Partly's mission is to connect the world's parts and we're doing that by building the first global platform for replacement parts, starting with auto parts. Our big vision is to accelerate the world towards a sustainable future where waste is eliminated and all replacement parts are universally searchable, accessible and available to all. We've more than tripled our team over the last 12 months and expect to double in size again over the coming 12 months. We're a global team spanning both Europe and Australasia. Founded by ex-Rocket Lab engineers, we utilise bleeding-edge technology to solve challenging but exciting problems that make a huge impact in a $1.9 trillion industry. We provide a scalable digital infrastructure solution to some of the world's largest businesses and the most exciting startups. Partly's solutions are integrated across hundreds of companies globally, providing the backbone for cataloguing and managing parts online. Blackbird Ventures (Canva, CultureAmp etc.) led both our Pre-Seed and Seed rounds, alongside I2BF (NYC), Hillfarrance, Icehouse, Peter Beck (Rocket Lab), Akshay Kothari (Notion Co-Founder) and Dylan Field (Figma Co-Founder). We're continuing to build a world-class team and ensuring Partly is a place where people can do the best work of their lives. We're proud of the culture we've built at Partly, and our values are lived throughout every experience.

Partly

If you haven’t listened to that Elon musk interview with Andrew Sorkin yet, don’t bother. I’ll give you the tldr: Elon: the reason I’m ever here is because we are good friends, Jonathan. Andrew:…first of all I’m Andrew… Andrew: so what do you say to advertisers who flock the platform because of your antisemitic comments? Elon: fuck them…I’m sorry I wasn’t being clear.. FUCK THEM! I hope that’s clear. Andrew: why are you the way you are? What motivates you? Elon: I’ve had a difficult childhood. Andrew: *starts talking about his failures” Elon: I’ve launched rockets, not made ice creams! Andrew: you have decidedly moved to the right in the past years… Elon: Biden Bitch didn’t invite Tesla to the EV summit!!! Does that seem fair to you? Andrew: would you vote for Biden? Elon: Vivek seems interesting. Free speech!!!!! Andrew: didn’t you throttle the New York Times over the summer though? What did that say about free speech? Elon: well freedom comes with a price okay? It’s pretty cheap though. Andrew: what about China? Do you throw free speech out the window there? Elon: *changes tone* my hands are tied. I love China. The Chinese companies are very competitive! you're welcome!!

tl;dr of Elon Musk's interview with Andrew Ross Sorkin (HackerNoon's Version) (Partly Satire)

এন্ড্রু রস সরকিনের সাথে ইলন মাস্কের সাক্ষাৎকারের ডাঃ (হ্যাকারনুনের সংস্করণ) (আংশিকভাবে ব্যঙ্গ)

tl;dr von Elon Musks Interview mit Andrew Ross Sorkin (HackerNoons Version) (teilweise Satire)

tl;dr de la entrevista de Elon Musk con Andrew Ross Sorkin (versión de HackerNoon) (en parte sátira)

tl;dr de l'interview d'Elon Musk avec Andrew Ross Sorkin (version HackerNoon) (en partie satire)

एंड्रयू रॉस सॉर्किन के साथ एलोन मस्क के साक्षात्कार का टीएल;डॉक्टर (हैकरनून का संस्करण) (आंशिक रूप से व्यंग्य)

イーロン・マスク氏のアンドリュー・ロス・ソーキン氏インタビュー（HackerNoon版）（一部風刺）

Elon Musk와 Andrew Ross Sorkin의 인터뷰 요약(HackerNoon 버전)(일부 풍자)

dr da entrevista de Elon Musk com Andrew Ross Sorkin (versão HackerNoon) (parcialmente sátira)

tl;dr интервью Илона Маска с Эндрю Россом Соркиным (версия HackerNoon) (частично сатира)

Elon Musk'un Andrew Ross Sorkin ile röportajından tl;dr (HackerNoon Versiyonu) (Kısmen Hiciv)

tl;dr cuộc phỏng vấn của Elon Musk với Andrew Ross Sorkin (Phiên bản HackerNoon) (Một phần châm biếm)

埃隆·马斯克 (Elon Musk) 对安德鲁·罗斯·索金 (Andrew Ross Sorkin) 的采访（HackerNoon 版）（部分讽刺）

Navigation with Large Language Models: Discussion and References

Authors:
(1) Renan F. F. da Silva, Institute of Computing, University of Campinas;
(2) Yulle G. F. Borges, Institute of Computing, University of Campinas;
(3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. 9 Conclusion Our approaches were quite effective, enabling us to find solutions very close to the optimal solution, even in instances with many items. These results are partly due to the excellent quality of the solutions given by Two-by-Two, which efficiently handles color constraints. Another positive factors are our neighborhoods, which are efficient in quality and time complexity. Lastly, Gilmore and Gomory Formulation has a strong relaxation for the BPP, and this property may extend to CBPP. For a strong relaxation, the values for the optimal fractional and integer solutions are very close, so the patterns used by relaxation probably belong to integer solutions of high quality. Therefore, this is an excellent method to find a good set of patterns for the initial build of GRASP. Acknowledgements This project was supported by the S˜ao Paulo Research Foundation (FAPESP) grants #2015/11937-9 and #2020/06511-0; and the Brazilian National Council For Scientific and Technological Development (CNPq) grants #144257/2019-0, #311039/2020-0 and #425340/2016 3. Declarations Conflict of interest All authors declare that they have no conflicts of interest. References [1] Alsarhan H, Chia D, Christman A, et al (2016) A two-pass algorithm for unordered colored bin packing. Proceedings of the 9th International Conference on Discrete Optimization and Operations Research and Scientific School pp 1–10 [2] Balogh J, B´ek´esi J, Dosa G, et al (2013) Black and white bin packing. In: Erlebach T, Persiano G (eds) Approximation and Online Algorithms. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 131–144, https://doi.org/10.1007/978-3-642-38016-7_12 [3] Balogh J, B´ek´esi J, D´osa G, et al (2015) Offline black and white bin packing. Theoretical Computer Science 596:92–101. https://doi.org/10.1016/j.tcs.2015.06.045 [4] Belov G, Scheithauer G (2006) A branch-and-cut-and-price algorithm for one-dimensional stock cutting and two-dimensional two-stage cutting. European Journal of Operational Research 171(1):85–106. https://doi.org/10.1016/j.ejor.2004.08.036 [5] B¨ohm M, Sgall J, Vesel´y P (2015) Online colored bin packing. In: Bampis E, Svensson O (eds) Approximation and Online Algorithms. Springer International Publishing, Cham, pp 35–46, https://doi.org/10.1007/978-3-319-18263-6_4 [6] B¨ohm M, D´osa G, Epstein L, et al (2018) Colored bin packing: Online algorithms and lower bounds. Algorithmica 80(1):155–184. https://doi.org/10.1007/s00453-016-0248-2 [7] Borges Y, Schouery R (2020) Modelos pseudo polinomiais para o problema do empacotamento colorido. In: Anais do V Encontro de Teoria da Computa¸c˜ao, pp 37–40, https://doi.org/ 10.5753/etc.2020.11084, (In Portuguese) [8] Borges YGF, Schouery RCS, Miyazawa FK (2023) Mathematical models and exact algorithms for the colored bin packing problem. 2305.15291 [9] Brand˜ao F, Pedroso JP (2016) Bin packing and related problems: General arc-flow formulation with graph compression. Computers & Operations Research 69:56–67. https://doi.org/10. 1016/j.cor.2015.11.009 [10] Buljubaˇsi´c M, Vasquez M (2016) Consistent neighborhood search for one-dimensional bin packing and two-dimensional vector packing. Computers & Operations Research 76:12–21. https://doi.org/10.1016/j.cor.2016.06.009 [11] Carvalho JMVd (1999) Exact solution of bin-packing problems using column generation and branch-and-bound. Annals of Operations Research 86(0):629–659. https://doi.org/10. 1023/A:1018952112615 [12] Castelli M, Vanneschi L (2014) A hybrid harmony search algorithm with variable neighbourhood search for the bin-packing problem. 2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014) pp 1–6. https://doi.org/10.1109/NaBIC.2014. 6921849 [13] Chen J, Han X, Bein W, et al (2015) Black and white bin packing revisited. In: Lu Z, Kim D, Wu W, et al (eds) Combinatorial Optimization and Applications. Springer International Publishing, Cham, pp 45–59, https://doi.org/10.1007/978-3-319-26626-8_4 [14] Delorme M, Iori M (2020) Enhanced pseudo-polynomial formulations for bin packing and cutting stock problems. INFORMS Journal on Computing 32(1):101–119. https://doi.org/ 10.1287/ijoc.2018.0880 [15] Delorme M, Iori M, Martello S (2018) Bpplib: a library for bin packing and cutting stock problems. Optimization Letters 12(2):235–250. https://doi.org/10.1007/s11590-017-1192-z [16] Demˇsar J (2006) Statistical comparisons of classifiers over multiple data sets. The Journal of Machine learning research 7:1–30 [17] D´osa G, Epstein L (2014) Colorful bin packing. In: Ravi R, Gørtz IL (eds) Algorithm Theory – SWAT 2014. Springer International Publishing, Cham, pp 170–181, https://doi.org/10. 1007/978-3-319-08404-6_15 [18] Feo TA, Resende MG (1989) A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters 8(2):67–71. https://doi.org/https://doi.org/ 10.1016/0167-6377(89)90002-3 [19] Fleszar K, Hindi KS (2002) New heuristics for one-dimensional bin-packing. Computers & Operations Research 29(7):821–839. https://doi.org/10.1016/S0305-0548(00)00082-4 [20] Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32(200):675–701. https: //doi.org/10.1080/01621459.1937.10503522 [21] Gilmore PC, Gomory RE (1961) A linear programming approach to the cutting-stock problem. Operations Research 9(6):849–859. https://doi.org/10.1287/opre.29.6.1092 [22] Gonz´alez-San-Mart´ın J, Cruz-Reyes L, G´omez-Santill´an C, et al (2023) Comparative Study of Heuristics for the One-Dimensional Bin Packing Problem, Springer Nature Switzerland, Cham, pp 293–305. https://doi.org/10.1007/978-3-031-28999-6_19 [23] Gupta JND, Ho JC (1999) A new heuristic algorithm for the one-dimensional binpacking problem. Production Planning & Control 10(6):598–603. https://doi.org/10.1080/ 095372899232894 [24] Iman RL, Davenport JM (1980) Approximations of the critical region of the fbietkan statistic. Communications in Statistics - Theory and Methods 9(6):571–595. https://doi.org/10. 1080/03610928008827904 [25] Karp RM (1972) Reducibility among Combinatorial Problems, Springer US, Boston, MA, pp 85–103. https://doi.org/10.1007/978-1-4684-2001-2_9 [26] de Lima VL, Iori M, Miyazawa FK (2022) Exact solution of network flow models with strong relaxations. Mathematical Programming pp 1–34. https://doi.org/10.1007/ s10107-022-01785-9 [27] Loh KH, Golden B, Wasil E (2008) Solving the one-dimensional bin packing problem with a weight annealing heuristic. Computers & Operations Research 35(7):2283–2291. https:// doi.org/10.1016/j.cor.2006.10.021 [28] Mladenovi´c N, Hansen P (1997) Variable neighborhood search. Computers & Operations Research 24(11):1097–1100. https://doi.org/10.1016/S0305-0548(97)00031-2 [29] Nemenyi PB (1963) Distribution-free multiple comparisons. PhD thesis, Princeton University [30] Pessoa A, Sadykov R, Uchoa E, et al (2020) A generic exact solver for vehicle routing and related problems. Mathematical Programming 183(1):483–523. https://doi.org/10.1007/ s10107-020-01523-z [31] Vance PH (1998) Branch-and-price algorithms for the one-dimensional cutting stock problem. Computational Optimization and Applications 9(3):211–228. https://doi.org/10.1023/A: 1018346107246 [32] Wei L, Luo Z, Baldacci R, et al (2020) A new branch-and-price-and-cut algorithm for onedimensional bin-packing problems. INFORMS Journal on Computing 32(2):428–443. https://doi.org/10.1287/ijoc.2018.0867 This paper is available on arxiv under CC 4.0 license. Authors: (1) Renan F. F. da Silva, Institute of Computing, University of Campinas; (2) Yulle G. F. Borges, Institute of Computing, University of Campinas; (3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. Authors: (1) Renan F. F. da Silva, Institute of Computing, University of Campinas; (2) Yulle G. F. Borges, Institute of Computing, University of Campinas; (3) Rafael C. S. Schouery, Institute of Computing, University of Campinas. 9 Conclusion Our approaches were quite effective, enabling us to find solutions very close to the optimal solution, even in instances with many items. These results are partly due to the excellent quality of the solutions given by Two-by-Two, which efficiently handles color constraints. Another positive factors are our neighborhoods, which are efficient in quality and time complexity. Lastly, Gilmore and Gomory Formulation has a strong relaxation for the BPP, and this property may extend to CBPP. For a strong relaxation, the values for the optimal fractional and integer solutions are very close, so the patterns used by relaxation probably belong to integer solutions of high quality. Therefore, this is an excellent method to find a good set of patterns for the initial build of GRASP. Acknowledgements This project was supported by the S˜ao Paulo Research Foundation (FAPESP) grants #2015/11937-9 and #2020/06511-0; and the Brazilian National Council For Scientific and Technological Development (CNPq) grants #144257/2019-0, #311039/2020-0 and #425340/2016 3. Acknowledgements Declarations Conflict of interest All authors declare that they have no conflicts of interest. Conflict of interest References [1] Alsarhan H, Chia D, Christman A, et al (2016) A two-pass algorithm for unordered colored bin packing. Proceedings of the 9th International Conference on Discrete Optimization and Operations Research and Scientific School pp 1–10 [2] Balogh J, B´ek´esi J, Dosa G, et al (2013) Black and white bin packing. In: Erlebach T, Persiano G (eds) Approximation and Online Algorithms. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 131–144, https://doi.org/10.1007/978-3-642-38016-7_12 [3] Balogh J, B´ek´esi J, D´osa G, et al (2015) Offline black and white bin packing. Theoretical Computer Science 596:92–101. https://doi.org/10.1016/j.tcs.2015.06.045 [4] Belov G, Scheithauer G (2006) A branch-and-cut-and-price algorithm for one-dimensional stock cutting and two-dimensional two-stage cutting. European Journal of Operational Research 171(1):85–106. https://doi.org/10.1016/j.ejor.2004.08.036 [5] B¨ohm M, Sgall J, Vesel´y P (2015) Online colored bin packing. In: Bampis E, Svensson O (eds) Approximation and Online Algorithms. Springer International Publishing, Cham, pp 35–46, https://doi.org/10.1007/978-3-319-18263-6_4 [6] B¨ohm M, D´osa G, Epstein L, et al (2018) Colored bin packing: Online algorithms and lower bounds. Algorithmica 80(1):155–184. https://doi.org/10.1007/s00453-016-0248-2 [7] Borges Y, Schouery R (2020) Modelos pseudo polinomiais para o problema do empacotamento colorido. In: Anais do V Encontro de Teoria da Computa¸c˜ao, pp 37–40, https://doi.org/ 10.5753/etc.2020.11084, (In Portuguese) [8] Borges YGF, Schouery RCS, Miyazawa FK (2023) Mathematical models and exact algorithms for the colored bin packing problem. 2305.15291 [9] Brand˜ao F, Pedroso JP (2016) Bin packing and related problems: General arc-flow formulation with graph compression. Computers & Operations Research 69:56–67. https://doi.org/10. 1016/j.cor.2015.11.009 [10] Buljubaˇsi´c M, Vasquez M (2016) Consistent neighborhood search for one-dimensional bin packing and two-dimensional vector packing. Computers & Operations Research 76:12–21. https://doi.org/10.1016/j.cor.2016.06.009 [11] Carvalho JMVd (1999) Exact solution of bin-packing problems using column generation and branch-and-bound. Annals of Operations Research 86(0):629–659. https://doi.org/10. 1023/A:1018952112615 [12] Castelli M, Vanneschi L (2014) A hybrid harmony search algorithm with variable neighbourhood search for the bin-packing problem. 2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014) pp 1–6. https://doi.org/10.1109/NaBIC.2014. 6921849 [13] Chen J, Han X, Bein W, et al (2015) Black and white bin packing revisited. In: Lu Z, Kim D, Wu W, et al (eds) Combinatorial Optimization and Applications. Springer International Publishing, Cham, pp 45–59, https://doi.org/10.1007/978-3-319-26626-8_4 [14] Delorme M, Iori M (2020) Enhanced pseudo-polynomial formulations for bin packing and cutting stock problems. INFORMS Journal on Computing 32(1):101–119. https://doi.org/ 10.1287/ijoc.2018.0880 [15] Delorme M, Iori M, Martello S (2018) Bpplib: a library for bin packing and cutting stock problems. Optimization Letters 12(2):235–250. https://doi.org/10.1007/s11590-017-1192-z [16] Demˇsar J (2006) Statistical comparisons of classifiers over multiple data sets. The Journal of Machine learning research 7:1–30 [17] D´osa G, Epstein L (2014) Colorful bin packing. In: Ravi R, Gørtz IL (eds) Algorithm Theory – SWAT 2014. Springer International Publishing, Cham, pp 170–181, https://doi.org/10. 1007/978-3-319-08404-6_15 [18] Feo TA, Resende MG (1989) A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters 8(2):67–71. https://doi.org/https://doi.org/ 10.1016/0167-6377(89)90002-3 [19] Fleszar K, Hindi KS (2002) New heuristics for one-dimensional bin-packing. Computers & Operations Research 29(7):821–839. https://doi.org/10.1016/S0305-0548(00)00082-4 [20] Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32(200):675–701. https: //doi.org/10.1080/01621459.1937.10503522 [21] Gilmore PC, Gomory RE (1961) A linear programming approach to the cutting-stock problem. Operations Research 9(6):849–859. https://doi.org/10.1287/opre.29.6.1092 [22] Gonz´alez-San-Mart´ın J, Cruz-Reyes L, G´omez-Santill´an C, et al (2023) Comparative Study of Heuristics for the One-Dimensional Bin Packing Problem, Springer Nature Switzerland, Cham, pp 293–305. https://doi.org/10.1007/978-3-031-28999-6_19 [23] Gupta JND, Ho JC (1999) A new heuristic algorithm for the one-dimensional binpacking problem. Production Planning & Control 10(6):598–603. https://doi.org/10.1080/ 095372899232894 [24] Iman RL, Davenport JM (1980) Approximations of the critical region of the fbietkan statistic. Communications in Statistics - Theory and Methods 9(6):571–595. https://doi.org/10. 1080/03610928008827904 [25] Karp RM (1972) Reducibility among Combinatorial Problems, Springer US, Boston, MA, pp 85–103. https://doi.org/10.1007/978-1-4684-2001-2_9 [26] de Lima VL, Iori M, Miyazawa FK (2022) Exact solution of network flow models with strong relaxations. Mathematical Programming pp 1–34. https://doi.org/10.1007/ s10107-022-01785-9 [27] Loh KH, Golden B, Wasil E (2008) Solving the one-dimensional bin packing problem with a weight annealing heuristic. Computers & Operations Research 35(7):2283–2291. https:// doi.org/10.1016/j.cor.2006.10.021 [28] Mladenovi´c N, Hansen P (1997) Variable neighborhood search. Computers & Operations Research 24(11):1097–1100. https://doi.org/10.1016/S0305-0548(97)00031-2 [29] Nemenyi PB (1963) Distribution-free multiple comparisons. PhD thesis, Princeton University [30] Pessoa A, Sadykov R, Uchoa E, et al (2020) A generic exact solver for vehicle routing and related problems. Mathematical Programming 183(1):483–523. https://doi.org/10.1007/ s10107-020-01523-z [31] Vance PH (1998) Branch-and-price algorithms for the one-dimensional cutting stock problem. Computational Optimization and Applications 9(3):211–228. https://doi.org/10.1023/A: 1018346107246 [32] Wei L, Luo Z, Baldacci R, et al (2020) A new branch-and-price-and-cut algorithm for onedimensional bin-packing problems. INFORMS Journal on Computing 32(2):428–443. https://doi.org/10.1287/ijoc.2018.0867 This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Fast Neighborhood Search Heuristics for the Colorful Bin Packing Problem: Conclusion and References

Unhappy With Your Manager? Fix It Without Changing Jobs

Q1 2024 brought some fascinating shifts in the market, with certain sectors outperforming others. Here's a rundown of the most profitable crypto narratives that dominated the first quarter of 2024, along with their implications. And if you run a crypto/DeFi/NFT project and you need a piece of advice on marketing and community building, you can [message me](mailto: serge@x10.agency) directly. Here is a Q1 2024 breakdown: Memecoins: The Unexpected Champions Who would have thought that would take the crypto world by storm in Q1 2024? With an astonishing average return of 1,312.6% across the top 10 memecoins by market cap, these playful tokens left other narratives in the dust. memecoins The standout performers were BRETT, which skyrocketed by 7,727.6%, and the viral sensation dogwifhat (WIF), which surged an impressive 2,721.2%. Newcomers like BOOK OF MEME (BOME), cat in a dogs world (MEW), and Dogecoin20 (DOGE20) also gained significant traction, highlighting the explosive growth of the meme coin sector. This meteoric rise in memecoins outpaced the next best-performing crypto narrative, Real World Assets (RWA), by 4.6 times. Other narratives like AI, DeFi, and DePIN also saw moderate to strong gains, but none could match the sheer dominance of memecoins in Q1 2024. The surge in meme coin popularity attracted the attention of influential figures like Ethereum co-founder Vitalik Buterin, who sees the potential for these playful projects to contribute positively to the ecosystem and global community. However, the inherent volatility of memecoins remains a significant concern, as highlighted by a recent Solana meme coin pre-sale debacle. Nonetheless, the Q1 2024 performance of memecoins has been nothing short of remarkable, solidifying their status as the most profitable crypto narrative of the quarter. Memecoins demonstrate the power of community and virality in the crypto market. While they may be volatile and risky, their potential for massive returns has captured the attention of investors. Implications: Real-World Assets (RWAs): Solid Returns Coming in second place behind the meteoric rise of memecoins, generated an average return of 285.6% in Q1 2024. Real World Assets (RWAs) Standout Performers - MANTRA (OM): Surged 1,074.4%, solidifying its position as a leading RWA token. - TokenFi (TOKEN): Gained 419.7%, demonstrating the growing investor appetite for RWA-based crypto assets. Outlier Performance However, not all RWA tokens shared in the overall sector's success. XDC Network (XDC) bucked the trend, declining by 15.6% during the quarter. RWAs continue to show promise as they bridge the gap between traditional finance and the crypto world. Their strong performance indicates that investors are increasingly interested in tokens backed by real-world assets. However, This outlier performance serves as a reminder that, even within the promising RWA narrative, individual token performance can vary significantly based on factors such as project fundamentals, market conditions, and investor sentiment. Implications: AI Narrative: Triple-Digit Triumphs The secured the third-best performing crypto theme in Q1 2024, with an average gain of 222.0%. AI narrative Top AI Crypto Performers AIOZ Network (AIOZ): Soared by an impressive 480.2%, cementing its position as a leading AI-focused crypto project. Fetch.ai (FET): Climbed 378.3%, demonstrating the growing investor appetite for AI-powered blockchain solutions. The AI narrative's success highlights the growing importance of artificial intelligence in various industries. As AI continues to transform businesses and create new opportunities, the demand for AI-related cryptocurrencies is likely to increase. Implications: DeFi and DePIN: Moderate but Steady DeFi Narrative The achieved moderate returns of 98.9% in Q1 2024. This performance was partly driven by the Uniswap (UNI) fee switch proposal, which generated renewed investor interest in the DeFi sector. DeFi narrative DePIN Narrative The started the quarter in the red but finished strong with 81.0% gains. This category was led by the standout performances of: DePIN (Decentralized Physical Infrastructure Networks) narrative • Arweave (AR): A leading DePIN project that saw significant price appreciation. Livepeer (LPT): Another DePIN token that delivered strong returns during the quarter. Theta Network (THETA): A DePIN project that also contributed to the overall gains in this narrative. DeFi and DePIN show that decentralized finance and infrastructure solutions are here to stay. Despite facing regulatory challenges and market volatility, these sectors continue to grow and attract investors. Implications: Layer 1, Gaming, and Layer 2: Room for Improvement The Layer 1 (L1) narrative returned 70.0%, with Toncoin (TON) and Bitcoin Cash (BCH) as the top performers. saw respectable gains of 64.4%, while Layer 2 (L2) solutions lagged behind at 39.5%. Stacks (STX) and Mantle (MNT) were the strongest contenders in this category. GameFi While L1, gaming, and L2 solutions didn't perform as well as other narratives in Q1 2024, they still offer significant potential. Improvements in scalability, interoperability, and user experience could help these sectors catch up and thrive in the future. Implications: To draw a conclusion, the first quarter of 2024 was all about the memecoin revolution, with RWAs, AI, and DeFi also making significant strides. The comparatively lower performance of L2s and GameFi serves as a reminder of the challenges in capturing investor attention in the fast-paced and unpredictable crypto market. P.S. Check out my previous articles on AI at HackerNoon: NFT Market Trends In 2024 Meme Coin Marketing: The Ultimate Breakdown Why Everyone's Talking About Blast: The DeFi & Airdrop How To L aunch an NFT Project in 2024 AI Crypto Coins: When the Power of AI Meets the Blockchain DeFi Community Building: A Step-by-Step Guide for Crypto Startups

Top Crypto Trends and Stories of 2024 (so far)

In the era of widespread adoption of AI models, the data science community exhibits a rich diversity of backgrounds, fostering an environment conducive to innovation. Collaborative efforts stemming from this diversity continue to propel us toward the envisioned future of a decade or two ago. However, innovation often follows incremental patterns and may require decades before yielding tangible commercial benefits. Among industries adept at translating ideas into tangible products, automotive manufacturers stand out, boasting a deep familiarity with research and development processes and advanced algorithms. This proficiency is evident in the innovation pipeline for super and hypercars, drawing inspiration from advancements in rocket science. Leveraging extensive simulations and field tests, these manufacturers consistently push the boundaries of performance and safety in vehicle design. Major manufacturers have achieved breakthroughs with torque vectoring systems by capitalizing on established development pipelines and harnessing the capabilities of reinforcement learning. The late 2010s witnessed a surge in commercial vehicles featuring these systems, attributed partly to their association with electric and hybrid vehicles and partly to the appeal of enhanced safety. While the full extent of their influence on driver experience remains to be realized, these systems have already demonstrated remarkable efficacy, particularly in high-performance vehicles. So, what exactly is a torque vectoring system, and how is this innovation shaping the landscape of automotive manufacturing? What is Torque Vectoring In Torque Vectoring Systems (TVS), torque denotes a rotational force applied to a vehicle’s differentials or generated by independent electric motors on its wheels. These systems aim to regulate this rotational force to enhance speed, stability, handling, balance, and other performance aspects. A compelling illustration of the protracted nature of innovation is evident in the evolution of torque vectoring systems, with the first cars equipped with Torque Vectoring Systems (TVS) debuting in the 1990s, pioneered by Mitsubishi. Initially known as Active Yaw Control, Mitsubishi integrated this technology into vehicles like the 1996 Mitsubishi Lancer Evolution IV GSR models. The initial implementation relied on steering, throttle inputs, and g-force sensors to mitigate torque steer, a phenomenon where engine torque influences steering behavior. Initially geared towards racing applications, these systems have since evolved to cater to broader audiences, with companies like Rimac continuing to employ them for racing. In contrast, others emphasize safety and enhanced handling features for everyday drivers. Furthermore, advancements driven by the pursuit of eco-friendly transportation have led to the utilization of independent electric motors in torque vectoring systems, further expanding their applicability. Commercial Applications Ricardo (Honorable Mention) In 2006, Ricardo, a renowned engineering consultancy with over a century of experience, introduced the concept of torque vectoring. Ricardo rolled out its initial torque vectoring project centered around the Audi A6 4.2L, intending to license its technology to interested automakers. Unlike Mitsubishi’s Active Yaw Control system and similar counterparts, Ricardo’s torque vectoring approach prioritized safety and stability, showcasing a different aspect of the technology. Limited information is currently available on Ricardo’s involvement in assisting manufacturers in developing these systems for commercially available vehicles. However, they have publicly provided engineering support to Chinese students building cars for the global multi-event Formula SAE race in 2019. Rimac When discussing electric vehicles, Tesla typically takes the spotlight regarding market dominance. However, Rimac stands out as a company dedicated to crafting high-performance electric hypercars, where they excel. Just ask Elon… ( t) X pos Rimac burst onto the scene in January 2016 with their inaugural concept car, the Concept_One prototype. This marked the debut of their torque vectoring system (TVS) in an all-wheel-drive fully electric vehicle. Spearheaded by Tomislav Šimunić, Rimac’s TVS development pushed the boundaries by employing individual motors on each wheel. Moreover, they implemented reinforcement algorithms utilizing data from various sensors, including accelerometers, gyroscopes, steering angle, and wheel speed sensors. This data is then used to predict torque distribution to optimize vehicle stability. https://www.youtube.com/shorts/i4jVW29gbDc?embedable=true Currently, Rimac boasts one of the fastest and safest hypercars on the market. Their innovation has led to unprecedented control, resulting in their car achieving the title of the fastest production car ever in 21 acceleration and deceleration categories. Furthermore, their TVS is so reliable that their drivers can cruise hands-free at speeds exceeding 230 mph while enjoying gelato! Toyota In 2018, Toyota introduced their Dynamic Torque Vectoring AWD and Electric 4WD systems, expanding the scope of this technology beyond on-road safety and control to encompass off-road safety features and enhanced fuel efficiency. https://www.youtube.com/watch?v=LCqCa7PP1gE&embedable=true The video above showcases Toyota’s emphasis on “proactive torque transfer,” allowing for seamless adjustment of torque distribution when one or more wheels lose traction, enabling drivers to navigate various terrains more effectively. https://youtu.be/d1UG1a38diw?embedable=true Furthermore, Toyota optimizes torque distribution to enhance fuel-efficiency, shifting from AWD to 2WD when conditions permit, directing torque predominantly to the front wheels and thus cutting fuel consumption. Their limited content suggests the employment of a Dynamic Control System designed for stability, taking into account challenging driving scenarios. Moreover, the system can evaluate tire traction levels. These functionalities resonate with the research findings discussed below, showcasing the seamless transition from research concepts to practical implementation in the automotive industry. Research Review Conceptual Models — Bicycle, Vehicle, Tire Model Following Occam’s razor principle, simplicity reigns supreme when initiating modeling endeavors, with complexity introduced only when its benefits are worthwhile. This philosophy holds for TVS as well. Early TVS adopted a bicycle model, allowing researchers to streamline vehicle dynamics assumptions by confining movements to two wheels. This simplified framework facilitated the study of lateral vehicle dynamics during various events like braking, acceleration, and cornering under typical conditions. Key inputs into the bicycle model include yaw rate, lateral acceleration, steering angle, and velocity. Yaw rate denotes the vehicle’s rotation speed around its vertical axis, aiding in determining its orientation during maneuvers like cornering. Lateral acceleration captures sideways movements perpendicular to the vehicle’s direction of travel. Steering angle refers to the front wheel angle, typically in relation to the rear wheel positioning, while velocity represents the vehicle’s speed. For more details, check out this article explaining a simple bicycle model. https://dingyan89.medium.com/simple-understanding-of-kinematic-bicycle-model-81cac6420357?embedable=true Simplicity in modeling often comes with assumptions that may not hold, especially in challenging driving scenarios. Extreme conditions like rain, snow, or rough off-road terrain expose the limitations of the traditional bicycle model. The assumption of symmetry between the left and right wheels hinders the accurate analysis of lateral dynamics across all four wheels. To address this, researchers upgraded to a more comprehensive 4-wheel vehicle model. One such model, proposed in the 2023 journal article by Hui Deng, Youqun Zhao, Fen Lin, and Qiuwei Wang, boasts seven degrees of freedom. This expanded model captures all four wheels’ lateral, longitudinal, rotational, tilting, leaning, and spinning movements. However, it still falls short in capturing driving dynamics such as suspension behavior and tire-road interactions. Although suspension dynamics may be indirectly represented within the model, a separate tire model is necessary to capture tire-road interactions accurately. Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy & Safety² Tire models typically include parameters like tire force and slip angles. In the journal article by Michele Vignati, Edoardo Sabbioni, and Federico Cheli, researchers describe a simple tire model that incorporates lateral and longitudinal dynamics, slip angles, and slippage. Like the seven degrees of freedom model indirectly addresses suspension dynamics, the tire model considers slippage influenced by latent variables such as surface texture, conditions, and angles. This comprehensive approach allows researchers to analyze vehicle dynamics across various conditions and terrains, enhancing the torque vectoring system’s adaptability and performance. A Torque Vectoring Control for Enhancing Vehicle Performance in Drifting³ Control Schemes/Implementations In Torque Vectoring Systems (TVS) development, a significant portion of research revolves around employing Reinforcement Learning techniques, leveraging real-time data collected from various measurement devices. These solutions are predominantly implemented in all-wheel drive, four-wheel drive hybrid, and electric vehicles. Typically, the training of these systems takes place within simulated environments. However, in certain instances, researchers conduct road tests and utilize live test outcomes to validate simulation results. Torque Vectoring System Testing While specific details about testing TVS are often limited, researchers generally have three options: entirely computer-based simulations, hardware-in-the-loop testing combining simulation with real parts, and full-scale vehicle road tests. Computer-based simulations offer cost-effective evaluation of TVS with precise control over various driving conditions. Hardware-in-the-loop testing balances simulation and road tests, providing insights aligned with real-world performance. Although the most expensive, road tests offer the most reliable results by implementing TVS fully, enabling the most accurate evaluation of model performance. In the study by Shayan Taherian, Sampo Kuutti, Marco Visca, and Saber Fallah, computer-based simulations were utilized to test their RL algorithm across diverse environments. The researchers employed an adaptive algorithm based on Deep Deterministic Policy Gradient to update TVS control parameters as it learns from the environment. Testing across various velocity and friction environments allowed validation of their adaptive algorithm, particularly in high-speed/low-velocity scenarios. Self-adaptive Torque Vectoring Controller Using Reinforcement Learning⁴ Similarly, in the research by Wei Xu, Hong Chen, Haiyan Zhao, and Bingtao Ren, real-time Simulink simulations were leveraged to validate their model predictive controller. This controller predicts future system states and optimizes for multiple energy efficiency and safety objectives. Tests were conducted using an electric vehicle model with a 120 km/h velocity and an “adhesion coefficient” of .85, measuring tire friction between tire and ground. Torque Optimization Control for Electric Vehicles with Four In-Wheel Motors Equipped with Regenerative Braking System⁵ Powertrain & Fuel Types The specific problem addressed by torque vectoring systems heavily influences the powertrain choice and fuel types utilized in their implementations. Traditional gasoline and diesel vehicles necessitate mechanical implementations due to a single torque source, leading to complex mechanical differentials, challenges in power distribution, and overall less efficiency compared to hybrid and electric vehicle setups, which enable independent motor implementations. Similarly, All-Wheel Drive (AWD) and Four-Wheel Drive (4WD) powertrains offer superior advantages over other options. These configurations enhance stability, versatility, and energy efficiency. The work of Jeongmin Cho and Kunsoo Huh, in their study , underscores the stability and versatility benefits of AWD vehicles. By distributing power to all four wheels, AWD enhances the effectiveness of torque vectoring systems, granting increased control over each wheel. This translates to improved vehicle dynamics during cornering, acceleration, and deceleration. Torque Vectoring System Design for Hybrid Electric–All-Wheel Drive Vehicle⁶ Meta Control Algorithms In Reinforcement Learning (RL), an agent decides actions based on the current situation to maximize a reward function. It learns which actions yield the highest rewards by assessing the outcomes of its actions. In torque vectoring algorithms, auto manufacturers define different reward functions based on the specific goals of their TVS, which vary depending on the type of vehicles used, such as sports cars versus mini-vans. These goals include increased stability, controllability, fuel economy, cornering performance, and optimizing various vehicle dynamics behaviors. One example of a multi-objective implementation is outlined in the paper by Jae-Young Park, Seung-Jin Heo, and Daeoh Kang. Their algorithm features a supervisory controller that selects between an “agile” mode and a “safe” mode based on vehicle and driving conditions. Agile mode prioritizes driver controllability, while safe mode focuses on stability. Controllability refers to the synchronization between driver steering input and vehicle direction, while stability relates to the vehicle’s resistance to deviations or disturbances. Inputs such as vehicle speed, steering angle, yaw rate, lateral acceleration, and wheel slip guide the supervisory controller’s decision-making process. Development of Torque Vectoring Control Algorithm for Front Wheel Driven Dual Motor System and Evaluation of Vehicle Dynamics Performance⁷ Another example is presented in the paper by Hui Deng, Youqun Zhao, Fen Lin, and Qiuwei Wang. Here, the researchers optimize for safety, economy, and driver load. Economy involves assessing motor efficiency and energy savings compared to vehicles without torque vectoring systems. Safety focuses on vehicle stability, while driver load considers the physical and cognitive load based on the correlation between the driver’s steering wheel angle and the vehicle’s longitudinal acceleration. Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety⁸ The algorithm includes an active safety controller and a torque allocation layer. The active safety controller calculates total longitudinal acceleration and additional “yaw moment” values based on driving conditions, applying safety and driver load penalties. These values are then fed to the torque allocation layer, which distributes torque to all four wheels. Additionally, the researchers implement a tire model using a Fibonacci tree optimization algorithm and a vehicle model with 7 degrees of freedom to enhance system performance. The , research done by Park et al., aims to enhance the driver’s experience through improved smoothness and convergence. Their TVS, designed for commercial application, underwent rigorous validation through real vehicle experiments conducted under various conditions. Torque Vectoring Algorithm of Electronic-Four-Wheel Drive Vehicles for Enhancement of Cornering Performance⁹ The researchers implemented a smooth sliding controller, GPS, and sensor inputs to generate desired yaw moment values. These values are then utilized by the torque allocation algorithm, which employs a daisy-chaining allocation method to distribute torque efficiently. The goal is to optimize the convergence from current yaw moment values to target values, thereby reducing the instability during this transition. Conclusion The evolution of torque vectoring systems underscores the transformative power of innovation within the automotive industry. From pioneering beginnings to cutting-edge applications, these systems have revolutionized vehicle dynamics, speed, stability, and handling. As automotive manufacturers continue to push the boundaries of technology and leverage advancements in advanced algorithms, the future holds promise for further improvements in safety, performance, and driver experience. However, hurdles remain in integrating this technology into internal combustion engine vehicles, and ongoing research and development efforts are required to overcome them. With continued dedication to innovation, torque vectoring systems are poised to shape the future landscape of automotive manufacturing, driving us toward a new era of mobility. Research Reference Martin Dendaluce Jahnke et al., “Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles,” 8, no. 2 (February 22, 2019): 250 Electronics Huifan Deng et al., “Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety,” 11, no. 4 (April 6, 2023): 459 Machines Michele Vignati, Edoardo Sabbioni, and Federico Cheli, “A Torque Vectoring Control for Enhancing Vehicle Performance in Drifting,” 7, no. 12 (December 5, 2018): 394 Electronics Shayan Taherian et al., “Self-Adaptive Torque Vectoring Controller Using Reinforcement Learning,” , September 19, 2021 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) Wei Xu et al., “Torque Optimization Control for Electric Vehicles with Four In-Wheel Motors Equipped with Regenerative Braking System,” 57 (February 2019): 95–108 Mechatronics Jeongmin Cho and Kunsoo Huh, “Torque Vectoring System Design for Hybrid Electric–All Wheel Drive Vehicle,” 234, no. 10–11 (April 7, 2020): 2680–92 Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering Jae-Young Park, Seung-Jin Heo, and Dae-Oh Kang, “Development of Torque Vectoring Control Algorithm for Front Wheel Driven Dual Motor System and Evaluation of Vehicle Dynamics Performance,” 21, no. 5 (October 2020): 1283–91 International Journal of Automotive Technology Huifan Deng et al., “Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety,” 11, no. 4 (April 6, 2023): 459 Machines Giseo Park et al., “Torque Vectoring Algorithm of Electronic-Four-Wheel Drive Vehicles for Enhancement of Cornering Performance,” 69, no. 4 (April 2020): 3668–79 IEEE Transactions on Vehicular Technology

Driving Innovation: The Science of Torque Vectoring Systems

Today, we're diving deep into the world of anchoring, a cognitive bias that influences our choices far more pervasively than we realize. If you’ve heard of Anchors before, you’ve probably heard it in a sales context. I’ll touch on that, but it goes way deeper than this. Understanding Anchoring isn't just about dodging sneaky sales tactics; it's about understanding how our brains construct reality from imperfect information. Anchors Aweigh: What Is This Thing, Anyway? Picture this: You're browsing for a new jacket. The first one you see is beautiful, but it's a whopping $400. Way out of your budget. However, as you wander through the store, every other jacket now seems like a relative bargain. Suddenly, that $150 option looks downright appealing. That initial $400 price tag served as an anchor. It distorted your perception of what constitutes a "reasonable" price for a jacket. This mental glitch, my friends, is the anchoring effect in action. Another (non sales, example), was shown that if people are first asked whether Gandhi died before or after age 9, and are then asked to guess Gandhi's actual age at death, their guesses will be lower than if they had first been asked whether Gandhi lived past age 200. The first anchor of 9 or 200 pulls their final estimate in that direction, despite it having no logical relevance. The anchoring bias describes our mind's tendency to become overly influenced by the first piece of information (the anchor) it receives on a topic. Once that anchor's dropped, subsequent judgments get made relative to it, even if the initial anchor is totally arbitrary or irrelevant. Anchoring in the Wild Anchoring isn't just about shopping sprees and impulse buys. It permeates all sorts of life decisions: The person who throws out a number first (salary, sale price, you name it) often has an advantage because it sets the psychological stage for the rest of the negotiation. Negotiations: Get too fixated on the price you originally paid for a stock? You might miss signs it's time to sell, even if the fundamentals have changed (that's sunk cost fallacy, a close cousin of anchoring). Investing: From job interviews to dating, that initial impression (positive or negative) can color how people perceive everything else you do. First Impressions: Anchors: The Cognitive Comfort Blanket Think of an anchor as a mental shortcut. Our brains crave stability and order – they don't enjoy floating around in a sea of uncertainty. Anchors, even irrelevant ones, give us a point of reference, a starting line from which to judge everything else. The tricky part? Once an anchor is planted in our minds, it has a cascading effect: We start noticing details that confirm the established anchor and downplay information that contradicts it. This is a form of confirmation bias. Selective Attention: An anchor can make something seem outrageously expensive or like an incredible deal, even if the objective value remains unchanged. Our point of reference becomes skewed. Distorted Valuation: Anchors don't just affect isolated decisions. They can have a long-term influence on our spending habits, expectations, and even our beliefs about the world. The Ripple Effect: Why Are Our Brains So Easily Swayed? Researchers have a few theories: Our brains are cognitive misers—they love shortcuts. Instead of thoroughly evaluating every decision from scratch, referencing an anchor is a quick, albeit imperfect, decision-making hack. Effort Reduction: When clueless about a fair price or course of action, any initial data point seems better than none. The anchor gives us some place to start. Uncertainty: The World is Your Anchor Playground Let's be real, anchors are everywhere: That initial "Free" frames future pricing decisions. Suddenly the monthly subscription feels way less appealing, even if the total cost is reasonable. Companies know this. The "Free Trial" Trap: Have you noticed how tip options on those card readers keep getting higher? They're anchoring your perception of what constitutes a "normal" tip amount. The Power of Suggestion: The first opinion you hear on a controversial topic, or even just the initial word choice ("riot" vs. "protest"), can shape how you process subsequent information. Anchoring in the Social Realm: The Achilles Heel of Anchoring The most insidious thing about anchors is that they work even when we KNOW they exist. Studies show that even when people are explicitly warned about anchoring bias, it still influences their estimation and judgment. Why does this happen? Here's where things get interesting: System 1 thinking (fast, intuitive) is easily swayed by anchors. But while System 2 (slow, deliberate reasoning) can recognize the bias, it takes significant effort to fully counteract its effects. Anchors Tap Into Deeper Thinking: Our brains are wired to assess things comparatively. Even if you intellectually recognize that an anchor is arbitrary, your emotional response is primed to the comparison. Relative, Not Absolute: Anchors create a sense of cognitive fluency. Options that feel familiar or easy to process often seem intuitively correct, even if they aren't the most rational choice. The Fluency Factor: Fight Back Against the Invisible Hand So, are we doomed to be cognitive puppets manipulated by random anchors? Not quite. Here's your toolkit: Impulse buys are the best friend of anchoring. Give yourself time. Before reacting to a price, offer, or information, impose a self-mandated waiting period. That pause gives your logical brain space to catch up. The Art of Delayed Gratification: Never rely on a single data point. Actively seek out alternative pricing, multiple opinions, or different perspectives on an issue. This helps break the spell of the initial anchor. Seek Counter-Anchors: Especially in negotiations, sometimes it pays to be the one who avoids stating the first number. Strategic ambiguity shifts the dynamic in your favor. Embrace Strategic Ambiguity: Beyond the Obvious – Anchoring in Unexpected Places Anchoring isn't just a salesperson's trick. It seeps into areas where we least expect it, subtly shaping our choices, opinions, and even our self-perception: Ever catch yourself longing for "the good old days" when things seemed cheaper, simpler, or better? Those nostalgic memories can become anchors, making us resistant to change or less receptive to the advantages of the present. Anchoring to the Past: Our initial impressions of ourselves (in childhood through early adulthood) have incredible staying power. That old "I'm bad at math" label can linger for decades, leading to missed opportunities because we're anchored to a past limitation. The Self-Perception Anchor: Studies suggest that even something as trivial as the severity of a sentencing demand by the prosecution can unconsciously anchor the judge's ultimate sentence, regardless of the actual evidence. Anchoring in the Courtroom: Anchors: A Double-Edged Sword It's important to remember, anchoring isn't inherently good or bad. Our brains evolved this shortcut for a reason. Here's where it can be helpful: Ever heard the advice to "visualize success"? It's partly an anchoring effect. Setting a high initial goal (sales revenue, fitness target, creative output) creates a reference point that can motivate us, even if we don't fully reach the first ambitious mark. Goal Setting as Anchoring: Consciously reframing negative thought patterns with positive affirmations can be a form of beneficial self-anchoring. Over time, it helps shift your internal reference point. Positive Self-Talk: Charities that display suggested donation amounts or non-profits highlighting the impact of even small contributions are leveraging anchoring principles to nudge people towards greater generosity. Anchors for Social Good: The Ultimate Goal: Cognitive Flexibility It's not about eliminating anchors – they're an inevitable part of how our brains work. The key is becoming mindful of when they're operating, knowing when to deliberately set our own anchors, and developing the flexibility to shift our reference points when necessary. Understanding anchoring is a step towards greater objectivity, better decision-making, and a healthy skepticism towards the hidden forces trying to shape our reality. Let's get meta - could this newsletter be one big anchor to get you thinking more critically about decision-making? Absolutely 😉 Scott Also appears . here

Unveiling the Anchors: How Your Brain Makes Sense of the World (And Gets It Wrong)

অ্যাঙ্করগুলি উন্মোচন করা: কীভাবে আপনার মস্তিষ্ক বিশ্বের অনুভূতি তৈরি করে (এবং এটি ভুল হয়ে যায়)

Die Anker enthüllen: Wie Ihr Gehirn die Welt versteht (und es falsch versteht)

Revelando las anclas: cómo su cerebro le da sentido al mundo (y lo entiende mal)

Dévoiler les ancres : comment votre cerveau donne un sens au monde (et se trompe)

एंकरों का अनावरण: आपका मस्तिष्क दुनिया को कैसे समझता है (और इसे गलत बनाता है)

アンカーを明らかにする: あなたの脳は世界をどのように理解するのか (そして間違ってしまうのか)

앵커 공개: 뇌가 세상을 어떻게 이해하고 잘못 이해하는지

Revelando as âncoras: como seu cérebro dá sentido ao mundo (e erra)

Раскрытие якорей: как ваш мозг воспринимает мир (и делает это неправильно)

Çapaların Açıklanması: Beyniniz Dünyayı Nasıl Anlıyor (Ve Yanlış Anlıyor)

Tiết lộ những điểm neo: Bộ não của bạn nhận thức thế giới như thế nào (và hiểu sai)

揭开锚点：你的大脑如何理解世界（以及理解错误）

This paper is available on arxiv under CC 4.0 license. Authors: (1) RITA S. P. MACIEL, Federal University of Bahia, Brazil; (2) PEDRO H. VALLE, Federal University of Juiz de Fora, Brazil; (3) KÉCIA S. SANTOS, Federal University of Bahia, Brazil; (4) ELISA Y. NAKAGAWA, University of São Paulo, Brazil. Table of Links Introduction Related Work Research Method Results Discussions Conclusions & References 3 RESEARCH METHOD We performed the three-phase process — planning, execution, and results synthesis [50] — as presented in Figure 1. Sections 3.1 and 3.2 summarize the planning and execution phases respectively, while Section 4 presents the results. 3.1 Planning The main elements of the tertiary study protocol are the study goal, research questions (RQs), search string, selected databases, and studies selection criteria. Concerning the goal, we used GQM (Goal Question Metric) to define it. The goal is to analyze secondary studies of SIS interoperability for the purpose of identifying the panorama with respect to interoperability types, models, frameworks, domains, and solutions from the point of view of researchers in the context of academic literature. We also defined three RQs, as listed below: • RQ1: Which interoperability types have been addressed? Rationale: Several interoperability types exist, but without an updated and complete panorama of them or a compilation of their definitions/understandings or classification • RQ2: Which are the existing interoperability models and frameworks? Rationale: Interoperability models and frameworks can show how interoperability types can be organized and related to each other. Besides, a complete view of these models and frameworks is missing. • RQ3: Which are the domains and their related solutions for interoperability? Rationale: Interoperability solutions can support the interoperability achievement in SIS in those domains (i.e., the contexts where interoperability is addressed by the studies). After several calibrations through pilot studies, the resulting search string was: (“interoperability” OR “interoperabiliting” OR “interoperable”) AND (“review of studies” OR “structured review” OR “literature review” OR “literature analysis” OR “in-depth survey” OR “literature survey” OR “meta-analysis” OR "systematic mapping"). We also selected the most recommended publications databases for conducting literature reviews: Scopus[8] , ACM DL, and IEEE Xplore. For the studies selection, we defined two inclusion criteria (IC) and six exclusion criteria (EC): • IC1: Study addresses interoperability in SIS. • IC2: Study addresses interoperability types. • EC1: Study does not address interoperability in SIS. • EC2: Study does not address interoperability types. • EC3: Study is not a literature review. • EC4: The full text of the study is not available. • EC5: Study is an older version of another study. • EC6: Study is not written in English. We also applied the quality assessment of studies due to our tertiary study intends to present a panorama of all secondary studies, and such assessment can drawn an overall view of the quality of these studies. For this, we followed the recommendation of Kitchenham et al. [49] by using DARE-4 criteria[9] ; hence, the questions for the quality assessment (QA) were: • QA1: Were the review’s selection criteria (inclusion and exclusion criteria) explicitly defined? • QA2: Was the literature search likely to have covered all relevant studies? • QA3: Did the reviewers assess the quality of the included studies? • QA4: Was the basic studies’ information adequately described? Each study was scored as follows: QA1 (Yes: the selection criteria were explicitly defined; Partly: the selection criteria were implicit; or No: the selection criteria were not defined and were not possible to be inferred); QA2 (Yes: the authors either searched in four or more databases and included additional search strategies or identified and referenced all journals addressing the topic of interest; Partly: the authors searched in three or four databases with no extra search strategies, or searched a defined but restricted set of journals and conference proceedings; or No: the authors searched up to two databases or a highly restricted set of journals); QA3 (Yes: the authors explicitly defined quality criteria and applied them to each primary study; Partly: quality criteria and application procedure were implicit; or No: no explicit quality assessment of primary studies was presented); and QA4 (Yes: information about each study was presented; Partly: only summarized information was presented; or No: no information was presented). Moreover, the numerical scores were Yes = 1, Partly = 0.5, No = 0, or Unknown (the information is not specified). 3.2 Execution and Update We searched for studies in the databases in May 2022, following the protocol rigorously. To do that, we configured the search string to each database’s search engine and retrieved studies based on titles, keywords, and abstracts. We focused on studies published in the last ten years (2012 to 2022), similar to most tertiary studies in the literature. We retrieved a total of 494 studies from the databases and after duplicate removal, 414 unique studies remained. During the studies selection process, we deeply examined each study, applied the selection criteria, and selected 35 studies that were relevant to answer our RQs. Next, we applied the quality assessment; for that, the two first authors evaluated the studies separately, and the third and fourth ones came together and solved divergences. As we finished the deep analysis of each study and results summarization in December 2022, we decided to update our tertiary study considering the period of 2022 until February 2023, as illustrated in Figure 1. We identified two new studies published in 2023. Hence, our tertiary studies selected a total of 37 studies published from January 2012 to February 2023. [8] https://www.scopus.com [9] https://web.archive.org/web/20070918200401/https://www.york.ac.uk/inst/crd/faq4.htm This paper is available on under CC 4.0 license. Arxiv

System Interoperability Types: A Tertiary Study: The Research Method That We Used and How We Used It

Partly

partly.com

51-200 employees

Since 2020

COMPANY RANKING

PARTLY

EVERGREEN INDEX #146

Partly WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

Partly

partly.com

51-200 employees

Since 2020

COMPANY RANKING

PARTLY

EVERGREEN INDEX #146

RELATED COMPANIESRANK

STORIES ABOUT PARTLY

Partly WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!