Azra Games has defied the funding gods and raised $42.7 million to accelerate development of its Project Legends mobile role-playing game.

Azra Games raises $42.7M for next-generation mobile RPG with Web3 features | VentureBeat

Azra

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This rundown has been compiled using data from several reports and databases, including Pitchbook, Galaxy Research, CryptoRank, InnMind, and Cointelegraph. Major Highlights Funding Continues to Decline: The Crypto VC market is still facing headwinds as investors are cautious, with Q2 2023 attracting $2.32 billion in funding, slightly lower than the previous quarter. Mixed Valuations: The number of completed crypto VC deals increased in Q2, but pre-money valuations continued to decline, reaching $17.93 million, while the median crypto VC deal stood at $3 million in Q2 2023. Web3 Dominance: Web3 Gaming, NFTs, and the Metaverse are high-interest segments, this nascent crypto niche recorded the highest number of deals in Q2 2023. Crypto VC Landscape: Despite headwinds in 2023, the US remains the leading destination for venture capital funds in the digital asset industry, with over $2 billion invested this year. Crypto Winter Continues: 46% of respondents in a survey carried out by InnMind believe that the crypto winter will persist throughout 2023, with market recovery expected in 2024. Crypto VC Funding Overview The has continued to face headwinds this year, with more investors shying away from the digital asset industry. According to the latest data from , Q2 2023 attracted $2.32 billion in funding, down from the previous quarter, which was slightly higher at $2.6 billion. crypto VC landscape Pitchbook Valuations and Number of Deals Show Mixed Trends The number of completed crypto VC deals increased from 439 in Q1 to 456 in Q2. This is the first time the figure has gone up since the market peaked in Q1 2022 when over 1,200 deals were completed during that quarter. However, on the other hand, the pre-money valuation for deals in the digital asset industry has continued on a downtrend, reaching $17.93 million, while the median crypto VC deal stood at $3 million in Q2 2023. Top 5 Crypto VCs by Capital Invested in Q1 2023 According to a Q1 2023 from the InnMind Web3 startups and VC platform, the same players from last year are still dominating the action. Here are some of the VCs that have invested the most funds into crypto startups this year: report - Invested $136.2 million in Q1 funding 10 startups, including EigenLayer, Term Labs, Avalon Corp, Chaos Labs, MSafe, Azra Games, Alongside, Architect, Obol Labs, and Alkimiya. Coinbase Ventures - Invested $131.5 million in Q1, some of the projects funded include Voldex, Alongside, Towns, Believer, Capsule, and CCP Games. a16z - Invested $107.8 million in Q1 to fund 9 projects: Mask, YGG, Inverse Finance, Beldex, Conflux, Tonstarter, Radix, So-Col, and Alchemy Pay. DWF Labs - Invested $75 million in Q1 into 6 projects namely Alkimiya, Caldera, Monad, Kaito, OP3N, and Econia Labs. DragonFly Capital - Invested $53.5 million in Q1 funding 10 projects: MSafe, Quasar, Open Forest Protocol, Quantum Temple, Sleepagotchi, Cedro Finance, OrbLabs, Monad, 3RM and Strider.’ Shima Capital Crypto VC Fundraising is at its lowest since Q3 2020 Evidently, crypto VCs have not invested a lot of funds into the market this year. This is due to a sharp decline in the amount of newly injected capital and new funds. Data from Galaxy Asset Management reveals that the number of new funds in Q2 2023 was only 10, with the total amount raised falling below $1 billion. Source: Galaxy Asset Management As expected, the decline in fundraising amounts has affected the average fund size, which currently stands at $236 million compared to $343 million in 2022. Meanwhile, the median fund size has halved from $100 million to $50 million. High Ticket Crypto VC Funding Rounds in 2023 While 2023 might have started on a low note for both the global and crypto VC industry, there have been several notable raises this year: Blockstream, a company specializing in digital asset infrastructure, $125 million in January through a convertible note and secured a loan to fund its mining co-location services. secured In March, Ledger, the prominent crypto hardware wallet provider, successfully $109 million in a series C funding round extension. attracted Taurus, a digital asset infrastructure provider serving financial institutions in Europe, $65 million in a Series B capital raise with Credit Suisse leading the funding round. raised Scroll, a Layer 2 Ethereum network working on a zero-knowledge Ethereum Virtual Machine (zkEVM) scaling solution, a $50.0 million late-stage VC round in March from prominent investors, including Polychain Capital and Sequoia China. secured Funding Rounds by Investor and Category As for the number of funding rounds, data from CryptoRank shows that Polygon Fund has participated in 29 funding rounds this year, closely followed by DWF Labs and Coinbase Ventures, both at 28 rounds each. It is also noteworthy that most of the funds were allocated toward blockchain service and blockchain infrastructure projects. Seed Stage Rounds Accounted for the Lion’s Share Seed-stage funding rounds have attracted the most capital this year, accounting for 33.82% while pre-seed and series A raises stood at 8.97% and 7.5% respectively. Crypto VC Investments by Segment & Region , NFTs, and the are still an area of high interest, this segment of the crypto market recorded the highest number of deals in Q2 2023 followed closely by Lending, Investing, Exchange, and Trading companies. Web3 Gaming Metaverse However, when it comes to the amount of capital invested, it was vice versa: trading and investing companies carried the day, while Web3 infrastructures came in second. The US Leads in Crypto VC Raises and Funding Rounds Despite regulatory uncertainty, the US remains the leading destination for venture capital funds targeting the digital asset industry. This year alone, over $2.1 billion has been invested in crypto startups based in the US. Undisclosed locations follow at $1.2 billion, and in third place is the United Kingdom with $602.93 million. InnMind Survey: Web3 Most Potential Niche and Crypto Winter to End in 2024 A recent survey conducted by InnMind, involving 147 professional VCs in the digital asset industry, reveals that the majority of participants believe Web3 infrastructure startups hold the most upside potential. More interestingly, 46% of the respondents believe that the crypto winter or the period of 'nothingness' will continue for the rest of 2023, and it is unlikely that we will see the market bouncing back until next year. Closing Thoughts The crypto bear market has undoubtedly impacted the availability of venture capital over the past year. However, it is essential to highlight that there remains notable activity in value-based projects focusing on Web3, especially in the metaverse and NFT segments. As we look ahead, the outlook on whether substantial capital will flow into the digital asset industry this year remains uncertain. That said, there is a higher likelihood that many investors will remain cautious and stay on the sidelines for the rest of 2023. Also published here.

Crypto VC Landscape in 2023: Adapting Strategies Amidst the Winter

The Country of the Blind, And Other Stories, by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here. A VISION OF JUDGMENT A VISION OF JUDGMENT I. — Bru-a-a-a. I listened, not understanding. Wa-ra-ra-ra. "Good Lord!" said I, still only half awake. "What an infernal shindy!" Ra-ra-ra-ra-ra-ra-ra-ra-ra Ta-ra-rra-ra. "It's enough," said I, "to wake——" and stopped short. Where was I. — Ta-rra-rara—louder and louder. "It's either some new invention——" Toora-toora-toora! Deafening! "No," said I, speaking loud in order to hear myself. "That's the Last Trump." Tooo-rraa! II. — The last note jerked me out of my grave like a hooked minnow. I saw my monument (rather a mean little affair, and I wished I knew who'd done it), and the old elm tree and the sea view vanished like a puff of steam, and then all about me—a multitude no man could number, nations, tongues, kingdoms, peoples—children of all the ages, in an amphitheatral space as vast as the sky. And over against us, seated on a throne of dazzling white cloud, the Lord God and all the host of his angels. I recognised Azrael by his darkness and Michael by his sword, and the great angel who had blown the trumpet stood with the trumpet still half raised. III. — "Prompt," said the little man beside me. "Very prompt. Do you see the angel with the book?" He was ducking and craning his head about to see over and under and between the souls that crowded round us. "Everybody's here," he said. "Everybody. And now we shall know— "There's Darwin," he said, going off at a tangent. "He'll catch it! And there—you see?—that tall, important-looking man trying to catch the eye of the Lord God, that's the Duke. But there's a lot of people one doesn't know. "Oh! there's Priggles, the publisher. I have always wondered about printers' overs. Priggles was a clever man ... But we shall know now—even about him. "I shall hear all that. I shall get most of the fun before ... My letter's S." He drew the air in between his teeth. "Historical characters, too. See? That's Henry the Eighth. There'll be a good bit of evidence. Oh, damn! He's Tudor." He lowered his voice. "Notice this chap, just in front of us, all covered with hair. Paleolithic, you know. And there again—" But I did not heed him, because I was looking at the Lord God. IV. — "Is this all?" asked the Lord God. The angel at the book—it was one of countless volumes, like the British Museum Reading-room Catalogue, glanced at us and seemed to count us in the instant. "That's all," he said, and added: "It was, O God, a very little planet." The eyes of God surveyed us. "Let us begin," said the Lord God. V. — The angel opened the book and read a name. It was a name full of A's, and the echoes of it came back out of the uttermost parts of space. I did not catch it clearly, because the little man beside me said, in a sharp jerk, "What's that?" It sounded like "Ahab" to me; but it could not have been the Ahab of Scripture. Instantly a small black figure was lifted up to a puffy cloud at the very feet of God. It was a stiff little figure, dressed in rich outlandish robes and crowned, and it folded its arms and scowled. "Well?" said God, looking down at him. We were privileged to hear the reply, and indeed the acoustic properties of the place were marvellous. "I plead guilty," said the little figure. "Tell them what you have done," said the Lord God. "I was a king," said the little figure, "a great king, and I was lustful and proud and cruel. I made wars, I devastated countries, I built palaces, and the mortar was the blood of men. Hear, O God, the witnesses against me, calling to you for vengeance. Hundreds and thousands of witnesses." He waved his hands towards us. "And worse! I took a prophet—one of your prophets——" "One of my prophets," said the Lord God. "And because he would not bow to me, I tortured him for four days and nights, and in the end he died. I did more, O God, I blasphemed. I robbed you of your honours——" "Robbed me of my honours," said the Lord God. "I caused myself to be worshipped in your stead. No evil was there but I practised it; no cruelty wherewith I did not stain my soul. And at last you smote me, O God!" God raised his eyebrows slightly. "And I was slain in battle. And so I stand before you, meet for your nethermost Hell! Out of your greatness daring no lies, daring no pleas, but telling the truth of my iniquities before all mankind." He ceased. His face I saw distinctly, and it seemed to me white and terrible and proud and strangely noble. I thought of Milton's Satan. "Most of that is from the Obelisk," said the Recording Angel, finger on page. "It is," said the Tyrannous Man, with a faint touch of surprise. Then suddenly God bent forward and took this man in his hand, and held him up on his palm as if to see him better. He was just a little dark stroke in the middle of God's palm. "Did he do all this?" said the Lord God. The Recording Angel flattened his book with his hand. "In a way," said the Recording Angel, carelessly. Now when I looked again at the little man his face had changed in a very curious manner. He was looking at the Recording Angel with a strange apprehension in his eyes, and one hand fluttered to his mouth. Just the movement of a muscle or so, and all that dignity of defiance was gone. "Read," said the Lord God. And the angel read, explaining very carefully and fully all the wickedness of the Wicked Man. It was quite an intellectual treat.—A little "daring" in places, I thought, but of course Heaven has its privileges... VI. — Everybody was laughing. Even the prophet of the Lord whom the Wicked Man had tortured had a smile on his face. The Wicked Man was really such a preposterous little fellow. "And then," read the Recording Angel, with a smile that set us all agog, "one day, when he was a little irascible from over-eating, he—" "Oh, not that," cried the Wicked Man, "nobody knew of that. "It didn't happen," screamed the Wicked Man. "I was bad—I was really bad. Frequently bad, but there was nothing so silly—so absolutely silly—" The angel went on reading. "O God!" cried the Wicked Man. "Don't let them know that! I'll repent! I'll apologise..." The Wicked Man on God's hand began to dance and weep. Suddenly shame overcame him. He made a wild rush to jump off the ball of God's little finger, but God stopped him by a dexterous turn of the wrist. Then he made a rush for the gap between hand and thumb, but the thumb closed. And all the while the angel went on reading—reading. The Wicked Man rushed to and fro across God's palm, and then suddenly turned about and fled up the sleeve of God. I expected God would turn him out, but the mercy of God is infinite. The Recording Angel paused. "Eh?" said the Recording Angel. "Next," said God, and before the Recording Angel could call the name a hairy creature in filthy rags stood upon God's palm. VII. — "Has God got Hell up his sleeve then?" said the little man beside me. "Is there a Hell?" I asked. "If you notice," he said—he peered between the feet of the great angels— "there's no particular indication of a Celestial City." "'Ssh!" said a little woman near us, scowling. "Hear this blessed Saint!" VIII. — "He was Lord of the Earth, but I was the prophet of the God of Heaven," cried the Saint, "and all the people marvelled at the sign. For I, O God, knew of the glories of thy Paradise. No pain, no hardship, gashing with knives, splinters thrust under my nails, strips of flesh flayed off, all for the glory and honour of God." God smiled. "And at last I went, I in my rags and sores, smelling of my holy discomforts——" Gabriel laughed abruptly. "And lay outside his gates, as a sign, as a wonder——" "As a perfect nuisance," said the Recording Angel, and began to read, heedless of the fact that the saint was still speaking of the gloriously unpleasant things he had done that Paradise might be his. And behold, in that book the record of the Saint also was a revelation, a marvel. It seemed not ten seconds before the Saint also was rushing to and fro over the great palm of God. Not ten seconds! And at last he also shrieked beneath that pitiless and cynical exposition, and fled also, even as the Wicked Man had fled, into the shadow of the sleeve. And it was permitted us to see into the shadow of the sleeve. And the two sat side by side, stark of all delusions, in the shadow of the robe of God's charity, like brothers. And thither also I fled in my turn. IX. — "And now," said God, as he shook us out of his sleeve upon the planet he had given us to live upon, the planet that whirled about green Sirius for a sun, "now that you understand me and each other a little better,...try again." Then he and his great angels turned themselves about and suddenly had vanished... The Throne had vanished. All about me was a beautiful land, more beautiful than any I had ever seen before—waste, austere, and wonderful; and all about me were the enlightened souls of men in new clean bodies... 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. H. G. Wells (2004). The Country of the Blind, And Other Stories. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/11870/11870-h/11870-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 www.gutenberg.org , located at . https://www.gutenberg.org/policy/license.html

A VISION OF JUDGMENT

The Professor Layton franchise is one of the most underrated puzzle game series of all time. The series consists of seven games, a tv show, a film and one spin-off game. Professor Layton is a puzzle adventure game series first released on the Nintendo DS and later on other Nintendo handheld platforms such as the Nintendo 3DS and the Nintendo Switch. The series follows professor Hershal Layton who seeks to uncover the mysteries of the world by solving puzzles. The core gameplay of the franchise consists of puzzle-solving alongside narrative sequences. As highlighted, there are seven games in the series; this article will highlight the Professor Layton games in chronological order. The Professor Layton Games in Chronological Order Professor Layton and the Last Spectre Professor Layton and the Miracle Mask Professor Layton and the Azran Legacy Professor Layton and the Curious Village Professor Layton and the Diabolical Box Professor Layton Vs. Phoenix Wright: Ace Attorney Professor Layton and the Unwound Future Layton's Mystery Journey: Katrielle and the Millionaires' Conspiracy 1. Professor Layton and the Last Spectre In chronological order, Professor Layton and the Last Spectre is the first Professor Layton game but the fourth released. The game follows Layton as he uncovers the mysteries of the last spectre, which has been destroying the town of Misthallery. The first  Professor Layton game chronologically tells how Layton and his sidekick Luke Triton first met. Both were received well by fans and critics, scoring 83 from critics and 8.3 from users on Metacritic. Professor Layton and the Last Spectre 2. Professor Layton and the Miracle Mask Professor Layton and the Miracle Mask is the second game chronologically in the series. follows on from the adventures of the Last Spectre, with Layton and his companions uncovering the mysteries behind the evil Gentleman of Miracles, terrorising the town of Montdol. Miracle Mask was the first game in the series to be released on the Nintendo 3DS, releasing in 2011 in Japan and 2012 worldwide. Overall, Miracle mask received an 82 from critics and an 8.1 from fans on Metacritic. Professor Layton and the Miracle Mask 3. Professor Layton and the Azran Legacy Professor Layton and the Azran Legacy is the third game chronologically in the series releasing on the Nintendo 3DS between the years 2013-2014. The game follows Miracle Mask taking the professor and his companions on a grand adventure to discover the Azran civilisation and its treasure. takes the professor to new places such as the city of Froenborg and introduces new characters such as the mysterious Aurora, a living Mummy! Professor Layton and the Azran Legacy reviewed well, receiving a critic score of 81 and a user score of 8.1. Professor Layton and the Azran Legacy 4. Professor Layton and the Curious Village was the first Professor Layton game released and the fourth game chronologically in the series. The game was first released on the Nintendo DS in 2007 in Japan and 2008 worldwide, taking the video game world by storm due to its unique puzzle-solving mechanics and use of the Nintendo DS's touch capabilities. The game followed Professor Layton ad Luke as they sought to find the golden apple. The game reviewed well on Metacritic, achieving critic scores of 85 and user scores of 8.5. Professor Layton and the Curious Village 5. Professor Layton and the Diabolical Box Professor Layton and the Diabolical Box is the fifth Professor Layton game chronologically. takes the professor on an adventure to discover the mysteries of the Diabolical Box, which curses anyone who opens it and has supposedly killed Layton's mentor. Professor Layton and the Diabolical Box was the second game in the series to release on the Nintendo DS, carrying on the strong performance of Curious village, with scores of 84 from critics on Metacritic and user scores of 8.5. Professor Layton and the Diabolical Box 6. Professor Layton Vs. Phoenix Wright: Ace Attorney As highlighted above, the Professor Layton series also consists of one spin-off game: Professor Layton Vs. Phoenix Wright: Ace Attorney. is a crossover game between Professor Layton and Pheonix Wright. The game uses classic characters from the two series and also adds brand new characters. Professor Layton Vs. Phoenix Wright: Ace Attorney consists of two gameplay elements: puzzle gameplay carried out by Professor Layton, using similar gameplay mechanics as Professor Layton and the Miracle Mask and a Trials gameplay involving Phoenix Wright defending clients. The game is the sixth instalment in the Layton series and was reviewed well on Metacritic with user scores of 8.3 and a critic score of 79. Professor Layton Vs. Phoenix Wright: Ace Attorney 7. Professor Layton and the Unwound Future The seventh game in the professor Layton series chronologically is . Professor Layton and the Unwound Future was released on the Nintendo DS in 2008 as the third Layton game on the console. The game follows Layton and Luke as they uncover the mystery's behind a time machine in London, quickly finding themselves transported into the future and the past. The game features new puzzles, new locations and builds on the lore of the Layton games. The game received an 86 from critics on Metacritic and an 8.7 from users, making it one of the highest-rated Layton games. Professor Layton and the Unwound Future 8. Layton's Mystery Journey: Katrielle and the Millionaires' Conspiracy Layton's Mystery Journey: Katrielle and the Millionaires' Conspiracy is chronologically the latest instalment in the series and the last game released. follows the story of Layton's daughter Katrielle Layton as she seeks to find her father, who has gone missing. The game was released in 2017 and featured new puzzles and characters, and new mysteries to uncover. Layton's Mystery Journey: Katrielle and the Millionaires is the worst-reviewed game of the series, with scores of 72 from critics and 6.6 from users. Layton's Mystery Journey: Katrielle and the Millionaires Conspiracy Final Thoughts This article has covered the Professor Layton Games in Chronological Order. As highlighted above, the Professor Layton games are some of the best-reviewed puzzle games of all time. Despite this, they are still widely unknown, deserving more attention. Nevertheless, the Professor Layton games are in the upper echelons of the best puzzle games of all time. For more Nintendo-related news and features, in the footer below. subscribe to our newsletter Also, check out, 10 Best Pokémon ROM Hacks You Can Download for Free Destiny 2: Darkness in the Light Quest Guide Ultimate KOTOR 2 Planet Order: The Best Way to Traverse the Stars

The Professor Layton Games in Chronological Order

Los juegos del profesor Layton en orden cronológico

Back in March, the that the widespread use of the word “blockchain” by multinational companies, international institutions, and famous crypto-entrepreneurs, has transformed the technical term into a meaningless buzzword. Verge boldly claimed At the start of the article, the cites a few examples of companies that have hijacked the word blockchain for their shady marketing campaigns. One company such changed its name from to and saw its share price rise by 289%. Verge Long Island Iced Tea Corp. Long Blockchain Corp , These and perhaps many other companies have a vested interested in perpetuating blockchain ignorance. There are hundreds of companies running crypto-scams — many of which are ponzi schemes and that prey on the ignorance of cryptocurrency investors. The con-artists behind these pyramid schemes, and fake mining pools often purport to mine various cryptocurrencies with insanely high, and unreasonably consistent profit margins. fake mining pools A lot of crypto-ponzi investors are unaware of the fact that Bitcoin and every other mineable cryptocurrency have hash-rate difficulties that increase exponentially as more miners join their respective blockchain networks as nodes, and compete for the block reward: the newly minted cryptocurrency for each mined block. _The estimated number of tera hashes per second (trillions of hashes per second) the Bitcoin network is performing._www.blockchain.com Hash Rate All miners, including will experience diminishing returns, regardless of how much they have invested in mining hardware. The only way any given mining pool would make more money over time, is if other mining pools abandon their efforts. But that is an extremely unlikely scenario, because the of investing in mining hardware compels most miners to continue mining their respective cryptocurrencies until the cost of electricity is greater than the revenue they receive from mining. mining pools sunk cost The lack of a clear and precise working definition of what a blockchain is makes it harder for new, and naive crypto-investors to learn more about the technology. And their lack of knowledge makes them easy prey for a new generation of con-men. Unethical marketing campaigns from ambitious tech companies will only make the situation worse. The had pointed out how legitimate companies are deliberately spreading confusing information to bolster their marketing campaigns. The article gives an example of the consequences of prominent tech companies running marketing campaigns built on the nebulous idea of a blockchain. Verge According to the major news outlets such as the and had erroneously described Estonia’s national identity system as a blockchain technology. The attributes this error to the myth making borne from the marketing decisions of a private Estonian company. Verge, Harvard Business Review The New Yorker, Verge Estonia’s technology vendor, Guardtime, rebranded its offering from “ ” to a “blockchain technology.” That’s not necessarily untrue since “blockchain” has no agreed-upon definition — and for now, it’s a . hash-linked time-stamping good marketing tactic Many more of the world’s most important ideas are plagued by the same conceptual imprecision we see in blockchain technology. But unlike other scientific ideas, the blockchain is a real-world solution. The mere fact that developers have coded and implemented blockchain technologies means that a working definition for blockchain technology exist. No biologist has ever created life in a test tube or petri dish — and that may be why life has despite hundreds of years of research and experimentation. must no scientific definition, Scientists have been debating the scientific meaning of life since Thomas Huxley tried to explain what life is by describing the components of protoplasm in a lecture called . On The Physical Basis of Life Much of the confusion surrounding the term “blockchain” stems from an ongoing debate among software developers on whether or not a blockchain can be centralized. Can a single database hosted on just one server or one computer be described as a blockchain if it uses time-stamping, and hashing algorithms to connect chronologically recorded transactions? The short answer is no. A blockchain is fundamentally a data structure in which transactions are verified in batches called blocks, and the nodes or servers verify said transactions through a consensus algorithm. There are a variety of blockchain consensus algorithms, but they boil down to four essential variations: proof of work, proof of stake, proof of importance, and byzantine fault tolerance or some combination thereof. The notes that some people think a private blockchain is just a shared database, but these two types of distributed databases have different consensus algorithms. A private blockchain uses what can be colloquially referred to as a consensus algorithm or technically described as (PBFT), which requires each masternode or participating company in the network, to simultaneously verify each new transaction by submitting their respective private keys. A shared database on the other hand, uses (MVCC) — a system in which simultaneous transactions that produce conflicting outputs are processed in parallel. And according to Gideon Greenspan, the founder of Coin Sciences, we can think of a blockchain as a distributed MVCC. Verge proof-of-permission practical byzantine fault tolerance multi-concurrency version control Without a consensus algorithm there would be no reason to group transactions into uniquely identifiable and there would be no logical way to justify the use of a blockchain data structure. Bitcoin, for example, uses transaction blocks to set a quantifiable limit on the number of times miners must solve a nonce, the unique key of each batch or block of transactions, in order to determine the overall security of the network. blocks or batches A block or batch of transactions in Bitcoin’s blockchain can only be verified or approved, once miners find the nonce: the 256-bit answer or key to a mathematical puzzle called Hashcash The Verge then cited an article from David Gerard in which the director of the (WFP) tried to explain what a blockchain is. As shown in the following quote; Houman Haddad, one of WFP’s executives, thought a comparison to Git would help clarify the meaning of blockchain technology. World Food Programme Git like blockchain, uses merkle trees. A blockchain, in a certain sense, can be seen as a peer-to-peer (P2P) hosted Git repository. In order to modify this repository, users must have a copy of the whole repository and pull the latest commits. When you download a blockchain client and run it, you have to check out the entire blockchain history, much like checking out a Git repo. Not all distributed databases are blockchains, because not all distributed databases need to process transactions with a consensus algorithm. Git is the kind of distributed database that doesn’t need a consensus algorithm, because each super-user or masternode has complete control over a given code repository and these super-users don’t need to organize their transactions in batches or blocks. To become a blockchain, Git would need some of its code repositories to be owned by multiple companies, and these companies would have to simultaneously agree to each new code change before it was implemented in order to justify the use of a consensus algorithm. is a distributed database that has no consensus algorithm. Git Transactions or commits (code changes) in Git, can be processed one at a time or in batches, and the staging index where new data is temporarily stored is somewhat like Bitcoin’s memory pool of unconfirmed transactions, but neither one of these traits make Git more like a blockchain. The WFP’s private network, built on the Ethereum blockchain, may contain a semi-immutable record of the thousands if not millions of transactions that have taken place within Azraq — Jordan’s refugee camp for Syrians, but that doesn’t make it a blockchain. WFP’s private network could become a blockchain when international NGOs and private companies decide to host their own competing nodes on its network. The lack of trust among such competing institutions could justify the use of a consensus algorithm based on a blockchain data structure. A private network built with the code base of a blockchain, is not an actual blockchain. Ethereum’s transaction may have forced Haddad to build a private network. But since the launch of the , there’s just no reason to continue using a shared database. EOS is a feeless blockchain that can process thousands of transactions per second. If speed and transparency are the key attributes the WFP is looking for in their distributed database, then EOS may be the perfect fit. But if privacy is also a priority, then and are both great tools for building a private blockchain from scratch. gas costs EOS mainnet Tendermint Hyperledger The legal ramifications of ambiguous blockchain terminology may have an even bigger impact on the long-term growth of blockchain adoption than misleading marketing campaigns. Angela Walch, a law professor from St. Mary’s University, believes that are driven by misconceptions and a poor understanding of how the new technology actually works. She provided a few suggestions on how US politicians should go about writing blockchain laws, like hiring internal blockchain experts in their legislative committees. America’s current attempts to create blockchain legislation She went on to describe how some former state employees are now closely associated with blockchain businesses and may be trying to persuade legislators to adopt more favorable legislation on behalf of their new clients. Some of the blockchain industry players are now working together to build a blockchain lobbying group called The Blockchain Association. Professor Walch then argues that the confusion surrounding the idea of immutability has had a negative impact on US legislation. The term “immutable,” with its varying and sometimes non-intuitive meanings in describing blockchain technology, is one that I very much wish we could strike from the blockchain lexicon, as “by ridding ourselves of an unnecessary confusion we should gain very much in the clearness of our thought. Programmers are accustomed to the idea that certain objects, individual pieces of data in a program — can be in the sense that they cannot be changed, but they can be entirely deleted from the program or of the program in question. A lawyer, on the other hand, would think of immutability as a complete or absolute resistance to changes of any kind. immutable a module Unfortunately, many programmers are pragmatic short-termists who feel that software development is more important than business communication, and that’s why programmatic terminology is often only as precise and as thorough as a pragmatist needs it to be. Blockchain developers could start describing blockchains as semi-immutable databases, and with a little caution and diligence, they will in due time solve all the contentious issues surrounding blockchain jargon.

Blockchain is Not Meaningless

Shanghai, China Emerging markets are . home to 86% of the world’s millennials In fact, China and India host more millennials combined than the entire working population of the US and Western Europe. And they are a skilled demographic to be reckoned with. According to a Goldman Sachs report, than their parents. people born in China in the 1990s are 31% more likely to graduate Despite a young tech-savvy workforce budding with entrepreneurial spirit, emerging markets struggle to gain traction due to volatile financial markets and unreliable infrastructure. Blockchain is setting the world on fire, transforming every industry it touches and redefining how we understand technology. It will foster innovation and ideas everywhere, but nowhere more than in emerging economies. This is because blockchain provides a platform of trust and credibility that is difficult to tamper with. It has the ability to , many of which in emerging markets, and inject them into the world economy. identify and verify the 1.1 billion individuals with no documents Emerging economies are using innovative technologies like blockchain to speed up their development and cement their place as industry pioneers. They may seem at a disadvantage with their lacking infrastructure, however, it’s easier and less expensive to build the latest tech on a clean foundation than it is to replace the old and outdated. Blockchain technology can be compared to , where countries “leapfrogged” over fixed-line telephones. the smartphone boom in emerging markets And this rapid development has been replicated across the world. Kenya overcame a lack of banks not by creating more, but by . By banking the unbanked, Kenya spurred a payments revolution that . adopting mobile payment services raised 194,000 Kenyans from poverty Sugarcane production in Brazil While developing countries wean themselves off fossil fuels, Brazil is by creating ethanol through sugarcane production. skipping the “developed” progression of petrol as a fuel source And at the other end of the world, China is with their “Solar Valley.” investing in renewable energy Blockchain can help stabilize emerging economies, and incentivize foreign investors to work in these financial markets. In industries like finance and humanitarian aid, it replaces middlemen that are susceptible to corruption, or may take a cut for their services. Don Tapscott, co-founder of the Blockchain Research Institution, views today’s financial system as “a kludge of industrial technologies and paper-based processes dressed up in a digital wrapper.” Financial institutions are barely keeping up with the pace of technology, and with each misstep comes a devastating economic impact. This is especially true for emerging economies lacking the infrastructure and technology that most nations are built upon. Despite insufficient resources, emerging markets have enormous potential, with low-cost labor, abundant resources and untapped industries ready for investment. What holds them back is their high risk. Corruption, fraud and antiquated bureaucracy keep potential business partners looking elsewhere. Blockchain bolsters emerging economies on multiple fronts. It decreases corruption by providing a clear and transparent platform for tracking payments and assets. Plus, it cuts unnecessary costs that could be better used elsewhere. Remittances are one of the largest form of capital inflows for emerging economies. According to the Overseas Research Institute, . 12% of every $200 sent in 2015 went to banks and other financial institutions United Nations General Assembly Sending humanitarian aid has always been an affair, with countries stopping their contributions upon learning that their donations are being taken by tyrants. Blockchain allows donors to trace payments as they travel, ensuring they don’t fall into the wrong hands. , which is supported by over 40 humanitarian groups, tested their program by watching money as it moved from a school in the UK to four schools in Swaziland. Disberse And in Jordan’s Azraq camp, Syrian refugees are , with the money traveling along a blockchain-based system. using iris-identification technology to purchase groceries and supplies This idea came after the World Food Programme noticed they were paying local banks millions of meals worth of fees. Blockchain reduced 98% of that cost, allowing them to serve more for the same price. It has also inspired companies like Microsoft to work with non-profit organizations on ID2020, a mission to provide legal identity to the 1.1 billion people who lack one. Getting a job usually requires financial and government records. With blockchain, citizens have a cyber wallet that follows them where they go. Blockchain helps provide digital documentation that can’t be lost or stolen, and has led Dubai to run all government documentation through it by 2020. The possibilities of this are endless. The blockchain could be used to verify an individual’s age, education, lineage, even their professional experience. Blockchain technology is reinventing the way that people understand finance, security and transparency. But for emerging economies, it’s changing lives. Our world has never witnessed a technology singlehandedly alter industries in such a way. Providing these budding countries with the resources and technology they need to succeed will drive innovation and globalization to new heights.

Blockchain Empowers Emerging Markets

By: Marketing & Operations Officer Laura Marissa Cullell Can technology progress human rights? Provide humanitarian aid? Help combat Climate Change? Address issues of identity, trafficking, and provide access to food? The answer to these questions is yes. And Blockchain is a fantastic space to explore these issues right now. In the last article of our series on Unique and Interesting Use Cases for Blockchain, I explore some of the applications of blockchain to achieve the UN Sustainable Development Goals. explored just some of the use cases of this innovative tech in the oil and gas industry. saw the possibilities for blockchain in the Music Industry. looked at the possibilities of blockchain in healthcare. Part I Part II Part III For the past couple of months, I’ve been working on my thesis on Blockchain, Human Rights and International Law for the . I have had the chance to learn about a plethora of innovative projects, pilots, and ideas that human rights activists are currently working on to make the world a better place. Is it lucrative? Not always. But it does help make a tangible difference. U.N. Mandated University for Peace Here are some of the most interesting applications of Blockchain for Social impact, more specifically how this innovative technology addresses some of the Sustainable Development Goals. Blockchain Technology and the UN: The Sustainable Development Goals The UN Sustainable Development Goals (SDGs) For those that are unfamiliar, in 2015, all 193 members of the United Nations unanimously passed a implementing a 15-year plan of achieving 17 Sustainable Development, global goals by 2030 (SDGs). Each of these goals has targets to achieve, totalling 169 different targets. The SDGs cover a broad range of social and economic development issues including poverty, hunger, health, education, gender equality, clean water, sanitation, energy, environment, and social justice. resolution SDG No. 2: Eradication of Hunger World Food Programme — Building Blocks UN Aid programs have been wrought with countless cases of fraud, red tape, hefty administrative fees and mismanagement of funds. In order to provide the maximum amount of aid to those who need it the most, the World Food Programme (WFP) implemented a pilot project in 2017 called Building Blocks. It was an early experiment that enabled the transfer of WFP Food and cash on a public Ethereum blockchain through a smartphone app to vulnerable families in Pakistan, addressing SDG Goal №1 and 2- poverty and hunger. Within months, the WFP expanded the pilot to a Syrian refugee camp in Azraq, Jordan to successfully facilitate cash transfers for over 10,000 Syrian refugees on its blockchain payments platform. According to , the implementation of blockchain technology also enabled Syrian refugees to buy food from local retailers using a biometric scan of their eye where each transaction was recorded on a blockchain, rendering the use of cash, bank cards, and paper vouchers obsolete. In this case, the refugees did not need to share any sensitive data with banks or mobile operators, benefiting from greater security and privacy through an immutable, secure blockchain. CCN Now, the World Food Programme is currently expanding its Ethereum-based blockchain after saving millions of dollars in bank transfers by utilizing decentralised blockchain technology. Currently, the WFP feeds over 100 million people across 80 countries. SDG No. 5: Gender Equality and Identity UN Women: Blockchain Simulation Lab is the only UN organ that was founded in the 21st Century. This organ of the UN is dedicated to gender equity and the empowerment of women and was established to accelerate progress of meeting their needs worldwide. Their focus is on increasing women’s leadership and participation, ending violence against women, engaging women in all aspects of peace and security processes,and making gender equality central to national development planning and budgeting. UN Women From January 29 — February 1, 2018, UN Women hosted a 4-day in order to create launching points of achieving goals like gender equality, and ending hunger and poverty. Successful programs from this lab would become part of larger UN initiatives including the UN Women’s Global Flagship Programmes for Disaster Risk Reduction. blockchain simulation lab The Blockchain simulation lab addressed humanitarian scenarios around identity. For stateless refugees, for example, identity isn’t limited to documentation. According to , Blockchain could provide a platform where storing identification documents as a viable way of proving their identity, in a humanitarian scenario. Daniell believes that authorities could also identity women or girls who have been trafficked or who have gone missing. Jordan Daniell The simulation lab is a create first step merging the pursuit of Gender equality and addressing other intersecting sustainable development goals. The winners of this hackathon was and his team. As social innovators from the Congo, and with experience in refugee camps on Malawi, they developed — an app that uses blockchain to enable secure money transfers among women entrepreneurs. This gives them absolute control over their own money, independent of any male members in their family. Olivier Mukuta VIPI Cash Other winners of this hackthon included — a secure place on blockchain built with Syncano where refugees can collect identity data which can be verified by trusted people their journey, and — a decentralized platform that verifies people’s skills and provides a digital verified CV. Digital Grab Bag Diwala SDG No. 13: Climate Action International Union for Conservation of Nature (IUCN): The Green List The (IUCN) is a membership Union uniquely composed of both government and civil society organisations. It provides public, private, and non-governmental organisations with the knowledge and tools that enable human progress, economic development, and nature conservation to take place together. International Union for Conservation of Nature The is looking to utilize blockchain to administer its Green List of Global Conservation areas, which encourages and supports the creation of new and protected conservation sites around the world. Blockchain offers a reduction of bank fees, more transparency about Green List funding, and progress towards meeting its goals. IUCN James Hardcastle, the IUCN’s programme believes that many of these protected areas don’t have an abundance of money at their disposal so reducing financial friction and transaction costs diverts more money into preserving ecosystems. This way, the IUCN can guarantee that donor funds go straight to the site they want to help. Blockchain is also said to possible improve the system of carbon asset transactions. UN Climate Change ( ) believes that recording carbon assets on a public blockchain could guarantee transparency and ensure that transactions are valid and settled automatically. UNFCC SDG No. 16: Peace, Justice and Strong Institutions and Digital Identities ID2020 Article 6 of the Universal Declaration on Human Rights stipulates that “ .” The Sustainable Development Goals (2015–2030) include target 16.9 which aims to “ ID2020 believes this must include the >20M refugees worldwide and seeks to provide. Everyone has the right to recognition everywhere as a person before the law provide legal identity to all, including birth registration, by 2030.” ID2020 is an alliance of governments, NGOs, and the private sector, along with the UN High Commissioner for Human Rights. All of these entities seek to use blockchain technology to give refugees a digital identity. They have currently teamed up with and are looking at rolling out an inter-operable, user-owned and controlled digital identity to its hundreds of thousands of staff. They hope that this initiative will evolve to a standard background check which can be distributed to potential clients using a biometrics system that can manage data on fingerprints and irises. Accenture Improvements on failed Governance Models One of the biggest advantage of using blockchain technology lies in its’ governance mechanisms. Blockchains enable trust which would, in turn, help mitigate corruption. Current international and domestic governance models are wrought with bureaucracy, red tape, and inefficiencies, which hinders access to these key governance structures. The proposed a model of Good Governance which provides a cursory framework for how governments should operate: UNDP Participation — all citizens have the right to an equal voice in decision making Rule of Law — fair an impartial legal frameworks specifically regarding human rights. Transparency — based on free flow of information, where processes, institutions and information is always readily accessible. Responsiveness — Institutions and government agencies serve all stakeholders. Consensus Orientation — acknowledging differing interests and attempts to reach a broad consensus on what is in the best interests of the group and (where possible), on policies and procedures Equity — All citizens regardless of gender, have opportunities to maintain their well-being Effectiveness and Efficiency — Allows both processes and institutions produce results that meet needs while making the best use of resources Accountability — where decision makers in government, the private sector, and civil society organizations are accountable to the public as well as to institutional stakeholders. Blockchain strengthens trust, and promotes information sharing between institutions and the public. It can have an impact on democratic voting models and promote the principles of good governance.A blockchain operates on the principles of consensus, accountability and transparency between all parties. According to , “Local communities could be empowered to manage their natural resources through ad hoc voting. For example, fish might be traded on a platform only if harvest quotas were approved by a community-based democratic process.” Guillaume Chapron By digitizing governance models and facilitating the creation of digital societies, such as , achieving access to just and peaceful institutions is definitely attainable. e-Estonia Challenges with the Technology Blockchain is still in its exploration and infancy phase and may not be suitable for every UN/International humanitarian Agency. For , Chief Innovation Manager at the Porini Foundation, ‘Blockchain for Good’ offers great potential, but cannot be viewed in isolation as a silver bullet for all the world’s pressing issues. Toni Caradonna “Blockchain can give transparency, efficiency gains and independence from banks, but we still need cooperation from legislators. Blockchain alone cannot solve the problem of people who feel free to pollute thinking that someone else will come along and clear up the mess.” State-level and international policy networks need to adapt and allow for innovations while providing adequate legislation that provides a base-level guidance on what can and can’t be accomplished with Blockchain. It’s also hard to ignore the technical challenges. Bitcoin, for example, is slow. Ethereum is an improvement but more are required before it can be universally applied. It’s also important to note that , and if someone were to lose their cryptographic key, or access to a blockchain, there is not way to retrieve it again. to err is human With the ongoing blurring lines between the real and digital world, there have been increasing concerns of data ownership and access to services. With all of these innovations, there needs to be adequate protections in place to protect the identities of refugees or stateless individuals. There is also the issue of the digital divide. This term has typically referred to the gap between those who have access to certain technologies and those that do not. Many developing countries do not have the adequate infrastructure or ability to provide with high-speed internet or digital accessibility. All humanitarian efforts with blockchain are moot if sustainable, cost-effective, long-term solutions cannot be adopted. Blockchain and the progression of Human Rights believes that blockchain presents an opportunity for the Internet development community to claim a degree of recognition in the human rights realm. Kobina Hughes It only makes sense that technology is being utilised to promote gender equality and address current social concerns in ways that are only able to be explored now due to innovations in technology. The fact that Blockchain is currently being adopted by governments and international organs is a wonderful first step at utilizing technology for promoting human rights for all. Do you know of any other interesting developments with Blockchain and the Sustainable Development Goals? Let us know in the comments below! Laura Marissa Cullell is the Marketing & Operations Officer at BlockX Labs. She is a MA Graduate of the UN University of Peace in International Law and Human Rights. She loves puns, glitter, and reading an obscene amount of books. You can reach her at: lcullell@blockxlabs.com

Blockchain and the Sustainable Development Goals

The haters always trod on PHP for a variety of reasons, but one of the easiest targets is how ugly the code can be. Some people may not agree that code cleanliness and “prettiness” matter, but when it comes to maintaining codebases for the long term, you can save your team a lot of time by having code that is easy to read. While at Apple, I developed an ecosystem of finance applications used by internal stakeholders. Over the years, I was constantly onboarding new developers into the tools to assist with general maintenance or enhancements. One of the major takeaways from that time is how important it is to be able to read your code with natural language. It’s interesting to find that most code is written in such a way that you have to read through a significant chunk of it, then go back to the start to fully understand what is going on. Using more expressive language or APIs while writing code can greatly reduce the onboarding and ramp-up times of new developers. This is where Collections shine. Laravel Collections take some complicated array traversals and make them incredibly easy to read and understand. Your code becomes a chain of commands read like “Filter this array to only words starting with a, then map the cost amount to the row, and lastly reduce this to a sum of all costs.” It is very easy to undersell the importance of writing your code with more expressive language, but once you’ve made it a habit, it’s hard to imagine how anything else is acceptable. Of course, readability cannot come at the expense of performance so it’s important to ensure that we’re always making sound choices and only prioritizing readability where it’s appropriate. We should always understand what is happening behind the scenes and be fully aware of what tradeoffs we are encountering with the way that we approach our solutions.  It does no good for our code to be readable if it takes a full second longer than a less readable solution. Let’s dive in and have an in-depth look at PHP arrays vs. Collections and compare performance between the two. Readability Laravel Collections One of the biggest advantages of using Laravel Collections is their fluent syntax, which allows for chaining methods. This results in clean and readable code, as you can perform multiple operations in a sequence without needing temporary variables or complex nested function calls. Let’s compare some of the commonly used array manipulation functions below to see how PHP compares to Collections in a very simple example. Filter PHP array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
}); Collections $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
}); Search PHP array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data); Collections $data->search(function($row) {
    return substr($row, 0, 1) === "a";
}); Map PHP array_map(function($row) {
    return "test";
}, $data); Collections $data->map(function($row) {
    return "test";
}); Sort PHP sort($data); Collections $data->sort(); Each PHP foreach($loop as $item) {
    $doSomething = true;
} Collections $data->each(function($row) {
    return "test";
}); Reduce PHP array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); Collections $data->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); Splice PHP array_splice($data, count($data)/2); Collections $data->splice(count($data)/2); All Together (PHP) $data = array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
});

$data = array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data);

$data = array_map(function($row) {
    return "test";
}, $data);

sort($data);

foreach($loop as $item) {
    $doSomething = true;
}

$sum = array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); All Together (Collections) $sum = $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
})->search(function($row) {
    return substr($row, 0, 1) === "a";
})->map(function($row) {
    return "test";
})->sort()
->each(function($row) {
    return "test";
})->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); Comparison With an approach this simple, there doesn’t seem to be a huge tradeoff in readability for each individual function, although when you consider the example where you need them all to be applied to a single array, you can clearly see that it is more concise and easier to read when using the chained-methods in a collection. Instead of constantly having to overwrite your variable and then set a new variable for output at the end, we can simply chain every command until we get to our desired output. Collections are definitely easier to read the more complex your code becomes. Performance I took the above examples and generated some test data to attempt to determine if collections were more or less performant than standard PHP functions. Each array had 100,000 random strings as items, and I ran each function 100 times. In the end, we calculated the average of all of the response times. The final results are below: ========== Tests Complete (ms) ==========
php      filter:  5.07
collect  filter:  6.49
=======================
php      search:  0.79
collect  search:  0
=======================
php      map:  3.45
collect  map:  4.18
=======================
php      sort:  25.27
collect  sort:  11.18
=======================
php      each:  1.03
collect  each:  6.96
=======================
php      reduce:  2.78
collect  reduce:  7.75
=======================
php      splice:  1
collect  splice:  0.74
======================= Conclusion As you can clearly see, though we gain a large amount of readability from Collections, we lose a significant amount of performance in some key areas. Filter, Map, Foreach, and Reduce are all faster with the standard PHP functions. Foreach and Reduce are actually incredibly significant differences. Search, Sort, and Splice all show Collections as the winner, and Sort is actually a massive time saver. It also is important to note that you would have to create each collection out of a built array which adds a tiny bit of overhead to setting up the collection to begin with, but even with that small amount of extra work and time, the results are pretty clear. In my opinion (and it is just an opinion based on these results), if performance is a huge concern, I would stick with standard PHP functionality for Foreach loops for sure and likely the same for any Reduce needs. If you need to do any sorting on large datasets, clearly Collections is the right way to go. The rest are so close that it really feels like a personal preference. At that point, I’d say collections are always easier to read and maintain. Obviously, you should take this information and make your own informed decision, however, if you’re like me, I think you’ll find yourself slipping collections in for many of these functions above. But I do think I’ll dial back my use of →each and →reduce where appropriate going forward! The haters always trod on PHP for a variety of reasons, but one of the easiest targets is how ugly the code can be. Some people may not agree that code cleanliness and “prettiness” matter, but when it comes to maintaining codebases for the long term, you can save your team a lot of time by having code that is easy to read. While at Apple, I developed an ecosystem of finance applications used by internal stakeholders. Over the years, I was constantly onboarding new developers into the tools to assist with general maintenance or enhancements. One of the major takeaways from that time is how important it is to be able to read your code with natural language. It’s interesting to find that most code is written in such a way that you have to read through a significant chunk of it, then go back to the start to fully understand what is going on. Using more expressive language or APIs while writing code can greatly reduce the onboarding and ramp-up times of new developers. This is where Collections shine. Laravel Collections take some complicated array traversals and make them incredibly easy to read and understand. Your code becomes a chain of commands read like “Filter this array to only words starting with a, then map the cost amount to the row, and lastly reduce this to a sum of all costs.” It is very easy to undersell the importance of writing your code with more expressive language, but once you’ve made it a habit, it’s hard to imagine how anything else is acceptable. Of course, readability cannot come at the expense of performance so it’s important to ensure that we’re always making sound choices and only prioritizing readability where it’s appropriate. We should always understand what is happening behind the scenes and be fully aware of what tradeoffs we are encountering with the way that we approach our solutions.  It does no good for our code to be readable if it takes a full second longer than a less readable solution. Let’s dive in and have an in-depth look at PHP arrays vs. Collections and compare performance between the two. Readability Laravel Collections One of the biggest advantages of using Laravel Collections is their fluent syntax, which allows for chaining methods. This results in clean and readable code, as you can perform multiple operations in a sequence without needing temporary variables or complex nested function calls. Let’s compare some of the commonly used array manipulation functions below to see how PHP compares to Collections in a very simple example. Filter PHP PHP array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
}); array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
}); Collections Collections $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
}); $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
}); Search PHP PHP array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data); array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data); Collections Collections $data->search(function($row) {
    return substr($row, 0, 1) === "a";
}); $data->search(function($row) {
    return substr($row, 0, 1) === "a";
}); Map PHP PHP array_map(function($row) {
    return "test";
}, $data); array_map(function($row) {
    return "test";
}, $data); Collections Collections $data->map(function($row) {
    return "test";
}); $data->map(function($row) {
    return "test";
}); Sort PHP PHP sort($data); sort($data); Collections Collections $data->sort(); $data->sort(); Each PHP PHP foreach($loop as $item) {
    $doSomething = true;
} foreach($loop as $item) {
    $doSomething = true;
} Collections Collections $data->each(function($row) {
    return "test";
}); $data->each(function($row) {
    return "test";
}); Reduce PHP PHP array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); Collections Collections $data->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); $data->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); Splice PHP PHP array_splice($data, count($data)/2); array_splice($data, count($data)/2); Collections Collections $data->splice(count($data)/2); $data->splice(count($data)/2); All Together (PHP) All Together (PHP) $data = array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
});

$data = array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data);

$data = array_map(function($row) {
    return "test";
}, $data);

sort($data);

foreach($loop as $item) {
    $doSomething = true;
}

$sum = array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); $data = array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
});

$data = array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data);

$data = array_map(function($row) {
    return "test";
}, $data);

sort($data);

foreach($loop as $item) {
    $doSomething = true;
}

$sum = array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); All Together (Collections) All Together (Collections) $sum = $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
})->search(function($row) {
    return substr($row, 0, 1) === "a";
})->map(function($row) {
    return "test";
})->sort()
->each(function($row) {
    return "test";
})->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); $sum = $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
})->search(function($row) {
    return substr($row, 0, 1) === "a";
})->map(function($row) {
    return "test";
})->sort()
->each(function($row) {
    return "test";
})->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); Comparison Comparison With an approach this simple, there doesn’t seem to be a huge tradeoff in readability for each individual function, although when you consider the example where you need them all to be applied to a single array, you can clearly see that it is more concise and easier to read when using the chained-methods in a collection. Instead of constantly having to overwrite your variable and then set a new variable for output at the end, we can simply chain every command until we get to our desired output. Collections are definitely easier to read the more complex your code becomes. Performance I took the above examples and generated some test data to attempt to determine if collections were more or less performant than standard PHP functions. Each array had 100,000 random strings as items, and I ran each function 100 times. In the end, we calculated the average of all of the response times. 100,000 random strings as items, and I ran each function 100 times. In the end, we calculated the average of all of the response times. The final results are below: ========== Tests Complete (ms) ==========
php      filter:  5.07
collect  filter:  6.49
=======================
php      search:  0.79
collect  search:  0
=======================
php      map:  3.45
collect  map:  4.18
=======================
php      sort:  25.27
collect  sort:  11.18
=======================
php      each:  1.03
collect  each:  6.96
=======================
php      reduce:  2.78
collect  reduce:  7.75
=======================
php      splice:  1
collect  splice:  0.74
======================= ========== Tests Complete (ms) ==========
php      filter:  5.07
collect  filter:  6.49
=======================
php      search:  0.79
collect  search:  0
=======================
php      map:  3.45
collect  map:  4.18
=======================
php      sort:  25.27
collect  sort:  11.18
=======================
php      each:  1.03
collect  each:  6.96
=======================
php      reduce:  2.78
collect  reduce:  7.75
=======================
php      splice:  1
collect  splice:  0.74
======================= Conclusion As you can clearly see, though we gain a large amount of readability from Collections, we lose a significant amount of performance in some key areas. Filter , Map , Foreach , and Reduce are all faster with the standard PHP functions. Foreach and Reduce are actually incredibly significant differences. Search , Sort , and Splice all show Collections as the winner, and Sort is actually a massive time saver. Filter Map Foreach Reduce Foreach Reduce Search Sort Splice Sort It also is important to note that you would have to create each collection out of a built array which adds a tiny bit of overhead to setting up the collection to begin with, but even with that small amount of extra work and time, the results are pretty clear. In my opinion (and it is just an opinion based on these results), if performance is a huge concern, I would stick with standard PHP functionality for Foreach loops for sure and likely the same for any Reduce needs. If you need to do any sorting on large datasets, clearly Collections is the right way to go. The rest are so close that it really feels like a personal preference. Foreach Reduce At that point, I’d say collections are always easier to read and maintain. Obviously, you should take this information and make your own informed decision, however, if you’re like me, I think you’ll find yourself slipping collections in for many of these functions above. But I do think I’ll dial back my use of →each and →reduce where appropriate going forward! →each →reduce

Illuminate Collections vs PHP Arrays

Colecciones Illuminate vs. matrices PHP

Collections Illuminate vs tableaux PHP

Illuminate コレクションと PHP 配列

Illuminate Collections наспроти PHP Arrays

Kolekcje Illuminate kontra tablice PHP

Coleções Illuminate vs Arrays PHP

Kumurika Ibyegeranyo vs PHP

Kolleksiýalary we PHP massiwlerini ýagtylandyryň

Authors:
(1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel;
(2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Table of Links Abstract and Introduction Related Work Methodology Experimental Settings Results Conclusion and Limitations Bibliographical References 2. Related Work K et al. (2020) have suggested that structural similarity of languages is essential for language model’s multilingual generalization capabilities. Their suggestion was further discussed by Dufter and Schütze (2020), who highlighted the essential components for a model to possess “multilinguality”, and show that the order of the words in the sentence is key to the model’s cross-lingual generalization capabilities. mBERT, as introduced by Devlin et al. (2019), was a pioneering language model that encompassed multiple languages, including Arabic and Hebrew. However, both Arabic and Hebrew are significantly under-represented in the pre-training data, resulting in inferior performance compared to the equivalent monolingual models on various downstream tasks (Antoun et al., 2020; Lan et al., 2020; Chriqui and Yahav, 2022; Seker et al., 2022). GigaBERT (Lan et al., 2020) is another multilingual model, trained for English and Arabic. However, the best results for most of the known NLP tasks are typically achieved by one of the large monolingual models in both Arabic and Hebrew. CAMeLBERT (Inoue et al., 2021), is one of those models. It is trained on texts written in Modern Standard Arabic (MSA), Classical Arabic, as well as dialectal variants. In the realm of Hebrew language models, AlephBERT (Seker et al., 2022) stands out as one of the leading performers, alongside others like HeBERT (Chriqui and Yahav, 2022). Among other datasets, the monolingual models mentioned above use the relevant parts of the OSCAR dataset (Ortiz Suárez et al., 2020) for training. Our model relies solely on the OSCAR dataset for both Hebrew and Arabic, resulting in a considerably smaller total number of words for each language in comparison to the existing monolingual language models. The effect of transliteration on cross-lingual generalization were discussed previously in (Dhamecha et al., 2021; Chau and Smith, 2021) and more recently in (Moosa et al., 2023; Purkayastha et al., 2023). None of these works study the languages of our focus: Arabic and Hebrew. Dhamecha et al. (2021) focused on languages from the Indo-Aryan family, which has been studied before for cross-lingual generalization and also has several publicly available multilingual models. To the best of our knowledge, our work is first to study generalization between Arabic and Hebrew and no multilingual masked language models that include both languages have been published apart from mBERT. Chau and Smith (2021) address the generalization from high- to low-resourced languages. However, both Arabic and Hebrew are currently considered medium- to high-resourced languages. Furthermore, their evaluation focuses solely on tokenlevel classification tasks, such as dependency parsing and part-of-speech tagging, whereas our evaluation targets machine translation, a sequence-to-sequence bilingual task. Purkayastha et al. (2023) employ Romanization for transliteration, whereas we transliterate Arabic into the Hebrew script. Analogous to Chau and Smith (2021), their evaluation centers on token-level classification tasks, which are not addressed in our work. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel; (2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Authors: Authors: (1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel; (2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Settings Experimental Settings Results Results Conclusion and Limitations Conclusion and Limitations Bibliographical References Bibliographical References 2. Related Work K et al. (2020) have suggested that structural similarity of languages is essential for language model’s multilingual generalization capabilities. Their suggestion was further discussed by Dufter and Schütze (2020), who highlighted the essential components for a model to possess “multilinguality”, and show that the order of the words in the sentence is key to the model’s cross-lingual generalization capabilities. mBERT, as introduced by Devlin et al. (2019), was a pioneering language model that encompassed multiple languages, including Arabic and Hebrew. However, both Arabic and Hebrew are significantly under-represented in the pre-training data, resulting in inferior performance compared to the equivalent monolingual models on various downstream tasks (Antoun et al., 2020; Lan et al., 2020; Chriqui and Yahav, 2022; Seker et al., 2022). GigaBERT (Lan et al., 2020) is another multilingual model, trained for English and Arabic. However, the best results for most of the known NLP tasks are typically achieved by one of the large monolingual models in both Arabic and Hebrew. CAMeLBERT (Inoue et al., 2021), is one of those models. It is trained on texts written in Modern Standard Arabic (MSA), Classical Arabic, as well as dialectal variants. In the realm of Hebrew language models, AlephBERT (Seker et al., 2022) stands out as one of the leading performers, alongside others like HeBERT (Chriqui and Yahav, 2022). Among other datasets, the monolingual models mentioned above use the relevant parts of the OSCAR dataset (Ortiz Suárez et al., 2020) for training. Our model relies solely on the OSCAR dataset for both Hebrew and Arabic, resulting in a considerably smaller total number of words for each language in comparison to the existing monolingual language models. The effect of transliteration on cross-lingual generalization were discussed previously in (Dhamecha et al., 2021; Chau and Smith, 2021) and more recently in (Moosa et al., 2023; Purkayastha et al., 2023). None of these works study the languages of our focus: Arabic and Hebrew. Dhamecha et al. (2021) focused on languages from the Indo-Aryan family, which has been studied before for cross-lingual generalization and also has several publicly available multilingual models. To the best of our knowledge, our work is first to study generalization between Arabic and Hebrew and no multilingual masked language models that include both languages have been published apart from mBERT. Chau and Smith (2021) address the generalization from high- to low-resourced languages. However, both Arabic and Hebrew are currently considered medium- to high-resourced languages. Furthermore, their evaluation focuses solely on tokenlevel classification tasks, such as dependency parsing and part-of-speech tagging, whereas our evaluation targets machine translation, a sequence-to-sequence bilingual task. Purkayastha et al. (2023) employ Romanization for transliteration, whereas we transliterate Arabic into the Hebrew script. Analogous to Chau and Smith (2021), their evaluation centers on token-level classification tasks, which are not addressed in our work. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

HeArBERT: A Bilingual Model for Arabic-Hebrew Translation Using Transliteration

Lua is a highly versatile programming language. It is widely used in gaming, embedded systems, web development, networking, automation, and customization. Its small size, flexibility, and speed make it a great choice for embedding into larger applications or for use in environments with constrained resources. When using Lua, we can’t avoid tables. Lua tables are flexible and can be used as arrays, dictionaries (hash maps), or even mixed structures. Tables have no fixed size and can grow based on our needs. Meanwhile, there is one important thing we should not forget: ordering in tables. Order insertion in tables Lua tables, when used as arrays, maintain the insertion order, which makes them a good candidate for lists or arrays in various use cases, including with Redis. In this case, tables can be created manually in the script or returned as a result by third-party libraries, like Redis. Let’s create the table manually and check the insertion order: steps = {}
for i = 1, 10, 1 do
    table.insert(steps,i)
end After the loop, steps = {1; 2; 3; 4; 5; 6; 7; 8; 9; 10} Let’s check what happens when Redis returns a table as a result. redis.call("HSET", 'users', 345, "John", 122, "Bob", 543, "Sam")
local dataDb = redis.call("HGETALL", 'users') After the script is executed, dataDb = {"345", "John", "122", "Bob", "543", "Sam"} In both scenarios, we can find the created table as an array where the insertion order is preserved. But what if we need to convert the Redis response (which is a table as an array) into a key-value associative array? For the first example, we store the following key-value pairs in Redis and want to use the same structure in Lua when processing: {
     345: {name: “John”},
     122: {name: “Bob”},
     543: {name: “Sam”},
     202: {name: “Peter”}
} Run the following script and check the created associative array: redis.call("HSET", 'users', 345, "John", 122, "Bob", 543, "Sam", 202, "Peter")
 
local dataDb = redis.call("HGETALL", 'users')
 
local directory = {}
 
for i = 1, #dataDb, 2 do
    directory[dataDb[i]] = dataDb[i+1]
end where, after the loop dataDb = {"345"; "John"; "122"; "Bob"; "543"; "Sam"; "202"; "Peter"} directory = {["122"]="Bob"; ["345"]="John"; ["202"]="Peter"; ["543"]="Sam"} Let's look at another example of using the cjson module: redis.call('SET', 'packages', '{"10":{"name":"Plus"},"15":{"name":"Family"},"25":{"name":"Premium"}}')
 
local pkgsDb = redis.call('GET', 'packages')
local parsedPkgs = cjson.decode(pkgsDb) where: pkgsDb = "{\"10\":{\"name\":\"Plus\"},\"15\":{\"name\":\"Family\"},\"25\":{\"name\":\"Premium\"}}" parsedPkgs = {["25"]={["name"]="Premium"}; ["15"]={["name"]="Family"}; ["10"]={["name"]="Plus"}} It is good to remember that the CJSON module returns a table in a different order than the one parsed in the JSON. This can prevent errors and save you time, as debugging such problems is quite difficult. In both scenarios, we may find that the insertion order is not guaranteed in a table as an associative array. How to sort a table key when needed? Sometimes, having ordered tables in Lua is important. The order of elements can be critical for the functionality that the script provides. We can use the built-in table.sort function to sort tables. It receives the array to be sorted, along with an optional order function. If this function is not provided, sort uses the default mode. All numeric values will be sorted in ascending order, and all strings will be sorted alphabetically. Sorting table – array In the case of simple sorting of numeric or string values, it is enough to use the following expression: local num = {4, 2, 6}
local str = {"pear" ,"apple", "melon"}
 
table.sort(num)
table.sort(str) where sorted  num = {2; 4; 6} and str = {"apple"; "melon"; "pear"} If the sorting is based on a certain condition, we can define a comparison function. local t = {4,7,2,3}
table.sort(t, function(a, b) return a > b end) where sorted   t = {4; 7; 2; 3} Sorting a table – dictionary To sort a table structured as a dictionary, we can sort it by key or value.
This requires several steps: Extract the keys or values into an array-like table


Sort this array


Iterate over the sorted keys and get the value from the original array by key Sort by keys: local transport = {[100]="train", [70]="tram", [90]="bus"}
 
local keys = {}
for key in pairs(transport) do
    table.insert(keys, key)
end
 
table.sort(keys)
 
for _, key in ipairs(keys) do
    print(key, transport[key])
end Script result: 70 tram, 90 bus, 100 train Sort by values: local entities = {}
 
for key, value in pairs(transport) do
    table.insert(entities, {key = key, value = value})
end
 
table.sort(entities, function(a, b) return a.value < b.value end)
 
for _, entity in ipairs(entities) do
    print(entity.key, entity.value)
end Script result: 90 bus, 100 train, 70 tram Conclusion Having ordered tables in Lua is important when the order of elements matters. In Lua, tables that function like dictionaries have no inherent order, meaning that when we iterate over them using pairs(), the order of elements is not guaranteed. In this article, we explored how to sort arrays in Lua using table.sort and how to manage separate key and value arrays for sorting dictionaries. Lua is a highly versatile programming language. It is widely used in gaming, embedded systems, web development, networking, automation, and customization. Its small size, flexibility, and speed make it a great choice for embedding into larger applications or for use in environments with constrained resources. When using Lua, we can’t avoid tables. Lua tables are flexible and can be used as arrays, dictionaries (hash maps), or even mixed structures. Tables have no fixed size and can grow based on our needs. Meanwhile, there is one important thing we should not forget: ordering in tables . ordering in tables Order insertion in tables Order insertion in tables Lua tables, when used as arrays, maintain the insertion order, which makes them a good candidate for lists or arrays in various use cases, including with Redis . Redis In this case, tables can be created manually in the script or returned as a result by third-party libraries, like Redis. Let’s create the table manually and check the insertion order: steps = {}
for i = 1, 10, 1 do
    table.insert(steps,i)
end steps = {}
for i = 1, 10, 1 do
    table.insert(steps,i)
end After the loop, steps = {1; 2; 3; 4; 5; 6; 7; 8; 9; 10} After the loop, steps = {1; 2; 3; 4; 5; 6; 7; 8; 9; 10} Let’s check what happens when Redis returns a table as a result. redis.call("HSET", 'users', 345, "John", 122, "Bob", 543, "Sam")
local dataDb = redis.call("HGETALL", 'users') redis.call("HSET", 'users', 345, "John", 122, "Bob", 543, "Sam")
local dataDb = redis.call("HGETALL", 'users') After the script is executed, dataDb = {"345", "John", "122", "Bob", "543", "Sam"} After the script is executed, dataDb = {"345", "John", "122", "Bob", "543", "Sam"} In both scenarios, we can find the created table as an array where the insertion order is preserved. In both scenarios, we can find the created table as an array where the insertion order is preserved. But what if we need to convert the Redis response (which is a table as an array) into a key-value associative array? For the first example, we store the following key-value pairs in Redis and want to use the same structure in Lua when processing: {
     345: {name: “John”},
     122: {name: “Bob”},
     543: {name: “Sam”},
     202: {name: “Peter”}
} {
     345: {name: “John”},
     122: {name: “Bob”},
     543: {name: “Sam”},
     202: {name: “Peter”}
} Run the following script and check the created associative array: redis.call("HSET", 'users', 345, "John", 122, "Bob", 543, "Sam", 202, "Peter")
 
local dataDb = redis.call("HGETALL", 'users')
 
local directory = {}
 
for i = 1, #dataDb, 2 do
    directory[dataDb[i]] = dataDb[i+1]
end redis.call("HSET", 'users', 345, "John", 122, "Bob", 543, "Sam", 202, "Peter")
 
local dataDb = redis.call("HGETALL", 'users')
 
local directory = {}
 
for i = 1, #dataDb, 2 do
    directory[dataDb[i]] = dataDb[i+1]
end where, after the loop where, after the loop dataDb = {"345"; "John"; "122"; "Bob"; "543"; "Sam"; "202"; "Peter"} dataDb = {"345"; "John"; "122"; "Bob"; "543"; "Sam"; "202"; "Peter"} directory = {["122"]="Bob"; ["345"]="John"; ["202"]="Peter"; ["543"]="Sam"} directory = {["122"]="Bob"; ["345"]="John"; ["202"]="Peter"; ["543"]="Sam"} Let's look at another example of using the cjson module: redis.call('SET', 'packages', '{"10":{"name":"Plus"},"15":{"name":"Family"},"25":{"name":"Premium"}}')
 
local pkgsDb = redis.call('GET', 'packages')
local parsedPkgs = cjson.decode(pkgsDb) redis.call('SET', 'packages', '{"10":{"name":"Plus"},"15":{"name":"Family"},"25":{"name":"Premium"}}')
 
local pkgsDb = redis.call('GET', 'packages')
local parsedPkgs = cjson.decode(pkgsDb) where: where: pkgsDb = "{\"10\":{\"name\":\"Plus\"},\"15\":{\"name\":\"Family\"},\"25\":{\"name\":\"Premium\"}}" pkgsDb = "{\"10\":{\"name\":\"Plus\"},\"15\":{\"name\":\"Family\"},\"25\":{\"name\":\"Premium\"}}" parsedPkgs = {["25"]={["name"]="Premium"}; ["15"]={["name"]="Family"}; ["10"]={["name"]="Plus"}} parsedPkgs = {["25"]={["name"]="Premium"}; ["15"]={["name"]="Family"}; ["10"]={["name"]="Plus"}} It is good to remember that the CJSON module returns a table in a different order than the one parsed in the JSON. This can prevent errors and save you time, as debugging such problems is quite difficult. It is good to remember that the CJSON module returns a table in a different order than the one parsed in the JSON. This can prevent errors and save you time, as debugging such problems is quite difficult. In both scenarios, we may find that the insertion order is not guaranteed in a table as an associative array. In both scenarios, we may find that the insertion order is not guaranteed in a table as an associative array. How to sort a table key when needed? How to sort a table key when needed? Sometimes, having ordered tables in Lua is important. The order of elements can be critical for the functionality that the script provides. We can use the built-in table.sort function to sort tables. It receives the array to be sorted, along with an optional order function. If this function is not provided, sort uses the default mode. All numeric values will be sorted in ascending order, and all strings will be sorted alphabetically. table.sort Sorting table – array Sorting table – array In the case of simple sorting of numeric or string values, it is enough to use the following expression: In the case of simple sorting of numeric or string values, it is enough to use the following expression: local num = {4, 2, 6}
local str = {"pear" ,"apple", "melon"}
 
table.sort(num)
table.sort(str) local num = {4, 2, 6}
local str = {"pear" ,"apple", "melon"}
 
table.sort(num)
table.sort(str) where sorted  num = {2; 4; 6} and str = {"apple"; "melon"; "pear"} where sorted  num = {2; 4; 6} and str = {"apple"; "melon"; "pear"} If the sorting is based on a certain condition, we can define a comparison function. local t = {4,7,2,3}
table.sort(t, function(a, b) return a > b end) local t = {4,7,2,3}
table.sort(t, function(a, b) return a > b end) where sorted   t = {4; 7; 2; 3} where sorted   t = {4; 7; 2; 3} Sorting a table – dictionary Sorting a table – dictionary To sort a table structured as a dictionary, we can sort it by key or value.
This requires several steps: Extract the keys or values into an array-like table Sort this array Iterate over the sorted keys and get the value from the original array by key Extract the keys or values into an array-like table Extract the keys or values into an array-like table Sort this array Sort this array Iterate over the sorted keys and get the value from the original array by key Iterate over the sorted keys and get the value from the original array by key Sort by keys: Sort by keys: local transport = {[100]="train", [70]="tram", [90]="bus"}
 
local keys = {}
for key in pairs(transport) do
    table.insert(keys, key)
end
 
table.sort(keys)
 
for _, key in ipairs(keys) do
    print(key, transport[key])
end local transport = {[100]="train", [70]="tram", [90]="bus"}
 
local keys = {}
for key in pairs(transport) do
    table.insert(keys, key)
end
 
table.sort(keys)
 
for _, key in ipairs(keys) do
    print(key, transport[key])
end Script result: 70 tram, 90 bus, 100 train Script result: 70 tram, 90 bus, 100 train Sort by values: Sort by values: local entities = {}
 
for key, value in pairs(transport) do
    table.insert(entities, {key = key, value = value})
end
 
table.sort(entities, function(a, b) return a.value < b.value end)
 
for _, entity in ipairs(entities) do
    print(entity.key, entity.value)
end local entities = {}
 
for key, value in pairs(transport) do
    table.insert(entities, {key = key, value = value})
end
 
table.sort(entities, function(a, b) return a.value < b.value end)
 
for _, entity in ipairs(entities) do
    print(entity.key, entity.value)
end Script result: 90 bus, 100 train, 70 tram Script result: 90 bus, 100 train, 70 tram Conclusion Having ordered tables in Lua is important when the order of elements matters. In Lua, tables that function like dictionaries have no inherent order, meaning that when we iterate over them using pairs() , the order of elements is not guaranteed. In this article, we explored how to sort arrays in Lua using table.sort and how to manage separate key and value arrays for sorting dictionaries. pairs() table.sort

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