FACULTY OF PHYSICALSCIENCES

Protein folding is an important process that allows a long chain of amino acids (called a polypeptide) to form a specific three-dimensional shape needed for it to work properly. This project studies how proteins fold using the energy landscape model, which explains folding as a gradual movement toward the most stable and low-energy shape. By reviewing studies from 2020 to 2025 and comparing examples of amyloid-β, prion, and αsynuclein proteins, the research shows how changes in the folding process can cause proteins to fold incorrectly. When this happens, they may form clumps, leading to diseases such as Alzheimer’s and Parkinson’s. The findings show that protein folding is not random it is guided by chemical interactions, helper molecules called chaperones, and how easily different shapes can form. Misfolding occurs when proteins get stuck in the wrong shape, creating harmful structures. Overall, this study helps explain why protein folding is so important to human health. It also shows how understanding the energy landscape model can help scientists find better ways to prevent or treat diseases caused by misfolded proteins.

Expanding noncontact techniques for monitoring groundwater level variations to include laser based devices has been limited in the past by the low reflectivity of water to laser light The advances made in the realization of highly sensitive receivers has greatly enabled Laser devices with the prospect for effective noncontact monitoring of groundwater levels. In this work a real-time groundwater level monitoring devices was implemented using a LiDAR-lite v3HP laser-based sensor, an Arduino Uno rev 3 microcontroller, and an ESP8266 Wi-Fi module. Hardware implementation involved interfacing the microcontroller with the LiDAR-lite, the Wi-Fi module, a 16 x 2 liquid crystal display and other necessary basic electronic components. The software implementation involved writing, editing, compiling and uploading codes through the Arduino ide unto the microcontroller. The implemented prototype was powered by a 20,000mAH, 5V power bank and deployed on two artesian wells to collect depth to water surface data which were uploaded automatically to ThingSpeak platform. Measurements at various times taken with the device were compared with manual measurements from a calibrated tape. A correction factor was applied to correct measurement residuals. Results of the validation showed that measured values were uploaded to the ThingSpeak platform an average of 33 seconds which could be reconfigured to longer timeframes. The results were remotely accessed on the platform. A correlation graph of measurement before and after applying the correction revealed a near perfect correlation coefficient of 0.99995 for the LiDAR readings for both pre-correction and post correction measurements; confirming a strong linear relationship with tape measurements. The coefficient of variation, root mean square error and limits of agreement from the Bland Altman’s plots all ascertained the improvement in post-correction measurements.