From Human Intelligence to Artificial Intelligence Chatbots: Modern Day Writing
The purpose of the project was to find out whether humans can tell the difference between human-generated text and artificial intelligence (AI) chatbots-generated text and to identify how Al-generated text differs from human-generated text. The dependent variable was the results given by the participants (whether the paragraphs were Al-generated texts or human-generated text). The Independent variable was the participants in the experiment and the controlled variable was the type of paragraphs (both the Al-generated texts and human-generated texts) and time used to test each participant. The hypothesis for this experiment was that the participants were not going to be able to differentiate between AI-generated text and human-generated text. In this descriptive and mixed-method study, participants were presented with questionnaires. Each participant needed to state whether they thought each paragraph was human-generated or AI-generated. At the end of the questionnaire, the participants were asked to briefly explain what assisted them in differentiating between the two. They were given 60 seconds to decide. A stopwatch was used to time them. A sample of 456 participants took part in this project. They were not told how many AI-generated passages and human-generated passages there were in the selection. They only knew the total number of passages. Over 99% of the participants could not correctly differentiate between AI-generated text and human-generated text in all passages. Only four participants were able to get 100% of the questions correct. All four learners attend schools located in urban areas. After I did my analysis, I discovered that my hypothesis was incorrect. Four of the participants were able to get 100% of the questions correct this indicates that not all the participants were not able to tell the difference between Al-generated text and human-generated text. This rejects my hypothesis. However, the chances of humans differentiating between the two are very low. All four learners attend schools located in urban areas; this indicates that it is easier for learners attending urban area schools to recognise AI-generated texts than learners attending schools located in rural areas.
SeaUVeed Succeed
In the first part of this project, distilled water and 95% ethanol were used to extract ultra-violet(UV)-absorbing and anti-oxidizing compounds from different types of algae including kelp, wakame, sea grape and nori. Activated charcoal was used in attempt to purify the extracts and removed excessive pigments. It was found that the charcoal was more effective in adsorbing pigments from ethanol extract in which up to 80 to 100% pigments could be removed. The UV-absorbing and anti-oxidizing properties of the algae extracts were also studied. All algae extracts showed significant UV-absorbing and anti-oxidizing properties. In particular, extract formed by using 3 g kelp powder in distilled water could significantly reduce 50% UV intensity and react 96.5% DPPH solution which acts as a source of free radicals. In the second part of the project, three applications of algae were explored in details. Firstly, it was found that kelp, wakame and nori extracts by using over 2 g of algae in 30 mL olive oil could absorb UVA and UVB by over 90%, which is comparable to the performance of zinc oxide, a common ingredient in commercial sunscreen products. Costs of preparing the sunscreens were also compared. Except for wakame extract, all other extracts were cheaper than using zinc oxide. Moreover, the kelp extract was found to maintain its UV- absorbing and anti-oxidizing abilities after at least 30 days of storage under room conditions. Lastly, sodium alginate was successfully extracted from kelp with a product yield up to 30%. The alginate solution was then used to form a calcium-alginate protective coating on plastic slides to reduce UV intensity by up to 50%. This aims to apply on nails or fingers during UV nail gel polish to protect against UVR.
In silico Investigation of Cyclosporine Conjugates as Potential Anti-angiogenic Agents via NFAT Inhibition
Calcineurin (CN) activation is a main cause of cancerous tumor formation, one of the leading causes of death globally. Cyclosporine-A (CsA) is a commercially available oral drug that inhibits CN activation; however, low bioavailability limits its use. Nine patented CsA conjugates are potential alternatives to CsA as they have improved cytotoxicities and bioavailabilities but unknown CN-binding affinity. This study aimed to identify the CNinhibition strength and bioavailability of CsA conjugates in silico drug-likeness evaluation via modified Lipinski’s Rule of Five was done on CsA, voclosporin, and CsA conjugates to test bioavailability. The binding affinities of bioavailable compounds were computed via docking to CN in five trials, and the binding affinities were compared. The Water-soluble, RVal, IIA, Alpha, and MeBmt 2 conjugates showed improved bioavailabilities compared to CsA as they passed the drug-likeness screening. After five trials of computational docking to CN, the IIA and RVal conjugates showed improved binding affinities at -15.8 kcal/mol and -15.2 kcal/mol, respectively, compared to CsA at -14.3 kcal/mol. Notably, IIA also showed an improved binding affinity compared to voclosporin at -15.5 kcal/mol. These results suggest that CsA conjugates may be better oral chemotherapeutic drugs than CsA.