuses hung from the partition, waiting our use. Nautilus Ned Land, on seeing them, showed evident repugnance to dress himself in one. “But, my worthy Ned, the forests of the Island of Crespo are nothing but submarine forests.” “Good!” said the disappointed harpooner, who saw his dreams of fresh meat fade away. “And you, M. Aronnax, are you going to dress yourself in those clothes?” “There is no alternative, Master Ned.” “As you please, sir,” replied the harpooner, shrugging his shoulders; “but as for me, unless I am forced, I will never get into one.” “No one will force you, Master Ned,” said Captain Nemo. “Is Conseil going to risk it?” asked Ned. “I follow my master wherever he goes,” replied Conseil. At the Captain’s call two of the ship’s crew came to help us to dress in these heavy and impervious clothes, made of india-rubber without seam, and constructed expressly to resist considerable pressure. One would have thought it a suit of armour, both supple and resisting. This suit formed trousers and waistcoat. The trousers were finished off with thick boots, weighted with heavy leaden soles. The texture of the waistcoat was held together by bands of copper, which crossed the chest, protecting it from the great pressure of the water, and leaving the lungs free to act; the sleeves ended in gloves, which in no way restrained the movement of the hands. There was a vast difference noticeable between these consummate apparatuses and the old cork breastplates, jackets, and other contrivances in vogue during the eighteenth century. Captain Nemo and one of his companions (a sort of Hercules, who must have possessed great strength), Conseil, and myself, were soon enveloped in the dresses. There remained nothing more to be done but to enclose our heads in the metal box. But before proceeding to this operation, I asked the Captain’s permission to examine the guns we were to carry. One of the men gave me a simple gun, the butt end of which, made of steel, hollow in the centre, was rather large. It served as a reservoir for compressed air, which a valve, worked by a spring, allowed to escape into a metal tube. A box of projectiles, in a groove in the thickness of the butt end, contained about twenty of these electric balls, which, by means of a spring, were forced into the barrel of the gun. As soon as one shot was fired, another was ready. Nautilus “Captain Nemo,” said I, “this arm is perfect, and easily handled: I only ask to be allowed to try it. But how shall we gain the bottom of the sea?” “At this moment, Professor, the is stranded in five fathoms, and we have nothing to do but to start.” Nautilus “But how shall we get off?” “You shall see.” Captain Nemo thrust his head into the helmet, Conseil and I did the same, not without hearing an ironical “Good sport!” from the Canadian. The upper part of our dress terminated in a copper collar upon which was screwed the metal helmet. Three holes, protected by thick glass, allowed us to see in all directions, by simply turning our head in the interior of the head-dress. As soon as it was in position, the Rouquayrol apparatus on our backs began to act; and, for my part, I could breathe with ease. With the Ruhmkorff lamp hanging from my belt, and the gun in my hand, I was ready to set out. But to speak the truth, imprisoned in these heavy garments, and glued to the deck by my leaden soles, it was impossible for me to take a step. But this state of things was provided for. I felt myself being pushed into a little room contiguous to the wardrobe-room. My companions followed, towed along in the same way. I heard a water-tight door, furnished with stopper-plates, close upon us, and we were wrapped in profound darkness. After some minutes, a loud hissing was heard. I felt the cold mount from my feet to my chest. Evidently from some part of the vessel they had, by means of a tap, given entrance to the water, which was invading us, and with which the room was soon filled. A second door cut in the side of the then opened. We saw a faint light. In another instant our feet trod the bottom of the sea. Nautilus And now, how can I retrace the impression left upon me by that walk under the waters? Words are impotent to relate such wonders! Captain Nemo walked in front, his companion followed some steps behind. Conseil and I remained near each other, as if an exchange of words had been possible through our metallic cases. I no longer felt the weight of my clothing, or of my shoes, of my reservoir of air, or my thick helmet, in the midst of which my head rattled like an almond in its shell. The light, which lit the soil thirty feet below the surface of the ocean, astonished me by its power. The solar rays shone through the watery mass easily, and dissipated all colour, and I clearly distinguished objects at a distance of a hundred and fifty yards. Beyond that the tints darkened into fine gradations of ultramarine, and faded into vague obscurity. Truly this water which surrounded me was but another air denser than the terrestrial atmosphere, but almost as transparent. Above me was the calm surface of the sea. We were walking on fine, even sand, not wrinkled, as on a flat shore, which retains the impression of the billows. This dazzling carpet, really a reflector, repelled the rays of the sun with wonderful intensity, which accounted for the vibration which penetrated every atom of liquid. Shall I be believed when I say that, at the depth of thirty feet, I could see as if I was in broad daylight? For a quarter of an hour I trod on this sand, sown with the impalpable dust of shells. The hull of the , resembling a long shoal, disappeared by degrees; but its lantern, when darkness should overtake us in the waters, would help to guide us on board by its distinct rays. Nautilus Soon forms of objects outlined in the distance were discernible. I recognised magnificent rocks, hung with a tapestry of zoophytes of the most beautiful kind, and I was at first struck by the peculiar effect of this medium. It was then ten in the morning; the rays of the sun struck the surface of the waves at rather an oblique angle, and at the touch of their light, decomposed by refraction as through a prism, flowers, rocks, plants, shells, and polypi were shaded at the edges by the seven solar colours. It was marvellous, a feast for the eyes, this complication of coloured tints, a perfect kaleidoscope of green, yellow, orange, violet, indigo, and blue; in one word, the whole palette of an enthusiastic colourist! Why could I not communicate to Conseil the lively sensations which were mounting to my brain, and rival him in expressions of admiration? For aught I knew, Captain Nemo and his companion might be able to exchange thoughts by means of signs previously agreed upon. So, for want of better, I talked to myself; I declaimed in the copper box which covered my head, thereby expending more air in vain words than was perhaps expedient. Various kinds of isis, clusters of pure tuft-coral, prickly fungi, and anemones formed a brilliant garden of flowers, enamelled with porphitæ, decked with their collarettes of blue tentacles, sea-stars studding the sandy bottom, together with asterophytons like fine lace embroidered by the hands of naïads, whose festoons were waved by the gentle undulations caused by our walk. It was a real grief to me to crush under my feet the brilliant specimens of molluscs which strewed the ground by thousands, of hammer-heads, donaciae (veritable bounding shells), of staircases, and red helmet-shells, angel-wings, and many others produced by this inexhaustible ocean. But we were bound to walk, so we went on, whilst above our heads waved shoals of physalides leaving their tentacles to float in their train, medusæ whose umbrellas of opal or rose-pink, escalloped with a band of blue, sheltered us from the rays of the sun and fiery pelagiæ, which, in the darkness, would have strewn our path with phosphorescent light. All these wonders I saw in the space of a quarter of a mile, scarcely stopping, and following Captain Nemo, who beckoned me on by signs. Soon the nature of the soil changed; to the sandy plain succeeded an extent of slimy mud, which the Americans call “ooze,” composed of equal parts of silicious and calcareous shells. We then travelled over a plain of sea-weed of wild and luxuriant vegetation. This sward was of close texture, and soft to the feet, and rivalled the softest carpet woven by the hand of man. But whilst verdure was spread at our feet, it did not abandon our heads. A light network of marine plants, of that inexhaustible family of sea-weeds of which more than two thousand kinds are known, grew on the surface of the water. I saw long ribbons of fucus floating, some globular, others tuberous; laurenciæ and cladostephi of most delicate foliage, and some rhodomeniæ palmatæ, resembling the fan of a cactus. I noticed that the green plants kept nearer the top of the sea, whilst the red were at a greater depth, leaving to the black or brown hydrophytes the care of forming gardens and parterres in the remote beds of the ocean. We had quitted the about an hour and a half. It was near noon; I knew by the perpendicularity of the sun’s rays, which were no longer refracted. The magical colours disappeared by degrees, and the shades of emerald and sapphire were effaced. We walked with a regular step, which rang upon the ground with astonishing intensity; the slightest noise was transmitted with a quickness to which the ear is unaccustomed on the earth; indeed, water is a better conductor of sound than air, in the ratio of four to one. At this period the earth sloped downwards; the light took a uniform tint. We were at a depth of a hundred and five yards and twenty inches, undergoing a pressure of six atmospheres. Nautilus At this depth I could still see the rays of the sun, though feebly; to their intense brilliancy had succeeded a reddish twilight, the lowest state between day and night; but we could still see well enough; it was not necessary to resort to the Ruhmkorff apparatus as yet. At this moment Captain Nemo stopped; he waited till I joined him, and then pointed to an obscure mass, looming in the shadow, at a short distance. “It is the forest of the Island of Crespo,” thought I;—and I was not mistaken. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (1994). Twenty Thousand Leagues under the Sea. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/files/164/164-h/164-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

A WALK ON THE BOTTOM OF THE SEA

I shall keep caddis worms

THE POND

Natural computing is a field of study that involves the use of natural systems, such as biological organisms or processes, to solve complex computational problems. Slime mold is one such natural system that has gained attention in recent years for its ability to solve optimization and decision-making problems in a novel and efficient way. Slime mold is a simple organism that belongs to the group of protists, and it has the unique ability to move and grow towards food sources in a way that appears intelligent, despite lacking a central nervous system. Researchers have studied the behavior of slime mold and have found that it can solve a range of problems, from finding the shortest path between two points to optimizing transportation networks and even designing efficient computer algorithms. The Physarum Optimization Algorithm is a computational model that simulates the behavior of slime mold to solve optimization problems. It is based on the observation that slime mold will spread out and grow towards food sources in the most efficient way possible, following the shortest path to connect different parts of its body. The algorithm works by representing a problem as a network and simulating the growth and propagation of slime mold agents along the network. The concentration of slime at each node indicates the optimal path between two points on the network. While this piqued your curiosity regarding how an organism can lay paths to solving human problems, I believe what is necessary to grab here is the understanding of natural computing and how slime mold can be used as a model organism for solving complex problems. I do believe this in itself sets the stage for understanding the basic principles of the Physarum Optimization Algorithm and its potential applications. To illustrate the Physarum Optimization Algorithm, I will use a straightforward example in this post, such as " ." As I alluded to previously, I would have to demonstrate how the algorithm works by spreading/propagating plasmodium agents throughout the network to replicate the activity of slime mold and demonstrating how the concentration of slime at each node shows the shortest path between two points. The algorithm works by simulating the behavior of slime mold to find the optimal path between two points. finding the shortest path between two points on a network Let’s take a look how Physarum Optimization Algorithm work: We start by initializing the network and placing plasmodium agents at each node. We then simulate the behavior of the agents by moving them along the network, based on a set of rules that mimic the behavior of slime mold. For example, the agents might move towards areas of high nutrient concentration (i.e., where other agents have traveled before) and avoid areas of low concentration. As the agents move along the network, they leave behind a trail of slime that indicates the concentration of slime mold at each node. We will then use the concentration of slime at each node to determine the shortest path between two nodes on the network. The path with the highest concentration of slime represents the optimal path between the two nodes. By propagating plasmodium agents along the network and simulating the behavior of slime mold, we can find the optimal path between two nodes on the network. The concentration of slime at each node indicates the shortest path between the two points. This method is not only efficient but also provides a biologically-inspired approach to optimization and decision-making problems. There are several areas slime molds can be used in decision-making. Some of the use cases is to use of the Physarum Optimization Algorithm to solve optimization problems in engineering and design. By representing the design problem as a network and simulating the behavior of slime mold, the algorithm can find the optimal configuration of components or materials to minimize cost or maximize performance. One of the many advantages of the Physarum optimization algorithm is its ability to solve logistics problems efficiently, leading to cost reduction for logistics players. The Physarum Optimization Research Group conducted a comprehensive review on the optimization applications of Physarum polycephalum, a type of slime mold that has been used as a model organism for natural computing and optimization (Chu et al., 2021). In their review published in Swarm and Evolutionary Computation, the authors provide an overview of how Physarum polycephalum has been utilized to solve various optimization problems, including routing problems in communication networks, clustering problems in data mining, and optimization problems in engineering and design. The potential of Physarum polycephalum for future applications in areas such as robotics and swarm intelligence is also highlighted. This review serves as a valuable resource for researchers and practitioners interested in the use of slime mold-inspired algorithms for optimization and decision-making problems. Now, let’s jump on it using python to exemplify slime molds actions as listed earlier. First, we will import dependencies to carry out this Physarum Optimization Algorithm: # Import dependencies
import numpy as np
import scipy.sparse as sparse
import networkx as nx
from queue import PriorityQueue NumPy for numerical computations, SciPy Sparse for sparse matrix representation, NetworkX for graph creation, and PriorityQueue for the implementation of the Dijkstra algorithm Next, we will initialize graph through and add edges to it: networkx # Initialize the network
G = nx.Graph()
G.add_edges_from([(0,1), (1,2), (2,3), (3,0)]) This initializes an undirected graph and adds edges between four nodes , , , and . G (0,1) (1,2) (2,3) (3,0) Then, we can initialize the parameter, such as plasmodium agents, rate of slime evaporation (this is a natural attitude of slime molds), concentration of slime at each node, etc: # Initialize the parameters
N = 100  # number of plasmodium agents. For test purposes, you can lower this number to 10 or thereabout
evaporation_rate = 0.1  # rate of slime evaporation
slime_concentration = sparse.csr_matrix((1, len(G.nodes)))  # concentration of slime at each node
plasmodium = np.zeros((len(G.nodes))) These lines/block initialize the parameters used in the simulation. is the number of plasmodium agents that will be simulated, is the rate at which the slime will evaporate, and is a sparse matrix that represents the concentration of slime at each node. N evaporation_rate slime_concentration Once we have initialized the parameters, I believe we can we can randomly generate the start node and the end node for the plasmodium agent to propagate between: start_node = np.random.choice(list(G.nodes))
end_node = np.random.choice(list(G.nodes)) This is as a result of wanting the plasmodium agent to propagate between start_node and end_node. Another possibility is to generate a static start_node and end_node, however, it will make our code less robust. So it is better we have randomness just to see the beauty of slime mode based algorithm. Now let’s propagate the plasmodium: queue = PriorityQueue()
queue.put((-plasmodium[start_node], start_node))
while not queue.empty():
    current_node = queue.get()[1]
    if current_node == end_node:
        break
    neighbors = list(G.neighbors(current_node))
    if len(neighbors) == 0:
        break
    for neighbor in neighbors:
        next_concentration = plasmodium[current_node] / len(neighbors)
        if next_concentration > slime_concentration[0, neighbor]:
            slime_concentration[0, neighbor] = next_concentration
            queue.put((-next_concentration, neighbor))
        plasmodium[neighbor] += plasmodium[current_node] / len(neighbors)
        
    # Update the slime concentration at each node
    slime_concentration *= (1 - evaporation_rate) This block propagate the plasmodium agent along the graph using a priority queue data structure. The queue is initialized with the start node and its corresponding plasmodium value. In the while loop, the current node is retrieved from the queue, and if it matches the end node, the loop is broken. The neighbors of the current node are retrieved and if there are no neighbors, the loop is broken. For each neighbor, the next slime concentration is calculated based on the current plasmodium concentration and the number of neighbors. If the next concentration is greater than the current slime concentration at the neighbor node, then the slime concentration is updated, and the neighbor is added to the queue with the next concentration value. The plasmodium concentration at the neighbor is also updated based on the current concentration and the number of neighbors. Under the same while..loop, we then updates the slime concentration at each node by multiplying it with the evaporation rate. At this point, we have done a great job with propagation. Knowing how slime molds work, it is certain you can never get O(1) time complexity in this algorithm. But you can make it better by having an optimized algorithmic complexities for this Physarum (Slime) Optimization Algorithm. You may have to look at the videos in the references to understand leads with slime molds and start thinking how better you will rewrite this code. We can now apply the method created thus far to determine the shortest path between two nodes depending on the concentration of slime, as we can do so by including the excerpt below: path = [start_node]
while path[-1] != end_node:
    neighbors = list(G.neighbors(path[-1]))
    next_node = neighbors[np.argmax(slime_concentration[0, neighbors])]
    path.append(next_node) These block finds the shortest path between the start node and the end node based on the slime concentration at each node. The current node is initialized with the start node, and while the current node is not equal to the end node, the neighbors of the current node are retrieved, and the next node is selected based on the maximum slime concentration among the neighbors. The next node is then added to the path, and the loop continues until the end node is reached. We can now output the shortest path using the function: print() # Print the shortest path
print(path) By output of the print statement, in the code we have written, for the first simulation, I got .  Don’t forget there is randomness in the start and end node, so we will always get differing outputs and that is indeed peculiar to slime molds. If we assume the start node is and the end node is . The list is initialized with the start node, and we continue to append the neighbor with the highest concentration of slime until we reach the end node. [2, 1, 0] 0 3 path In this case, the shortest path between node and node based on the slime concentration is . However, the actual path found by the algorithm in this specific run of the simulation happened to be . This is due to the randomness involved in the simulation - the plasmodium agent starts from a random node, and the algorithm selects random end nodes for each run. 0 3 [0, 1, 2, 3] [2, 1, 0] Overall, this code generates a network and initializes plasmodium agents to propagate along the network, updating the slime concentration at each node based on the agent's movement. The shortest path between two randomly generated nodes is then calculated based on the slime concentration at each node. This algorithm can be useful in modeling the movement of agents or particles in a network and finding the most efficient path between two points based on some criteria. You can find the complete repository on my github ( ). https://github.com/AfolabiOlaoluwa/physarum_optimization_algorithm After exploring the Python code that exemplifies how the slime mold algorithm works, we can conclude that natural computing and biological-inspired algorithms are powerful tools for . Slime molds, despite lacking a centralized nervous system, are able to solve complex decision-making problems by adapting their growth patterns in response to environmental stimuli. By understanding how slime molds work, we can develop new algorithms and computational models that mimic the behavior of these organisms and apply them to a wide range of real-world problems, such as urban planning, logistics, and transportation. solving complex optimization problems Moreover, the study of natural systems can also inspire us to develop more sustainable and efficient approaches to problem-solving. Slime molds, for example, are able to solve optimization problems with minimal resources and energy consumption, making them a potential source of inspiration for the development of more energy-efficient and eco-friendly computing technologies. In conclusion, the study of natural computing and biological-inspired algorithms not only allows us to solve complex problems more efficiently, but it also provides us with a new perspective on how to approach problem-solving in a more sustainable and nature-inspired way. Reference: Wired. "Mycologist Explains How a Slime Mold Can Solve Mazes." Online video clip. YouTube. YouTube, 5 April 2018. . https://www.youtube.com/watch?v=7YWbY7kWesI Biographic. (2016, June 7). Lens of Time: Slime Lapse. . . https://www.biographic.com/lens-of-time-slime-lapse/ https://youtu.be/olCEGsKWQ3c Chu, D., Ma, W., Yang, Z., Li, J., Deng, Y., & Cheong, K. H. (2021). A comprehensive review on the optimization applications of Physarum polycephalum. Swarm and Evolutionary Computation, 64, 100937. doi: . 10.1016/j.swevo.2021.100937 L. Liu, Y. Song, H. Zhang, H. Ma, and A. V. Vasilakos, "Physarum Optimization: A Biology-Inspired Algorithm for the Steiner Tree Problem in Networks," in IEEE Transactions on Computers, vol. 64, no. 3, pp. 818-832, March 2015, doi: . 10.1109/TC.2013.229 A Hagberg, D Schult, P Swart, in , G Varoquaux, T Vaught, J Millman (Eds.), pp. 11-15. Exploring Network Structure, Dynamics, and Function using NetworkX Proceedings of the 7th Python in Science conference (SciPy 2008)

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