Computationally artificially derived chemical synthesis and multistep preparation processes can have a variety of use-cases. The idea of utilizing advanced computational complexity, bleu-score computational ranking type systems, and isolating different variable data is integrated in the synthesis preparation processes. Some use-cases presented include: edible polymers and the field study of printable foods, synthetic carbon capturing polymers and biomasses, and CBD Isolate for phytocannabinoids. Synthetic chemistry can tackle a variety of issues from food shortage to the opioid crisis. A computational scoring system can help with elimination reactions and non or organic reactionary mechanisms. Also, accuracy through validation-scoring and forms of molecular mechanics is crucial in the experimentation process. The same applies to various forms of variances, scattering and composition accuracy. The easiest way to demonstrate some of these concepts is through the analysis of said systems for these usecase varieties.

Due to the Doppler effect in waves, one can't efficiently transmit signals underwater. This paper takes you to novel approaches that utilize sonar conversion techniques as well as different UART communication methods and software defined networking mechanisms, in order to build underwater wireless networks. The case for UWNs being utilized for oceanic colonization is also presented, as well as how this applies to the creation of "Aquatic IOT type technologies" and new forms of telemetry. Presented in this paper are concepts that were deployed by the Stark Drones Corporation in competing for various challenges such as "The Internet of H2O Challenge" and GigabitDCx. Also presented, is a proposal to apply these technologies for monitoring lake contamination and various forms of e-coli buildup as well as phosphorus run-ons. These networks allow for a cleaner, more sustainable and observable ocean. 

Abstract Distributed computing and parallel processing are often used for offloading large amounts of data in instances such as BOINC. Projects, such as the Decentralized-Internet SDK also allow for people to build instances of cluster computing projects for the offloading of data or decentralized architecture. Generative Adversarial Networks are currently used by AI experts in order to generate data that would have otherwise been non-existent. Given that certain biomedical datasets only have a small amount of donors or case studies available, means that more data would allow for a higher degree of accuracy. Since, certain diseases may not have enough donors or resources to collect that data, one method may be mathematically creating viable artificial data. This however, requires large amounts of processing. The utilization of a regressional model that would allow for a generative adversarial network (GAN) to recursively build medical data sets based off of pre-existing data in order to increase the statistical pool of accuracy should be feasible with distributed computing. This approach should also be worth trying in the case of absolute unknowns and false positives.1.0 Problem StatementFor findings in statistics, one wants to have a high degree of significance.[1] The more data you have, the higher the degree of accuracy. For example, the cancer known as Diffuse Intrinsic Pontine Glioma (DIPG)[2] with a high degree of rarity relies many statistical unknowns. Given this, and the rarity of survival, likely not much data is readily available to get a full sense of knowledge on DIPG. Other abnormalities could include medical diseases that have genetic variants, cardiac diseases, and such that could utilize a better sense of higher degree of accuracy in data. Grid Computing architecture such as the introduction of BOINC[3] i.e “Berkley’s Open Infrastructure for Network Computing” allow for offloading of large amounts of data through parallel processing. Other projects such as the Decentralized Internet SDK[4] allow for people to build distributed computing clusters and instances in support of decentralization. A proposal is to create a distributed processing program for the allowance of a regressional GAN that would increase the amount of biomedical data being analyzed in order to receive a higher degree of accuracy for the researcher to viably conclude results upon.