13 Cybercrime Facts That Will Give You Chills

13 факта за киберпрестъпността, които ще ви побият тръпки

13টি সাইবার অপরাধের তথ্য যা আপনাকে শান্ত করবে

13 hechos sobre delitos cibernéticos que te pondrán los pelos de punta

Hotzikara emango dizuten ziberkrimenaren 13 gertakari

ゾッとするサイバー犯罪の13の事実

13 ការពិតអំពីឧក្រិដ្ឋកម្មតាមអ៊ីនធឺណិតដែលនឹងផ្តល់ឱ្យអ្នកនូវភាពត្រជាក់

13 Fakta Jenayah Siber Yang Akan Menggigil Anda

13 faktów na temat cyberprzestępczości, które wywołają u Ciebie dreszcze

13 fatos sobre crimes cibernéticos que vão te dar arrepios

Sizi Tüyler Ürpertecek 13 Siber Suç Gerçeği

13 Sự thật về tội phạm mạng sẽ khiến bạn rùng mình

Iinyani ezili-13 zoBugebenga be-Cyberculos eziya kukunika ukuzola

Everything You Need to Know About Zero-Knowledge Proof (ZKP) Technology

One of my favorite interview questions is “What do such words as async and await tell you?” because it opens up an opportunity to have an interesting discussion with an interviewee… Or it doesn’t because they float on this topic. In my opinion, it is drastically important to understand why we use this technique. I feel like many developers prefer to rely on the “it is the best practice” statement and use asynchronous methods blindly. This article shows the difference between asynchronous and synchronous methods in practice. Tools .NET Web API application (test target) 2 Azure SQL Databases 2 Azure App Service on Windows (hosts the application) Azure App Insights (to gather metrics) locust framework (to simulate user load). Configuration I will run a benchmark in the following way. Two independent locust instances are running on two machines. Locust instances simulate a user that does the following: The user from the locust host 1 hits the synchronous endpoint of the App Service 1, gets the response, and stays idle for 0.5–1 seconds (the exact time delay is random). Repeats till the end of the experiment. The user from the locust host 2 behaves precisely the same, with only one difference — he hits the asynchronous endpoint of App Service 2. Under the hood, each App Service connects to its own database and executes a SELECT query that takes five seconds and returns a few rows of data. See the controller’s code below for references. I’ll use Dapper to make a call to the database. I’d like to draw your attention to the fact that the asynchronous endpoint calls the database asynchronously as well (QueryAsync<T>). It's worth adding that I deploy the same code to both app services. During the test, the number of users grows evenly to the target number (Number of Users). The speed of growth is controlled by a Spawn Rate parameter (number of unique users to join per second) — the higher the number is, the quicker the users are being added. The spawn rate is set to 10 users/s for all experiments. All experiments are limited to 15 minutes. You may find machine configuration details in the Technical Details section of the article. Metrics requests per minute — shows the number of requests that the application actually processed and returned a status code.
thread count — shows the number of threads the app service consumes.
median response time, ms The red lines refer to asynchronous, and the blue lines — to the synchronous endpoint, respectively. That’s it about theory. Let’s start. Experiment #1 number of users: 75 (per service) We can see that both endpoints perform similarly — handling around 750 requests per minute with a median response time of 5200 ms. The most fascinating graph in this experiment is a thread trend. You can see significantly higher numbers for the synchronous endpoint (a blue graph) — more than 100 threads! That’s expected, though, and matches the theory — when a request comes in, and the application makes a call to the database, the thread is blocked because it has to wait for a roundtrip to complete. Therefore, when another request comes in, the application has to produce a new thread to handle it. The red graph — the asynchronous endpoint thread count— proves different behavior. When a request comes in and the application makes a call to the database, the thread returns to a thread pool instead of being blocked. Therefore, when another request comes in, this free thread is reused. Despite incoming requests growing, the application doesn’t require any new threads, so their count remains the same. It’s worth mentioning the 3rd metric — median response time. Both endpoints showed the same result — 5200 ms. So, there is no difference in terms of performance. Now, it is time to pull up the stakes. Experiment #2 number of users: 150 We doubled the load. The asynchronous endpoint handles this task successfully—its request per minute rate floats around 1500. The synchronous brother eventually reached a comparable number of 1410. But if you look at the graph below, you will see that it took 10 minutes! The reason is that the synchronous endpoint reacts to a new user's arrival by creating another thread, but users are being added to the system (just to remind you that the Spawn Rate is 10 users/s) quicker than the web server can adapt. That’s why it queued so many requests at the very beginning. Unsurprisingly, the thread count metric is still around 34 for the asynchronous endpoint, while it increased from 102 to 155 for the synchronous one. The median response time degraded similarly to the request per minute rate — the synchronous response time was much higher at the beginning of the experiment. If I had kept the test for 24 hours, the median numbers would become even. Experiment #3 number of users: 200 The third experiment is intended to prove trends revealed during the second one —we can see further degradation of the synchronous endpoint. Conclusion Using asynchronous instead of synchronous operations doesn’t directly improve performance or user experience. First, it enhances stability and predictability under pressure. In other words, it raises the load threshold so the system can process more before it degrades. Appendix #1. Technical Details Azure App Service: B1, 100 ACU, 1,75 Gb Memory, A-Series compute equivalent.
Azure SQL Database: Standard S4: 200 DTUs, 500 Mb storage.
SQL Connection settings: Max Pool Size=200. Appendix #2. Notes To achieve the cleanest test result, I should have run tests from 2 VMs located in the same network where the target App Services sit. However, I assumed that a network lag would impact both apps in a more or less similar way. Therefore, it cannot jeopardize the main goal — comparing how asynchronous and synchronous methods behave. Appendix #3. Bonus Experiment What did I hack to force the synchronous endpoint to perform almost as asynchronous and plot the graph below (experiment conditions are the same as in the third one — 200 users)? One of my favorite interview questions is “What do such words as async and await tell you?” because it opens up an opportunity to have an interesting discussion with an interviewee… Or it doesn’t because they float on this topic. In my opinion, it is drastically important to understand why we use this technique. async await I feel like many developers prefer to rely on the “it is the best practice” statement and use asynchronous methods blindly. This article shows the difference between asynchronous and synchronous methods in practice. Tools .NET Web API application (test target) .NET Web API application (test target) 2 Azure SQL Databases 2 Azure SQL Databases 2 Azure App Service on Windows (hosts the application) 2 Azure App Service on Windows (hosts the application) Azure App Insights (to gather metrics) Azure App Insights (to gather metrics) locust framework (to simulate user load). locust framework (to simulate user load). locust Configuration I will run a benchmark in the following way. Two independent locust instances are running on two machines. Locust instances simulate a user that does the following: The user from the locust host 1 hits the synchronous endpoint of the App Service 1, gets the response, and stays idle for 0.5–1 seconds (the exact time delay is random). Repeats till the end of the experiment. The user from the locust host 1 hits the synchronous endpoint of the App Service 1, gets the response, and stays idle for 0.5–1 seconds (the exact time delay is random). Repeats till the end of the experiment. synchronous App Service 1, The user from the locust host 2 behaves precisely the same, with only one difference — he hits the asynchronous endpoint of App Service 2. The user from the locust host 2 behaves precisely the same, with only one difference — he hits the asynchronous endpoint of App Service 2. asynchronous App Service 2. Under the hood, each App Service connects to its own database and executes a SELECT query that takes five seconds and returns a few rows of data. See the controller’s code below for references. I’ll use Dapper to make a call to the database. I’d like to draw your attention to the fact that the asynchronous endpoint calls the database asynchronously as well ( QueryAsync<T> ). QueryAsync<T> It's worth adding that I deploy the same code to both app services. During the test, the number of users grows evenly to the target number ( Number of Users ). The speed of growth is controlled by a Spawn Rate parameter (number of unique users to join per second) — the higher the number is, the quicker the users are being added. The spawn rate is set to 10 users/s for all experiments. Number of Users Spawn Rate spawn rate All experiments are limited to 15 minutes. You may find machine configuration details in the Technical Details section of the article. Metrics requests per minute — shows the number of requests that the application actually processed and returned a status code. thread count — shows the number of threads the app service consumes. median response time, ms requests per minute — shows the number of requests that the application actually processed and returned a status code. requests per minute thread count — shows the number of threads the app service consumes. thread count median response time, ms median response time, ms The red lines refer to asynchronous, and the blue lines — to the synchronous endpoint, respectively. That’s it about theory. Let’s start. Experiment #1 Experiment #1 number of users: 75 (per service) number of users : 75 (per service) number of users We can see that both endpoints perform similarly — handling around 750 requests per minute with a median response time of 5200 ms. The most fascinating graph in this experiment is a thread trend. You can see significantly higher numbers for the synchronous endpoint (a blue graph) — more than 100 threads! That’s expected, though, and matches the theory — when a request comes in, and the application makes a call to the database, the thread is blocked because it has to wait for a roundtrip to complete. Therefore, when another request comes in, the application has to produce a new thread to handle it. The red graph — the asynchronous endpoint thread count— proves different behavior. When a request comes in and the application makes a call to the database, the thread returns to a thread pool instead of being blocked. Therefore, when another request comes in, this free thread is reused. Despite incoming requests growing, the application doesn’t require any new threads, so their count remains the same. It’s worth mentioning the 3rd metric — median response time . Both endpoints showed the same result — 5200 ms. So, there is no difference in terms of performance. median response time Now, it is time to pull up the stakes. Experiment #2 number of users: 150 number of users : 150 number of users We doubled the load. The asynchronous endpoint handles this task successfully—its request per minute rate floats around 1500. The synchronous brother eventually reached a comparable number of 1410. But if you look at the graph below, you will see that it took 10 minutes! request per minute The reason is that the synchronous endpoint reacts to a new user's arrival by creating another thread, but users are being added to the system (just to remind you that the Spawn Rate is 10 users/s) quicker than the web server can adapt. That’s why it queued so many requests at the very beginning. Spawn Rate Unsurprisingly, the thread count metric is still around 34 for the asynchronous endpoint, while it increased from 102 to 155 for the synchronous one. The median response time degraded similarly to the request per minute rate — the synchronous response time was much higher at the beginning of the experiment. If I had kept the test for 24 hours, the median numbers would become even. thread count median response time request per minute Experiment #3 number of users: 200 number of users : 200 number of users The third experiment is intended to prove trends revealed during the second one —we can see further degradation of the synchronous endpoint. Conclusion Using asynchronous instead of synchronous operations doesn’t directly improve performance or user experience. First, it enhances stability and predictability under pressure. In other words, it raises the load threshold so the system can process more before it degrades. Appendix #1. Technical Details Azure App Service: B1, 100 ACU, 1,75 Gb Memory, A-Series compute equivalent. Azure SQL Database: Standard S4: 200 DTUs, 500 Mb storage. SQL Connection settings: Max Pool Size=200. Azure App Service: B1, 100 ACU , 1,75 Gb Memory, A-Series compute equivalent. ACU Azure SQL Database: Standard S4: 200 DTUs, 500 Mb storage. SQL Connection settings: Max Pool Size=200. Appendix #2. Notes To achieve the cleanest test result, I should have run tests from 2 VMs located in the same network where the target App Services sit. However, I assumed that a network lag would impact both apps in a more or less similar way. Therefore, it cannot jeopardize the main goal — comparing how asynchronous and synchronous methods behave. Appendix #3. Bonus Experiment Appendix #3. Bonus Experiment What did I hack to force the synchronous endpoint to perform almost as asynchronous and plot the graph below (experiment conditions are the same as in the third one — 200 users)?

Async vs Sync Benchmark (.NET): The Difference Between Asynchronous and Synchronous Methods

مقارنة بين Async وSync (.NET): الفرق بين الطرق المتزامنة وغير المتزامنة

Async vs Sync Benchmark (.NET): Asinxron və Sinxron Metodlar Arasındakı Fərq

Async vs Sync Benchmark (.NET): la diferencia entre métodos asincrónicos y sincrónicos

Async vs Sync Benchmark (.NET): a diferenza entre os métodos asíncronos e síncronos

Async vs Sync Benchmark (.NET): Az aszinkron és a szinkron módszerek közötti különbség

Async vs Sync Benchmark (.NET): la differenza tra metodi asincroni e sincroni

非同期 vs 同期ベンチマーク (.NET): 非同期メソッドと同期メソッドの違い

Async vs Sync Benchmark (.NET)៖ ភាពខុសគ្នារវាងវិធីសាស្ត្រ Asynchronous និង Synchronous Methods

Async vs Sync Benchmark (.NET): Асинхрондуу жана синхрондуу методдордун ортосундагы айырма

Async vs Sync Benchmark (.NET): różnica między metodami asynchronicznymi i synchronicznymi

Async vs Sync Benchmark (.NET): Rozdiel medzi asynchrónnymi a synchrónnymi metódami

Async vs Sync Benchmark (.NET): Musiyano uripakati peAsynchronous uye Synchronous Methods

What do you think of the whole AI trend taking the internet by storm right now? If you're like most people I've talked to recently, you're somewhat wary of it. But there's a lot of silver lining to the AI cloud steadily advancing over the internet that I want to point out, and I thought I should let you know how I'm using it going forward in my work as an Applied Neuroscience Strategist. Let's Start With the Negatives... AI is going to exacerbate the enshittification of the content realm of the internet. AI is rocket fuel for the content farms of the internet that pump out poorly researched articles and videos about everything under the sun. On your YouTube recommended page and in your search results, expect a lot more content of dubious veracity. You should now have a heightened sense of skepticism about everything hear online. YouTube was ground zero for the enshittification; with their censorship policies, their pushing content creators into doing low-effort streaming, and their algorithmic boosting of brain-deadening shorts. Expect YouTube to become just flooded with AI-generated content; be skeptical of faceless channels, look for channels run by actual people who appear on camera and have a broader internet presence. AI is going to eat up a lot of jobs in the IT and creative fields. Once upon a time, I was a nightclub promoter, and we paid a pretty penny to graphic designers in Denver to create flyers for our weekly events. Now, AI can do that at a tiny fraction of the cost. Programmers, coders, and web developers now need to worry about AI replacing them. Many predict that this will be a massive driver of technological unemployment, necessitating a universal basic income. I'm not so pessimistic about the future; in the not-so-distant future, I see Bitcoin arising as the global reserve currency; I argue that Bitcoin will make civilization sane again; the satanically-inversed, money-as-debt, fiat banking system has and is harming the economy so much more than AI ever will. Freed of it, the downstream positives are hard to fathom. AI will accelerate the digital enslavement of human sexuality. I expect even more lost generations of men to lose themselves in the mire of the pixel-powered pleasure world. The only solution to this is the personal one, take back your sexual sovereignty from the digital sea of siren simulacrums drawing you toward a life of quiet desperation. A Tantra practice is a good place for men to start that reconquista! The silver lining on this, as I pointed out in a Twitter thread a while back, is that AI is going to compete aggressively with the sad mass of women who have become digital sex workers on OnlyFans. The majority of them simply won't be able to compete with AI and will hopefully turn to a more decent way to make their living. https://x.com/JRoseland/status/1650830140505481218?embedable=true Will AI begin editing our genes and eventually transform us into slave species? I had the pleasure of debating this with one of the greatest minds on the internet who wrote an amazing book on the topic. If it does, it will be thousands of years in the future, and in the interim, our species will enjoy an enlightened golden age that the greatest science fiction authors could only fathom on DMT. That golden age will present ample opportunity for the planetary decentralization of humanity, ensuring its survival. The cosmos will be pregnant with the brilliance of our progeny. https://rumble.com/vc4acq-jonathan-roseland-on-the-revolutionary-phenotype-the-public-space-344.html?embedable=true The Positives AI is going to break the monopoly on good movies and television long-held largely by the enemies of civilization. Current society has been poisoned for at least a half-century by the demoralizing propaganda pumped out by Hollywood (and more recently, Netflix). With AI, independent filmmakers (with an edifying message to spread) can now compete. How might our culture shift in a positive direction if every year there were ten movies like The Sound of Freedom coming out? AI will result in some amazing scientific breakthroughs in anti-aging science. AI will assist scientists in cross-referencing studies on Pubmed and crunching data from clinical trials. AI will help citizen scientists, like you and me, sniff out bad science. AI will take a lot of tedium out of researching, developing, and bringing to market innovative new health products. I expect boom times ahead in the lifespan business! The AI Trend Is Like Many Other Trends Cast in a Negative Light Bad for the normie, but something the Lifehacker can yield for their edification. Bad for the masses of passive consumers who sit on their couches entranced by a glowing box. But potentially very good for the proactive man or woman with a vision of grandeur for their life or the world. How I'm Using AI Writing is a lifelong passion of mine, and researching Biohacking, antiaging, and philosophy is a +12-year passion that's led me to enjoy a life of meaning, beauty, and adventure. So, this time-consuming part of my life and work is NOT something I'm going to be outsourcing in a major way to AI. Recently, I was doing some research for a Biohacking coaching client on how to stimulate neuroplasticity in the vision centers of the brain. I was just curious what ChatGPT could find; it produced a thorough listicle of neuroplasticity hacks, but when I clicked on the studies it provided as evidence, I discovered that all the links were to the wrong studies. So, it's got a long way to go! I'll be using it merely for brainstorming and to diversify my perspective on whatever I'm researching. Podcasting is another thing I'm not going to outsource to AI. I enjoy talking to the internet, narrating my articles, answering your Q&A questions, or sharing my musings. I won't be running my voice through one of those replicator tools that turns it into narration voice. The image generators I use almost daily now to create poignant imagery for articles or video thumbnail slides. I use Google's ImageFX, Imagine AI, and Gab.AI. Video editing takes up an UNGODLY amount of time; any AI tools that allow me to streamline this will free up a lot of time for me to work creatively instead. Limitless Mindset music is now a thing. I have the musical aptitude of a rock, so I've begun creating catchy songs that summarize my various articles and podcasts with Suno.com and appending them to almost every new upload (a good reason to listen till the end!) Check out the growing library of Limitless Mindset AI songs here; they sound about as good as anything you'll hear on the radio in a variety of genres! My favorite is The Uridine Monophosphate Groove, a country western banger about the Nootropic. AI music may seem terribly gimmicky, but give a listen to my Suno library; one of them might just stick your head reminding of you a crucial Biohacking factoid! https://youtu.be/s4v7apJt-aE?embedable=true What do you think of the whole AI trend taking the internet by storm right now? If you're like most people I've talked to recently, you're somewhat wary of it. But there's a lot of silver lining to the AI cloud steadily advancing over the internet that I want to point out, and I thought I should let you know how I'm using it going forward in my work as an Applied Neuroscience Strategist. Let's Start With the Negatives... Let's Start With the Negatives... AI is going to exacerbate the enshittification of the content realm of the internet . AI is rocket fuel for the content farms of the internet that pump out poorly researched articles and videos about everything under the sun. On your YouTube recommended page and in your search results, expect a lot more content of dubious veracity. You should now have a heightened sense of skepticism about everything hear online. AI is going to exacerbate the enshittification of the content realm of the internet enshittification content farms YouTube was ground zero for the enshittification; with their censorship policies, their pushing content creators into doing low-effort streaming, and their algorithmic boosting of brain-deadening shorts . Expect YouTube to become just flooded with AI-generated content; be skeptical of faceless channels, look for channels run by actual people who appear on camera and have a broader internet presence. enshittification; shorts faceless AI is going to eat up a lot of jobs in the IT and creative fields . Once upon a time, I was a nightclub promoter, and we paid a pretty penny to graphic designers in Denver to create flyers for our weekly events. Now, AI can do that at a tiny fraction of the cost. Programmers, coders, and web developers now need to worry about AI replacing them. Many predict that this will be a massive driver of technological unemployment, necessitating a universal basic income. AI is going to eat up a lot of jobs in the IT and creative fields I'm not so pessimistic about the future; in the not-so-distant future, I see Bitcoin arising as the global reserve currency; I argue that Bitcoin will make civilization sane again ; the satanically-inversed, money-as-debt, fiat banking system has and is harming the economy so much more than AI ever will. Freed of it, the downstream positives are hard to fathom. Bitcoin will make civilization sane again sane again AI will accelerate the digital enslavement of human sexuality . I expect even more lost generations of men to lose themselves in the mire of the pixel-powered pleasure world. The only solution to this is the personal one, take back your sexual sovereignty from the digital sea of siren simulacrums drawing you toward a life of quiet desperation. A Tantra practice is a good place for men to start that reconquista ! AI will accelerate the digital enslavement of human sexuality reconquista The silver lining on this, as I pointed out in a Twitter thread a while back, is that AI is going to compete aggressively with the sad mass of women who have become digital sex workers on OnlyFans. The majority of them simply won't be able to compete with AI and will hopefully turn to a more decent way to make their living. https://x.com/JRoseland/status/1650830140505481218?embedable=true https://x.com/JRoseland/status/1650830140505481218?embedable=true Will AI begin editing our genes and eventually transform us into slave species? I had the pleasure of debating this with one of the greatest minds on the internet who wrote an amazing book on the topic. If it does, it will be thousands of years in the future, and in the interim, our species will enjoy an enlightened golden age that the greatest science fiction authors could only fathom on DMT. Will AI begin editing our genes and eventually transform us into slave species? That golden age will present ample opportunity for the planetary decentralization of humanity, ensuring its survival. The cosmos will be pregnant with the brilliance of our progeny. https://rumble.com/vc4acq-jonathan-roseland-on-the-revolutionary-phenotype-the-public-space-344.html?embedable=true https://rumble.com/vc4acq-jonathan-roseland-on-the-revolutionary-phenotype-the-public-space-344.html?embedable=true The Positives The Positives AI is going to break the monopoly on good movies and television long-held largely by the enemies of civilization . Current society has been poisoned for at least a half-century by the demoralizing propaganda pumped out by Hollywood (and more recently, Netflix). With AI, independent filmmakers (with an edifying message to spread) can now compete. How might our culture shift in a positive direction if every year there were ten movies like The Sound of Freedom coming out? AI is going to break the monopoly on good movies and television long-held largely by the enemies of civilization The Sound of Freedom AI will result in some amazing scientific breakthroughs in anti-aging science . AI will assist scientists in cross-referencing studies on Pubmed and crunching data from clinical trials. AI will help citizen scientists, like you and me, sniff out bad science. AI will take a lot of tedium out of researching, developing, and bringing to market innovative new health products. I expect boom times ahead in the lifespan business! AI will result in some amazing scientific breakthroughs in anti-aging science lifespan The AI Trend Is Like Many Other Trends Cast in a Negative Light The AI Trend Is Like Many Other Trends Cast in a Negative Light Bad for the normie , but something the Lifehacker can yield for their edification. Bad for the masses of passive consumers who sit on their couches entranced by a glowing box. But potentially very good for the proactive man or woman with a vision of grandeur for their life or the world. normie How I'm Using AI How I'm Using AI Writing is a lifelong passion of mine, and researching Biohacking, antiaging, and philosophy is a +12-year passion that's led me to enjoy a life of meaning, beauty, and adventure. So, this time-consuming part of my life and work is NOT something I'm going to be outsourcing in a major way to AI . So, this time-consuming part of my life and work is NOT something I'm going to be outsourcing in a major way to AI Recently, I was doing some research for a Biohacking coaching client on how to stimulate neuroplasticity in the vision centers of the brain. I was just curious what ChatGPT could find; it produced a thorough listicle of neuroplasticity hacks, but when I clicked on the studies it provided as evidence, I discovered that all the links were to the wrong studies. So, it's got a long way to go! I'll be using it merely for brainstorming and to diversify my perspective on whatever I'm researching. Podcasting is another thing I'm not going to outsource to AI . I enjoy talking to the internet, narrating my articles, answering your Q&A questions, or sharing my musings. I won't be running my voice through one of those replicator tools that turns it into narration voice. Podcasting is another thing I'm not going to outsource to AI The image generators I use almost daily now to create poignant imagery for articles or video thumbnail slides. I use Google's ImageFX, Imagine AI, and Gab.AI. Video editing takes up an UNGODLY amount of time; any AI tools that allow me to streamline this will free up a lot of time for me to work creatively instead. Limitless Mindset music is now a thing . I have the musical aptitude of a rock, so I've begun creating catchy songs that summarize my various articles and podcasts with Suno.com and appending them to almost every new upload (a good reason to listen till the end!) Check out the growing library of Limitless Mindset AI songs here ; they sound about as good as anything you'll hear on the radio in a variety of genres! My favorite is The Uridine Monophosphate Groove , a country western banger about the Nootropic. Limitless Mindset music is now a thing here The Uridine Monophosphate Groove AI music may seem terribly gimmicky, but give a listen to my Suno library; one of them might just stick your head reminding of you a crucial Biohacking factoid! https://youtu.be/s4v7apJt-aE?embedable=true https://youtu.be/s4v7apJt-aE?embedable=true

Why I'm Cautiously Optimistic About AI's Impact on Society

Table of Links Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 5. Acknowledgements and References APPENDIX A. Appendix 5. ACKNOWLEDGEMENTS The authors would like to thank Estelle Janin and Cole Mathis for encouraging and productive conversations, and for feedback on the manuscript. REFERENCES Affholder, A., Guyot, F., Sauterey, B., Ferri`ere, R., & Mazevet, S. 2021, Nature Astronomy, doi: 10.1038/s41550-021-01372-6 Balbi, A., & Grimaldi, C. 2020, Proceedings of the National Academy of Sciences, 117, 21031, doi: 10.1073/pnas.2007560117 Bartlett, S., Li, J., Gu, L., et al. 2022, Nature Astronomy, 6, 387, doi: 10.1038/s41550-021-01559-x Benner, S. A. 2010, Astrobiology, 10, 1021, doi: 10.1089/ast.2010.0524 Bich, L., & Green, S. 2018, Synthese, 195, 3919, doi: 10.1007/s11229-017-1397-9 Bixel, A., & Apai, D. 2021, The Astronomical Journal, 161, 228, doi: 10.3847/1538-3881/abe042 Carroll-Nellenback, J., Frank, A., Wright, J., & Scharf, C. 2019, The Astronomical Journal, 158, 117, doi: 10.3847/1538-3881/ab31a3 Catling, D. C., Krissansen-Totton, J., Kiang, N. Y., et al. 2018, Astrobiology, 18, 709, doi: 10.1089/ast.2017.1737 Checlair, J. H., Villanueva, G. L., Hayworth, B. P. C., et al. 2021, The Astronomical Journal, 161, 150, doi: 10.3847/1538-3881/abdb36 Cleaves, H. J., Hystad, G., Prabhu, A., et al. 2023, Proceedings of the National Academy of Sciences, 120, e2307149120, doi: 10.1073/pnas.2307149120 Cleland, C. E. 2012, Synthese, 185, 125, doi: 10.1007/s11229-011-9879-7 Cleland, C. E., & Chyba, C. F. 2002, Origins of Life and Evolution of the Biosphere, 32, 387, doi: 10.1023/A:1020503324273 Cockell, C. S. 2022, in New Frontiers in Astrobiology (Elsevier), 1–17, doi: 10.1016/B978-0-12-824162-2.00009-9 Gobat, R. 2021, The Astrophysical Journal, 16 Green, J., Hoehler, T., Neveu, M., et al. 2021, Nature, 598, 575, doi: 10.1038/s41586-021-03804-9 Grimaldi, C., Lingam, M., & Balbi, A. 2021, The Astronomical Journal, 162, 23, doi: 10.3847/1538-3881/abfe61 Hahsler, M., Piekenbrock, M., & Doran, D. 2019, Journal of Statistical Software, 91, 1, doi: 10.18637/jss.v091.i01 Harman, C. E., & Domagal-Goldman, S. 2018, in Handbook of Exoplanets, ed. H. J. Deeg & J. A. Belmonte (Cham: Springer International Publishing), 1–22, doi: 10.1007/978-3-319-30648-3 71-1 Janin, E. 2021, Astronomy & Geophysics, 62 Kim, H., Smith, H. B., Mathis, C., Raymond, J., & Walker, S. I. 2019, Science Advances, 5, eaau0149, doi: 10.1126/sciadv.aau0149 Kinney, D., & Kempes, C. 2022, Biology & Philosophy, 37, 22, doi: 10.1007/s10539-022-09859-w Kovacevic, A. B. 2022, arXiv:2202.07347 [astro-ph, q-bio]. http://arxiv.org/abs/2202.07347 Lenardic, A., Seales, J., & Covington, A. 2022, International Journal of Astrobiology, 1, doi: 10.1017/S1473550422000222 Lenardic, A., Seales, J., Moore, W. B., & Jellinek, A. M. 2023, Nature Astronomy, 1, doi: 10.1038/s41550-023-02031-8 Lin, H. W., & Loeb, A. 2015, The Astrophysical Journal, 810, L3, doi: 10.1088/2041-8205/810/1/L3 Lingam, M., Grimaldi, C., & Balbi, A. 2021, Monthly Notices of the Royal Astronomical Society, 509, 4365, doi: 10.1093/mnras/stab3108 Lynas, M., Houlton, B. Z., & Perry, S. 2021, Environmental Research Letters, 16, 114005, doi: 10.1088/1748-9326/ac2966 Mariscal, C., & Doolittle, W. F. 2020, Synthese, 197, 2975, doi: 10.1007/s11229-018-1852-2 Marshall, S. M., Mathis, C., Carrick, E., et al. 2021, Nature Communications, 12, 3033, doi: 10.1038/s41467-021-23258-x Mix, L. J. 2015, Astrobiology, 15, 15, doi: 10.1089/ast.2014.1191 Moore, W. B., Lenardic, A., Jellinek, A. M., et al. 2017, Nature Astronomy, 1, 1, doi: 10.1038/s41550-017-0043 Olejarz, J., Iwasa, Y., Knoll, A. H., & Nowak, M. A. 2021, Nature Communications, 12, 3985, doi: 10.1038/s41467-021-23286-7 Pacetti, E., Turrini, D., Schisano, E., et al. 2022, The Astrophysical Journal, 937, 36, doi: 10.3847/1538-4357/ac8b11 Rideout, J. R., Caporaso, G., Bolyen, E., et al. 2023, biocore/scikit-bio: scikit-bio 0.5.9: Maintenance release, Zenodo, doi: 10.5281/zenodo.8209901 Satopaa, V., Albrecht, J., Irwin, D., & Raghavan, B. 2011, in 2011 31st International Conference on Distributed Computing Systems Workshops (Minneapolis, MN, USA: IEEE), 166–171, doi: 10.1109/ICDCSW.2011.20 Schwieterman, E. W., Meadows, V. S., Domagal-Goldman, S. D., et al. 2016, The Astrophysical Journal, 819, L13, doi: 10.3847/2041-8205/819/1/L13 Schwieterman, E. W., Kiang, N. Y., Parenteau, M. N., et al. 2018, Astrobiology, 18, 663, doi: 10.1089/ast.2017.1729 Smith, H. B., & Mathis, C. 2023, BioEssays, 2300050, doi: 10.1002/bies.202300050 Smith, H. H., Hyde, A. S., Simkus, D. N., et al. 2021, Life, 11, 498, doi: 10.3390/life11060498 Sol´e, R. V., & Munteanu, A. 2004, Europhysics Letters (EPL), 68, 170, doi: 10.1209/epl/i2004-10241-3 Tasker, E., Tan, J., Heng, K., et al. 2017, Nature Astronomy, 1, 0042, doi: 10.1038/s41550-017-0042 Tasker, E., Unterborn, C., Laneuville, M., et al., eds. 2020, Planetary diversity: rocky planet processes and their observational signatures, AAS-IOP astronomy (Bristol, UK: IOP Publishing) The scikit-bio development team. 2020, scikit-bio: A bioinformatics library for data scientists, students, and developers. http://scikit-bio.org Totani, T. 2023, International Journal of Astrobiology, 22, 347, doi: 10.1017/S147355042300006X Vickers, P., Cowie, C., Dick, S. J., et al. 2023, Astrobiology, ast.2022.0084, doi: 10.1089/ast.2022.0084 Walker, S. I., Bains, W., Cronin, L., et al. 2018, Astrobiology, 18, 779, doi: 10.1089/ast.2017.1738 Wong, M. L., Prabhu, A., Williams, J., Morrison, S. M., & Hazen, R. M. 2023, Journal of Geophysical Research: Planets, 128, e2022JE007658, doi: 10.1029/2022JE007658 Wright, J. T., Mullan, B., Sigurdsson, S., & Povich, M. S. 2014, The Astrophysical Journal, 792, 26, doi: 10.1088/0004-637X/792/1/26 Authors:
(1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp);
(2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Methods 2.1. Modeling Panspermia and Terraformation 2.1. Modeling Panspermia and Terraformation 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 2.2. Identifying the Presence of Terraformed Planets and 2.3. Software and Availability 3. Results 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.1. Panspermia can increase the correlation between planets’ compositions and positions 3.2. Likely terraformed planets can be identified from clustering 3.2. Likely terraformed planets can be identified from clustering 4. Summary and Discussion 4. Summary and Discussion 5. Acknowledgements and References 5. Acknowledgements and References APPENDIX APPENDIX A. Appendix A. Appendix 5. ACKNOWLEDGEMENTS The authors would like to thank Estelle Janin and Cole Mathis for encouraging and productive conversations, and for feedback on the manuscript. REFERENCES Affholder, A., Guyot, F., Sauterey, B., Ferri`ere, R., & Mazevet, S. 2021, Nature Astronomy, doi: 10.1038/s41550-021-01372-6 Balbi, A., & Grimaldi, C. 2020, Proceedings of the National Academy of Sciences, 117, 21031, doi: 10.1073/pnas.2007560117 Bartlett, S., Li, J., Gu, L., et al. 2022, Nature Astronomy, 6, 387, doi: 10.1038/s41550-021-01559-x Benner, S. A. 2010, Astrobiology, 10, 1021, doi: 10.1089/ast.2010.0524 Bich, L., & Green, S. 2018, Synthese, 195, 3919, doi: 10.1007/s11229-017-1397-9 Bixel, A., & Apai, D. 2021, The Astronomical Journal, 161, 228, doi: 10.3847/1538-3881/abe042 Carroll-Nellenback, J., Frank, A., Wright, J., & Scharf, C. 2019, The Astronomical Journal, 158, 117, doi: 10.3847/1538-3881/ab31a3 Catling, D. C., Krissansen-Totton, J., Kiang, N. Y., et al. 2018, Astrobiology, 18, 709, doi: 10.1089/ast.2017.1737 Checlair, J. H., Villanueva, G. L., Hayworth, B. P. C., et al. 2021, The Astronomical Journal, 161, 150, doi: 10.3847/1538-3881/abdb36 Cleaves, H. J., Hystad, G., Prabhu, A., et al. 2023, Proceedings of the National Academy of Sciences, 120, e2307149120, doi: 10.1073/pnas.2307149120 Cleland, C. E. 2012, Synthese, 185, 125, doi: 10.1007/s11229-011-9879-7 Cleland, C. E., & Chyba, C. F. 2002, Origins of Life and Evolution of the Biosphere, 32, 387, doi: 10.1023/A:1020503324273 Cockell, C. S. 2022, in New Frontiers in Astrobiology (Elsevier), 1–17, doi: 10.1016/B978-0-12-824162-2.00009-9 Gobat, R. 2021, The Astrophysical Journal, 16 Green, J., Hoehler, T., Neveu, M., et al. 2021, Nature, 598, 575, doi: 10.1038/s41586-021-03804-9 Grimaldi, C., Lingam, M., & Balbi, A. 2021, The Astronomical Journal, 162, 23, doi: 10.3847/1538-3881/abfe61 Hahsler, M., Piekenbrock, M., & Doran, D. 2019, Journal of Statistical Software, 91, 1, doi: 10.18637/jss.v091.i01 Harman, C. E., & Domagal-Goldman, S. 2018, in Handbook of Exoplanets, ed. H. J. Deeg & J. A. Belmonte (Cham: Springer International Publishing), 1–22, doi: 10.1007/978-3-319-30648-3 71-1 Janin, E. 2021, Astronomy & Geophysics, 62 Kim, H., Smith, H. B., Mathis, C., Raymond, J., & Walker, S. I. 2019, Science Advances, 5, eaau0149, doi: 10.1126/sciadv.aau0149 Kinney, D., & Kempes, C. 2022, Biology & Philosophy, 37, 22, doi: 10.1007/s10539-022-09859-w Kovacevic, A. B. 2022, arXiv:2202.07347 [astro-ph, q-bio]. http://arxiv.org/abs/2202.07347 Lenardic, A., Seales, J., & Covington, A. 2022, International Journal of Astrobiology, 1, doi: 10.1017/S1473550422000222 Lenardic, A., Seales, J., Moore, W. B., & Jellinek, A. M. 2023, Nature Astronomy, 1, doi: 10.1038/s41550-023-02031-8 Lin, H. W., & Loeb, A. 2015, The Astrophysical Journal, 810, L3, doi: 10.1088/2041-8205/810/1/L3 Lingam, M., Grimaldi, C., & Balbi, A. 2021, Monthly Notices of the Royal Astronomical Society, 509, 4365, doi: 10.1093/mnras/stab3108 Lynas, M., Houlton, B. Z., & Perry, S. 2021, Environmental Research Letters, 16, 114005, doi: 10.1088/1748-9326/ac2966 Mariscal, C., & Doolittle, W. F. 2020, Synthese, 197, 2975, doi: 10.1007/s11229-018-1852-2 Marshall, S. M., Mathis, C., Carrick, E., et al. 2021, Nature Communications, 12, 3033, doi: 10.1038/s41467-021-23258-x Mix, L. J. 2015, Astrobiology, 15, 15, doi: 10.1089/ast.2014.1191 Moore, W. B., Lenardic, A., Jellinek, A. M., et al. 2017, Nature Astronomy, 1, 1, doi: 10.1038/s41550-017-0043 Olejarz, J., Iwasa, Y., Knoll, A. H., & Nowak, M. A. 2021, Nature Communications, 12, 3985, doi: 10.1038/s41467-021-23286-7 Pacetti, E., Turrini, D., Schisano, E., et al. 2022, The Astrophysical Journal, 937, 36, doi: 10.3847/1538-4357/ac8b11 Rideout, J. R., Caporaso, G., Bolyen, E., et al. 2023, biocore/scikit-bio: scikit-bio 0.5.9: Maintenance release, Zenodo, doi: 10.5281/zenodo.8209901 Satopaa, V., Albrecht, J., Irwin, D., & Raghavan, B. 2011, in 2011 31st International Conference on Distributed Computing Systems Workshops (Minneapolis, MN, USA: IEEE), 166–171, doi: 10.1109/ICDCSW.2011.20 Schwieterman, E. W., Meadows, V. S., Domagal-Goldman, S. D., et al. 2016, The Astrophysical Journal, 819, L13, doi: 10.3847/2041-8205/819/1/L13 Schwieterman, E. W., Kiang, N. Y., Parenteau, M. N., et al. 2018, Astrobiology, 18, 663, doi: 10.1089/ast.2017.1729 Smith, H. B., & Mathis, C. 2023, BioEssays, 2300050, doi: 10.1002/bies.202300050 Smith, H. H., Hyde, A. S., Simkus, D. N., et al. 2021, Life, 11, 498, doi: 10.3390/life11060498 Sol´e, R. V., & Munteanu, A. 2004, Europhysics Letters (EPL), 68, 170, doi: 10.1209/epl/i2004-10241-3 Tasker, E., Tan, J., Heng, K., et al. 2017, Nature Astronomy, 1, 0042, doi: 10.1038/s41550-017-0042 Tasker, E., Unterborn, C., Laneuville, M., et al., eds. 2020, Planetary diversity: rocky planet processes and their observational signatures, AAS-IOP astronomy (Bristol, UK: IOP Publishing) The scikit-bio development team. 2020, scikit-bio: A bioinformatics library for data scientists, students, and developers. http://scikit-bio.org Totani, T. 2023, International Journal of Astrobiology, 22, 347, doi: 10.1017/S147355042300006X Vickers, P., Cowie, C., Dick, S. J., et al. 2023, Astrobiology, ast.2022.0084, doi: 10.1089/ast.2022.0084 Walker, S. I., Bains, W., Cronin, L., et al. 2018, Astrobiology, 18, 779, doi: 10.1089/ast.2017.1738 Wong, M. L., Prabhu, A., Williams, J., Morrison, S. M., & Hazen, R. M. 2023, Journal of Geophysical Research: Planets, 128, e2022JE007658, doi: 10.1029/2022JE007658 Wright, J. T., Mullan, B., Sigurdsson, S., & Povich, M. S. 2014, The Astrophysical Journal, 792, 26, doi: 10.1088/0004-637X/792/1/26 Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA (hbs@elsi.jp); (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan (lana.sinapayen@gmail.com). Authors: Authors: (1) Harrison B. Smith, Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan, and Blue Marble Space Institute of Science, Seattle, Washington, USA ( hbs@elsi.jp ); hbs@elsi.jp (2) Lana Sinapayen, Sony Computer Science Laboratories, Kyoto, Japan and National Institute for Basic Biology, Okazaki, Japan ( lana.sinapayen@gmail.com ). lana.sinapayen@gmail.com This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed license. available on arxiv

A Biosignature Based on Modeling Panspermia and Terraformation: References

The Systemic Impact of Deplatforming on Social Media: Acknowledgements, Data, and References

The Contribution of US Broadband Infrastructure to GDP: Acknowledgements & References

Integrating Physics-Informed Neural Networks for Earthquake Modeling: Summary & References

Thermodynamic Limits Around M-dwarf Stars: Acknowledgements

Authors:
(1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu);
(2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Table of Links Abstract and I. Introduction II. Overview of PING System and Gridlock III. Design Insights and Educational Impact IV. Conclusion and References IV. CONCLUSION This paper presents findings from the ongoing development and testing of a personalized intelligent tutoring system integrated into an educational serious game. With our student observation results and design insights, we hope to provide some ideas and direction for other researchers in this area who are interested in creating this type of system. We also show some preliminary results on the educational efficacy of the proposed system. Looking ahead, additional work will be focused on further verifying the efficacy of the system, as well as gathering additional feedback from students and educators to further refine the final product. REFERENCES [1]. D. A. Schwinn, C. S. Cooper, and J. E. Robillard, “putting students at the center: Moving beyond time-variable one-size-fits-all medical education to true individualization,” Advances in Medical Education and Practice, vol. Volume 10, pp. 109–112, 2019. doi: 10.2147/amep.s187946 [2]. S. Yang, H. Tian, L. Sun, and X. Yu, “From one-size-fits-all teaching to adaptive learning: The crisis and solution of education in the era of ai,” Journal of Physics: Conference Series, vol. 1237, no. 4, p. 042039, 2019. doi: 10.1088/1742-6596/1237/4/042039 [3]. S. Olson, Grand Challenges for Engineering Imperatives, Prospects, and Priorities: Summary of A Forum. Washington, D.C: National Academies Press, 2016. [4]. T.-C. Lin, “An investigation of the relationship between in-class and outof-class efforts on student learning: Empirical evidence and strategy suggestion,” Journal of the Scholarship of Teaching and Learning, vol. 16, no. 4, pp. 14–32, 2016. doi: 10.14434/josotl.v16i4.20028 [5]. E. Mousavinasab et al., “Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods,” Interactive Learning Environments, vol. 29, no. 1, pp. 142–163, 2018. doi: 10.1080/10494820.2018.1558257 [6]. F. Martin and D. U. Bolliger, “Engagement matters: Student perceptions on the importance of engagement  strategies in the Online Learning Environment,” Online Learning, vol. 22, no. 1, 2018. doi: 10.24059/olj.v22i1.1092 [7]. G. Hookham and K. Nesbitt, “A systematic review of the definition and measurement of engagement in serious games,” Proceedings of the Australasian Computer Science Week Multiconference, 2019. doi: 10.1145/3290688.3290747 [8]. M.-T. Wang and T. L. Hofkens, “Beyond classroom academics: A schoolwide and multi-contextual perspective on student engagement in school,” Adolescent Research Review, vol. 5, no. 4, pp. 419–433, 2019. doi: 10.1007/s40894-019-00115-z [9]. M. Beyyoudh, M. K. Idrissi, and S. Bennani, “A new approach of integrating serious games in intelligent tutoring systems,” Learning and Analytics in Intelligent Systems, pp. 85–91, 2019. doi: 10.1007/978-3- 030-36778-7_10 [10]. F. Laamarti, M. Eid, and A. El Saddik, “An overview of serious games,” International Journal of Computer Games Technology, vol. 2014, pp. 1– 15, 2014. doi: 10.1155/2014/358152 [11]. J. Liang, Y. Tang, R. Hare, B. Wu and F. -Y. Wang, "A LearningEmbedded Attributed Petri Net to Optimize Student Learning in a Serious Game," in IEEE Transactions on Computational Social Systems, 2022, doi: 10.1109/TCSS.2021.3132355. [12]. R. Hare and Y. Tang, "Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2047-2055, April 2023, doi: 10.1109/TSMC.2022.3227919. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Authors: Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); Dept. of Electrical and Computer Engineering, Rowan University, (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Dept. of Electrical and Computer Engineering, Rowan University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of PING System and Gridlock II. Overview of PING System and Gridlock III. Design Insights and Educational Impact III. Design Insights and Educational Impact IV. Conclusion and References IV. Conclusion and References IV. CONCLUSION This paper presents findings from the ongoing development and testing of a personalized intelligent tutoring system integrated into an educational serious game. With our student observation results and design insights, we hope to provide some ideas and direction for other researchers in this area who are interested in creating this type of system. We also show some preliminary results on the educational efficacy of the proposed system. Looking ahead, additional work will be focused on further verifying the efficacy of the system, as well as gathering additional feedback from students and educators to further refine the final product. REFERENCES [1]. D. A. Schwinn, C. S. Cooper, and J. E. Robillard, “putting students at the center: Moving beyond time-variable one-size-fits-all medical education to true individualization,” Advances in Medical Education and Practice, vol. Volume 10, pp. 109–112, 2019. doi: 10.2147/amep.s187946 [2]. S. Yang, H. Tian, L. Sun, and X. Yu, “From one-size-fits-all teaching to adaptive learning: The crisis and solution of education in the era of ai,” Journal of Physics: Conference Series, vol. 1237, no. 4, p. 042039, 2019. doi: 10.1088/1742-6596/1237/4/042039 [3]. S. Olson, Grand Challenges for Engineering Imperatives, Prospects, and Priorities: Summary of A Forum. Washington, D.C: National Academies Press, 2016. [4]. T.-C. Lin, “An investigation of the relationship between in-class and outof-class efforts on student learning: Empirical evidence and strategy suggestion,” Journal of the Scholarship of Teaching and Learning, vol. 16, no. 4, pp. 14–32, 2016. doi: 10.14434/josotl.v16i4.20028 [5]. E. Mousavinasab et al., “Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods,” Interactive Learning Environments, vol. 29, no. 1, pp. 142–163, 2018. doi: 10.1080/10494820.2018.1558257 [6]. F. Martin and D. U. Bolliger, “Engagement matters: Student perceptions on the importance of engagement  strategies in the Online Learning Environment,” Online Learning, vol. 22, no. 1, 2018. doi: 10.24059/olj.v22i1.1092 [7]. G. Hookham and K. Nesbitt, “A systematic review of the definition and measurement of engagement in serious games,” Proceedings of the Australasian Computer Science Week Multiconference, 2019. doi: 10.1145/3290688.3290747 [8]. M.-T. Wang and T. L. Hofkens, “Beyond classroom academics: A schoolwide and multi-contextual perspective on student engagement in school,” Adolescent Research Review, vol. 5, no. 4, pp. 419–433, 2019. doi: 10.1007/s40894-019-00115-z [9]. M. Beyyoudh, M. K. Idrissi, and S. Bennani, “A new approach of integrating serious games in intelligent tutoring systems,” Learning and Analytics in Intelligent Systems, pp. 85–91, 2019. doi: 10.1007/978-3- 030-36778-7_10 [10]. F. Laamarti, M. Eid, and A. El Saddik, “An overview of serious games,” International Journal of Computer Games Technology, vol. 2014, pp. 1– 15, 2014. doi: 10.1155/2014/358152 [11]. J. Liang, Y. Tang, R. Hare, B. Wu and F. -Y. Wang, "A LearningEmbedded Attributed Petri Net to Optimize Student Learning in a Serious Game," in IEEE Transactions on Computational Social Systems, 2022, doi: 10.1109/TCSS.2021.3132355. [12]. R. Hare and Y. Tang, "Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2047-2055, April 2023, doi: 10.1109/TSMC.2022.3227919. 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

Enhancing Learning with Gamified Instruction: Conclusion and References

Stories, News & Mentions about S4

S4’s Stories on HackerNoon